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ENH: Add divmod to series.
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| 9 | 2016-09-12T18:58:01Z | 2016-09-19T21:09:27Z | 2016-09-19T21:07:02Z |
CONTRIBUTOR
| null |
- [x] closes #8174
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
edit: in the issue it said this is uncommon; however, I recently ran into an issue where I was doing arithmetic with fiscal quarters and using divmod seemed more natural.
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WIP/API: add magic 'X' for selection
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[] | null | 6 | 2016-09-13T01:51:32Z | 2016-10-30T14:59:50Z | 2016-10-30T14:59:50Z |
CONTRIBUTOR
| null |
- [ ] could close #13133
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
This is very WIP, but wanted to put it up and show the general direction. This adds essentially a modified version of `pandas_ply` that produces plain callables that can be passed to the existing `[]`/`assign` methods. Short demo below.
One thing that's tricky is figuring out when an expression is "complete." `pandas_ply` and `dplython` don't have to do this because they use a special method to instantiate the selection, but I'd prefer not to do this if possible, so this doesn't touch any pandas internals. There's one example below (`X.c.str.upper()`) that shows where the current heuristic is failing.
cc @shoyer, @jreback, @joshuahhh @dodger487, welcome any thoughts
```
df = pd.DataFrame({'a':[1,2,3], 'b':[1.5, 2.5, 3.4],
'c':['abc', 'def', 'efg'],
'd':pd.to_datetime(['2014-01-01', '2014-01-02', '2014-01-03'])})
from pandas import X
df[X.a > 1]
Out[3]:
a b c d
1 2 2.5 def 2014-01-02
2 3 3.4 efg 2014-01-03
df[X.d.dt.day == 2]
Out[4]:
a b c d
1 2 2.5 def 2014-01-02
df.assign(e=X.a+1)
Out[5]:
a b c d e
0 1 1.5 abc 2014-01-01 2
1 2 2.5 def 2014-01-02 3
2 3 3.4 efg 2014-01-03 4
df.assign(e=X.b.pipe(np.exp))
Out[6]:
a b c d e
0 1 1.5 abc 2014-01-01 4.481689
1 2 2.5 def 2014-01-02 12.182494
2 3 3.4 efg 2014-01-03 29.964100
# this should work, but doesn't
df.assign(e=X.c.str.upper())
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-7-65b030f82188> in <module>()
----> 1 df.assign(e=X.c.str.upper())
# this can't work, but need to give a good error msg
df.assign(e=np.log(X.a))
---------------------------------------------------------------------------
TypeError:
pandas `X` is a deferred object that cannot be passed into
functions. The object was attempted to be converted to a numpy array which is invalid.
To pass a deferred Series into a function, use the .pipe
function, for example, X.a.pipe(np.log), instead np.log(df.a)
```
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Option to Raise Exception on Unmapped Value in Series.map
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[] | null | 4 | 2016-09-13T02:22:20Z | 2021-05-01T23:41:07Z | null |
CONTRIBUTOR
| null |
Add a flag (or extend `na_action`) to raise an exception in `Series.map` if a value is not successfully mapped (ie in the case where `arg` is a dict.
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DOC: fix incorrect example in unstack docstring (GH14206)
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| 5 | 2016-09-13T08:13:44Z | 2016-09-14T04:39:58Z | 2016-09-13T23:00:15Z |
NONE
| null |
- [x] closes #14206
The following PR closes #14206 and corrects the Docs as suggested in the issue thread.
@jorisvandenbossche Kindly review.
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| 1 | 2016-09-13T09:33:26Z | 2016-09-13T10:00:56Z | 2016-09-13T09:58:53Z |
NONE
| null |
I am using postgresql database
#### Code Sample, a copy-pastable example if possible
a=pd.DataFrame({'a':[decimal.Decimal('0.1'), decimal.Decimal('0.1'), decimal.Decimal('0.1'), decimal.Decimal('0.3')]})
#### Expected Output
conn is sql alchemy connection
a.to_sql('testdec',conn)
what is produced:
CREATE TABLE testdec
(
index bigint,
a text
)
I would have expected the use of decimal type?
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.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: de_DE
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.1
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: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.5.1 (dt dec pq3 ext)
jinja2: 2.8
boto: 2.39.0
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BUG: loffset not applied when using resample with agg() (GH13218)
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| 9 | 2016-09-13T16:32:56Z | 2016-12-31T16:58:13Z | 2016-12-31T16:56:53Z |
CONTRIBUTOR
| null |
- [x] closes #13218 and #15002 (continuation of closed PR https://github.com/pydata/pandas/pull/13861)
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
_apply_loffset is called in resampler's downsample and groupby_and_agg methods, defined [here](https://github.com/pydata/pandas/blob/master/pandas/tseries/resample.py#L535) and [here](https://github.com/pydata/pandas/blob/master/pandas/tseries/resample.py#L545)
When a `str` is passed into `aggregate`, the above methods are called [here](https://github.com/pydata/pandas/blob/master/pandas/core/base.py#L431), and consequently the loffset is applied.
**However**, if anything other than a `str` is passed into `aggregate`, then a different path will be taken and **the loffset must be applied**. So a simple check in `aggregate` will apply the loffset correctly.
note: my last PR was closed, and I can't reopen it by a force push, hence the new one
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add check for infinity in __call__ of EngFormatter
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| 4 | 2016-09-13T19:09:55Z | 2016-09-13T23:18:07Z | 2016-09-13T22:00:08Z |
CONTRIBUTOR
| null |
EngFormatter throws an exception if passed 'inf' as below.
This patch checks for infinity and just returns 'inf' in that case. The fix is analagous to the recent fix for NaN:
https://github.com/pydata/pandas/commit/d0734ba4d0f4c228110dc3974943ce4ec2adeea4
``` python
import pandas
import pandas.core.format as fmt
ef = fmt.EngFormatter()
ef(pandas.np.inf)
...
```
---
OverflowError Traceback (most recent call last)
<ipython-input-17-c4aa659346ee> in <module>()
3
4 ef = fmt.EngFormatter()
----> 5 ef(pandas.np.inf)
/usr/lib/python3/dist-packages/pandas/core/format.py in **call**(self, num)
2485
2486 if dnum != 0:
-> 2487 pow10 = decimal.Decimal(int(math.floor(dnum.log10() / 3) \* 3))
2488 else:
2489 pow10 = decimal.Decimal(0)
OverflowError: cannot convert Infinity to integer
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BUG: Panel.at[] bug with DatetimeIndex
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| 2 | 2016-09-13T19:52:01Z | 2018-07-06T20:21:10Z | 2017-07-10T21:04:40Z |
CONTRIBUTOR
| null |
```
In [1]: import pandas as pd
In [2]: import datetime as dt
In [3]: p = pd.Panel(0., items=list('abc'),
major_axis=pd.date_range(dt.date(2000, 1, 1), periods=3),
minor_axis=list('xyz'), dtype=floa
In [4]: p.ix['c', dt.date(2000, 1, 2), 'z'] += 2.
In [5]: p.loc['c', dt.date(2000, 1, 2), 'z'] += 3.
In [6]: p.at['c', dt.date(2000, 1, 2), 'z'] += 4.
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-6-ec4d00dae6ba> in <module>()
----> 1 p.at['c', dt.date(2000, 1, 2), 'z'] += 4.
c:\python27\lib\site-packages\pandas\core\indexing.pyc in __getitem__(self, key)
1647
1648 key = self._convert_key(key)
-> 1649 return self.obj.get_value(*key, takeable=self._takeable)
1650
1651 def __setitem__(self, key, value):
c:\python27\lib\site-packages\pandas\core\panel.pyc in get_value(self, *args, **kwargs)
502 lower = self._get_item_cache(args[0])
503
--> 504 return lower.get_value(*args[1:], takeable=takeable)
505
506 def set_value(self, *args, **kwargs):
c:\python27\lib\site-packages\pandas\core\frame.pyc in get_value(self, index, col, takeable)
1836 series = self._get_item_cache(col)
1837 engine = self.index._engine
-> 1838 return engine.get_value(series.get_values(), index)
1839
1840 def set_value(self, index, col, value, takeable=False):
pandas\index.pyx in pandas.index.IndexEngine.get_value (pandas\index.c:3234)()
pandas\index.pyx in pandas.index.IndexEngine.get_value (pandas\index.c:2931)()
pandas\index.pyx in pandas.index.DatetimeEngine.get_loc (pandas\index.c:11064)()
pandas\index.pyx in pandas.index.DatetimeEngine._date_check_type (pandas\index.c:11232)()
KeyError: datetime.date(2000, 1, 2)
In [7]: pd.show_versions()
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 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 27.1.2
Cython: None
numpy: 1.11.1
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.1
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.15
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: None
boto: None
pandas_datareader: None
```
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| 176,776,804 |
MDU6SXNzdWUxNzY3NzY4MDQ=
| 14,216 |
Inconsistent type casting between DataFrame and Series
|
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| 7 | 2016-09-13T23:28:06Z | 2017-09-12T15:43:22Z | 2016-09-14T10:23:44Z |
NONE
| null |
There is an inconsistency between how Series and DataFrames export types to dicts. The issue only manifests if the dataframe has multiple columns with different types. I think the issue is probably pretty far under the surface with differences between Series and DataFrame dtypes and just happened to be uncovered by export to dict.
#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
orig_dict = [{0: 'a', 1: 1}]
# does not happen with
# orig_dict =[{0: 0, 1: 1}]
print 'Original dict'
for k in orig_dict[0]:
print type(orig_dict[0][k])
print
df = pd.DataFrame(orig_dict)
dict_exported_from_dataframe = [df.loc[[x]].to_dict(orient='records')[0] for x in df.index]
dict_exported_from_series = [df.loc[x].to_dict() for x in df.index]
print 'Dict exported from DataFrame'
for k in dict_exported_from_dataframe[0]:
print type(dict_exported_from_dataframe[0][k])
print
print 'Dict exported from Series'
for k in dict_exported_from_series[0]:
print type(dict_exported_from_series[0][k])
print
```
#### Expected Output
```
Original dict
<type 'str'>
<type 'int'>
Dict exported from DataFrame
<type 'str'>
<type 'int'>
Dict exported from Series
<type 'str'>
<type 'int'>
```
OR
```
Original dict
<type 'str'>
<type 'int'>
Dict exported from DataFrame
<type 'str'>
<type 'numpy.int64'>
Dict exported from Series
<type 'str'>
<type 'numpy.int64'>
```
#### 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: None
pip: 8.1.2
setuptools: 25.2.0
Cython: None
numpy: 1.11.1
scipy: 0.13.0b1
statsmodels: None
xarray: None
IPython: 5.1.0
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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MDU6SXNzdWUxNzY3Nzk4NTE=
| 14,217 |
BUG: Copying columns containing lists
|
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| 1 | 2016-09-13T23:50:04Z | 2016-09-14T08:04:03Z | 2016-09-14T01:48:19Z |
NONE
| null |
## BRIEF EXPLANATION
Copying a column using df.apply() will fail if that column contains lists and the first item in that column is a list of length exactly two.
## CODE SAMPLE
```
import pandas as pd
def list_to_list(df):
return df['list']
check = pd.DataFrame({'id': [0,1], 'list':[['1','2'], ['1', '2']]})
check['new_list'] = check.apply(list_to_list, axis = 1)
```
Running the code above results in the following error message:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-48-02b4161cb08e> in <module>()
1 check = pd.DataFrame({'id': [0,1], 'list':[['1','2'], ['1', '2']]})
----> 2 check['new_list'] = check.apply(list_to_list, axis = 1)
/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in __setitem__(self, key, value)
2297 else:
2298 # set column
-> 2299 self._set_item(key, value)
2300
2301 def _setitem_slice(self, key, value):
/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in _set_item(self, key, value)
2365 self._ensure_valid_index(value)
2366 value = self._sanitize_column(key, value)
-> 2367 NDFrame._set_item(self, key, value)
2368
2369 # check if we are modifying a copy
/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc in _set_item(self, key, value)
1206
1207 def _set_item(self, key, value):
-> 1208 self._data.set(key, value)
1209 self._clear_item_cache()
1210
/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc in set(self, item, value, check)
3332 except KeyError:
3333 # This item wasn't present, just insert at end
-> 3334 self.insert(len(self.items), item, value)
3335 return
3336
/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc in insert(self, loc, item, value, allow_duplicates)
3434 block = make_block(values=value,
3435 ndim=self.ndim,
-> 3436 placement=slice(loc, loc+1))
3437
3438 for blkno, count in _fast_count_smallints(self._blknos[loc:]):
/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc in make_block(values, placement, klass, ndim, dtype, fastpath)
2452
2453 return klass(values, ndim=ndim, fastpath=fastpath,
-> 2454 placement=placement)
2455
2456
/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc in __init__(self, values, ndim, fastpath, placement, **kwargs)
1626 super(ObjectBlock, self).__init__(values, ndim=ndim,
1627 fastpath=fastpath,
-> 1628 placement=placement, **kwargs)
1629
1630 @property
/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc in __init__(self, values, placement, ndim, fastpath)
85 raise ValueError('Wrong number of items passed %d,'
86 ' placement implies %d' % (
---> 87 len(self.values), len(self.mgr_locs)))
88
89 @property
ValueError: Wrong number of items passed 2, placement implies 1
```
```
check1 = pd.DataFrame({'id': [0,1], 'list':[['1','2'], ['1', '2', '3']]})
check1['new_list'] = check1.apply(list_to_list, axis = 1)
```
Running the code above results in the following error message:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-49-571835e7c0cb> in <module>()
1 check1 = pd.DataFrame({'id': [0,1], 'list':[['1','2'], ['1', '2', '3']]})
----> 2 check1['new_list'] = check1.apply(list_to_list, axis = 1)
/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in apply(self, func, axis, broadcast, raw, reduce, args, **kwds)
3970 if reduce is None:
3971 reduce = True
-> 3972 return self._apply_standard(f, axis, reduce=reduce)
3973 else:
3974 return self._apply_broadcast(f, axis)
/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in _apply_standard(self, func, axis, ignore_failures, reduce)
4079 index = None
4080
-> 4081 result = self._constructor(data=results, index=index)
4082 result.columns = res_index
4083
/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in __init__(self, data, index, columns, dtype, copy)
224 dtype=dtype, copy=copy)
225 elif isinstance(data, dict):
--> 226 mgr = self._init_dict(data, index, columns, dtype=dtype)
227 elif isinstance(data, ma.MaskedArray):
228 import numpy.ma.mrecords as mrecords
/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in _init_dict(self, data, index, columns, dtype)
361
362 return _arrays_to_mgr(arrays, data_names, index, columns,
--> 363 dtype=dtype)
364
365 def _init_ndarray(self, values, index, columns, dtype=None,
/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in _arrays_to_mgr(arrays, arr_names, index, columns, dtype)
5166 axes = [_ensure_index(columns), _ensure_index(index)]
5167
-> 5168 return create_block_manager_from_arrays(arrays, arr_names, axes)
5169
5170
/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc in create_block_manager_from_arrays(arrays, names, axes)
3914 return mgr
3915 except (ValueError) as e:
-> 3916 construction_error(len(arrays), arrays[0].shape, axes, e)
3917
3918
/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc in construction_error(tot_items, block_shape, axes, e)
3878 implied = tuple(map(int, [len(ax) for ax in axes]))
3879 if passed == implied and e is not None:
-> 3880 raise e
3881 raise ValueError("Shape of passed values is {0}, indices imply {1}".format(
3882 passed,implied))
ValueError: could not broadcast input array from shape (3) into shape (2)
```
Increasing the number of items in the second list increases the number associated with the first "shape" in the error message
```
check2 = pd.DataFrame({'id': [0,1], 'list':[['1','2'], []]})
check2['new_list'] = check2.apply(list_to_list, axis = 1)
```
Running the code above results in the following error message:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-50-a4f4e0227671> in <module>()
1 check2 = pd.DataFrame({'id': [0,1], 'list':[['1','2'], []]})
----> 2 check2['new_list'] = check2.apply(list_to_list, axis = 1)
/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in apply(self, func, axis, broadcast, raw, reduce, args, **kwds)
3970 if reduce is None:
3971 reduce = True
-> 3972 return self._apply_standard(f, axis, reduce=reduce)
3973 else:
3974 return self._apply_broadcast(f, axis)
/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in _apply_standard(self, func, axis, ignore_failures, reduce)
4079 index = None
4080
-> 4081 result = self._constructor(data=results, index=index)
4082 result.columns = res_index
4083
/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in __init__(self, data, index, columns, dtype, copy)
224 dtype=dtype, copy=copy)
225 elif isinstance(data, dict):
--> 226 mgr = self._init_dict(data, index, columns, dtype=dtype)
227 elif isinstance(data, ma.MaskedArray):
228 import numpy.ma.mrecords as mrecords
/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in _init_dict(self, data, index, columns, dtype)
361
362 return _arrays_to_mgr(arrays, data_names, index, columns,
--> 363 dtype=dtype)
364
365 def _init_ndarray(self, values, index, columns, dtype=None,
/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in _arrays_to_mgr(arrays, arr_names, index, columns, dtype)
5166 axes = [_ensure_index(columns), _ensure_index(index)]
5167
-> 5168 return create_block_manager_from_arrays(arrays, arr_names, axes)
5169
5170
/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc in create_block_manager_from_arrays(arrays, names, axes)
3914 return mgr
3915 except (ValueError) as e:
-> 3916 construction_error(len(arrays), arrays[0].shape, axes, e)
3917
3918
/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc in construction_error(tot_items, block_shape, axes, e)
3878 implied = tuple(map(int, [len(ax) for ax in axes]))
3879 if passed == implied and e is not None:
-> 3880 raise e
3881 raise ValueError("Shape of passed values is {0}, indices imply {1}".format(
3882 passed,implied))
ValueError: Shape of passed values is (2, 0), indices imply (2, 2)
```
## EXPECTED OUTPUT
In each case, I would expect the 'new_list' column to be created without errors, and with lists of the specified length.
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-573.22.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 8.0.3
setuptools: 20.1.1
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
IPython: 4.0.3
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.5.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: 0.6.7.None
psycopg2: None
Jinja2: None
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MDU6SXNzdWUxNzY4NzU1MjQ=
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DEPR: deprecate .ix
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| 22 | 2016-09-14T10:50:22Z | 2018-07-20T13:47:07Z | 2017-01-18T16:12:00Z |
CONTRIBUTOR
| null |
enough said.
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| 4 | 2016-09-14T10:52:58Z | 2019-09-12T18:45:39Z | 2019-09-12T18:45:39Z |
CONTRIBUTOR
| null |
Various feature changes in the future might be invasive to the long-term support (LTS) concept. IOW, having a deprecation warning for Panel. So allow an option to control this.
- Deprecate `Panel`, #13563
- Deprecate `.ix`: https://github.com/pydata/pandas/issues/14218
something like:
`options.features.panel = 'warn' | 'raise' | 'ignore'`
`options.features.ix = 'warn' | 'raise' | 'ignore'`
These would default to `warn`, but can then be user settable. Normally don't like changes to global state, but if you _are_ using a 'deprecated' feature, easiest to silence things like this.
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[] | null | 10 | 2016-09-14T10:55:06Z | 2018-07-13T14:51:50Z | 2018-07-13T14:51:49Z |
CONTRIBUTOR
| null |
Contemplated deprecations for 0.20.0
- [x] Deprecate `Panel`, #13563 (#15601) (0.20.0)
- [x] Deprecate `.ix`: https://github.com/pydata/pandas/issues/14218 (#15113) (0.20.0)
- [ ] Deprecate `.filter` & `.select` https://github.com/pydata/pandas/issues/12401 (#17633, 0.21.0)
- [ ] Deprecate `.between` https://github.com/pydata/pandas/issues/13027
- [x] Deprecate `.sortlevel` #14279 (#15099) (0.20.0)
- [ ] remove things in #6581 in 0.17.0
- [x] top-level namespace cleanup #13790 (0.20.0)
- [x] `TimeSeries` class (#15098) (0.20.0)
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| 3 | 2016-09-14T11:12:51Z | 2016-09-14T11:51:32Z | 2016-09-14T11:15:35Z |
CONTRIBUTOR
| null |
When it comes to writing unit tests that return a `DataFrame` or a `TimeSeries` there's a clear need to compare the output to the expected result. The comparison can be strict, or relaxed in terms of the order of columns and/or order of rows, and could also be forgiving for floats.
I'm thinking of an approach implemented in https://pythonhosted.org/toytable/toytable.table.html#table-literals, i.e. defining a table as
``` python
>>> pokedex = table_literal("""
... | Pokemon (str) | Level (int) | Owner (str) | Type (str) |
... | Pikchu | 12 | Ash Ketchum | Electric |
... | Bulbasaur | 16 | Ash Ketchum | Grass |
... | Charmander | 19 | Ash Ketchum | Fire |
... | Raichu | 23 | Lt. Surge | Electric |
... """)
```
and subsequently invoking `self.assertTableEqual` or `self.assertTableContentsEqual`.
Does anything similar exist in pandas (I could not find), and can the above approach be upvoted please?
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read_csv(compression='gzip') fails while reading compressed file from s3
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| 2 | 2016-09-14T15:12:09Z | 2016-09-15T17:23:59Z | 2016-09-15T12:15:06Z |
NONE
| null |
#### reading gzipped csv from s3 bucket (private)
```
import pandas as pd
import boto3
session = boto3.Session()
s3client = session.client('s3')
obj = s3client.get_object(Bucket=bucket_name, Key=objkey)
df = pd.read_csv(obj['Body'], compression='gzip', nrows=5, engine='python')
```
#### Error
```
File "/Users/username/anaconda/lib/python2.7/site-packages/pandas/io/parsers.py", line 562, in parser_f
return _read(filepath_or_buffer, kwds)
File "/Users/username/anaconda/lib/python2.7/site-packages/pandas/io/parsers.py", line 315, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/Users/username/anaconda/lib/python2.7/site-packages/pandas/io/parsers.py", line 645, in __init__
self._make_engine(self.engine)
File "/Users/username/anaconda/lib/python2.7/site-packages/pandas/io/parsers.py", line 805, in _make_engine
self._engine = klass(self.f, **self.options)
File "/Users/username/anaconda/lib/python2.7/site-packages/pandas/io/parsers.py", line 1608, in __init__
self.columns, self.num_original_columns = self._infer_columns()
File "/Users/username/anaconda/lib/python2.7/site-packages/pandas/io/parsers.py", line 1823, in _infer_columns
line = self._buffered_line()
File "/Users/username/anaconda/lib/python2.7/site-packages/pandas/io/parsers.py", line 1975, in _buffered_line
return self._next_line()
File "/Users/username/anaconda/lib/python2.7/site-packages/pandas/io/parsers.py", line 2006, in _next_line
orig_line = next(self.data)
File "/Users/username/anaconda/lib/python2.7/gzip.py", line 464, in readline
c = self.read(readsize)
File "/Users/username/anaconda/lib/python2.7/gzip.py", line 268, in read
self._read(readsize)
File "/Users/username/anaconda/lib/python2.7/gzip.py", line 295, in _read
pos = self.fileobj.tell() # Save current position
AttributeError: 'StreamingBody' object has no attribute 'tell'
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 15.4.0
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: 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: 1.4.5
patsy: 0.4.0
dateutil: 2.5.3
pytz: 2016.6.1
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: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
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| 176,940,406 |
MDU6SXNzdWUxNzY5NDA0MDY=
| 14,223 |
ENH: add "by" attribute to all plot methods
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closed
| false | null |
[] | null | 2 | 2016-09-14T15:33:18Z | 2016-09-14T22:07:09Z | 2016-09-14T22:07:09Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
- boxplot has a grouby attribute "by" which makes it easy to plot all months of a year in a facet grid of subplots: http://stackoverflow.com/a/37687386
- It would be greta to have this also for other plot types such as scatter, line, historgram
I checked and other isssues on subplot & grouby are rather related to histogramms.
This here aims for a quick method how to create subplot facet grid for the same variable(s) on different sections or intervals of the dataframe:
- 1 plot for every year of a multi-year timeseries
- 1 plot for each average day of a full year series
In theory, this would a facet grid of subplots where each plot is made from a column of a 2-d pivot table, e.g. a table with teh 12 months on x-axis and values on x-axis.
#### Expected Output
- https://plot.ly/~MattSundquist/11035.png from http://blog.plot.ly/post/117105992082/time-series-graphs-eleven-stunning-ways-you-can & http://nbviewer.jupyter.org/gist/mkcor/0ee7c73d9ba4c68ddc1a/
- http://i.stack.imgur.com/dGhai.png from http://stackoverflow.com/a/37126436
Similar functionality is provided by [calmap](https://github.com/martijnvermaat/calmap)
==> I hope this suggestion is not beyond the scope of pandas.
--> similar functonality but with less convenience is provided by seaborn
http://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.FacetGrid.html
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MDU6SXNzdWUxNzY5NzI0MzM=
| 14,224 |
sum() does not honor skipna argument
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| 2 | 2016-09-14T17:40:01Z | 2016-09-14T21:11:14Z | 2016-09-14T20:58:36Z |
NONE
| null |
sum() is giving 0.0 as the total, rather than NaN, when the entire row/column is NaN.
Note that this bug does not arise in pandas 17.1 with numpy 1.11.0, running on Ubuntu.
#### Code Sample, a copy-pastable example if possible
```
import numpy as np
df = pd.DataFrame([[np.nan,np.nan,np.nan,np.nan],
[1,np.nan,np.nan,np.nan],[2,2,2,np.nan]],columns=['a','b','c','d'])
df.sum(skipna=True) # should be true by default. Same result if argument is omitted
```
#### Actual Output
a 3.0
b 2.0
c 2.0
d 0.0
#### Expected Output
a 3.0
b 2.0
c 2.0
d NaN
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
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: 23.1.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: None
patsy: 0.4.1
dateutil: 2.2
pytz: 2014.7
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.3.4
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.999
httplib2: 0.8
apiclient: 1.2
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.5.4 (dt dec pq3 ext)
jinja2: 2.7.2
boto: 2.40.0
pandas_datareader: None
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BUG: GH13629 Binned groupby median function calculates median on empt…
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| false | null |
[] | null | 3 | 2016-09-15T01:57:41Z | 2016-09-18T15:57:27Z | 2016-09-18T15:57:27Z |
CONTRIBUTOR
| null |
- [ x] closes #13629
- [ x] tests added / passed
- [ x] passes `git diff upstream/master | flake8 --diff`
- [x ] whatsnew entry
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| 177,097,516 |
MDU6SXNzdWUxNzcwOTc1MTY=
| 14,226 |
Conditionally styling by HTML classes
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| 5 | 2016-09-15T06:14:22Z | 2021-02-08T06:56:18Z | 2021-02-08T06:56:18Z |
NONE
| null |
Currently the `DataFrame.Styler` class provides for adding of css key/value attribute pairs. But it is sometimes preferable to add a HTML class to a cell instead, and then let the styling be done by an external css file.
As an example consider the following DataFrame:
```
df = pd.DataFrame([[1,2],
[-5,1]],
columns=['A','B'])
```
As an example, we would like to attach the class "negative" to the -5 and receive the following HTML:
```
<html><table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>A</th>
<th>B</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>1</td>
<td>2</td>
</tr>
<tr>
<th>1</th>
<td class="negative">-5</td>
<td>1</td>
</tr>
</tbody>
</table></html>
```
Regarding how to implement this, here are a couple of ideas:
1. Add a flag `by_class` to `apply()` and `applymap()` that instructs the styler that the "function" will return a classes instead of css strings.
2. Add new functions apply_class() and applymap_class().
3. Create a new class StyleClass() and use `isinstance(ret,StyleClass)` to determine whether to modify the style directly or by adding additional classes.
IMO, I believe that it would be a better interface of styler that apply and applymap were to return classes by default and then optionally provide a css style sheet that can be resolved when turning the style into html. Such an approach would also be easier to adapt to additional backends, e.g. LaTeX. The disadvantage though is that it would break backwards compatibility, and that it would make styling for iPython (the main usecase?) more complicated.
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| 177,205,198 |
MDU6SXNzdWUxNzcyMDUxOTg=
| 14,227 |
Labels mismatch when adding series with repeated index values in 0.19rc1
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| 7 | 2016-09-15T15:15:12Z | 2016-09-30T21:23:51Z | 2016-09-30T21:23:35Z |
CONTRIBUTOR
| null |
This showed up in the dask.dataframe test suite when testing against the 0.19 release candidate. I'm unsure if this was an intended change or not:
### Pandas 0.18
``` python
In [1]: import pandas as pd
In [2]: a = pd.Series([1, 2], index=[1, 1])
In [3]: b = pd.Series([10, 10], index=[1, 2])
In [4]: a
Out[4]:
1 1
1 2
dtype: int64
In [5]: b
Out[5]:
1 10
2 10
dtype: int64
In [6]: a + b
Out[6]:
1 11.0
1 12.0
2 NaN
dtype: float64
```
### Pandas 0.19rc1
``` python
In [1]: import pandas as pd
In [2]: a = pd.Series([1, 2], index=[1, 1])
In [3]: b = pd.Series([10, 10], index=[1, 2])
In [4]: a
Out[4]:
1 1
1 2
dtype: int64
In [5]: b
Out[5]:
1 10
2 10
dtype: int64
In [6]: a + b
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-6-f96fb8f649b6> in <module>()
----> 1 a + b
/home/mrocklin/Software/anaconda/lib/python3.5/site-packages/pandas/core/ops.py in wrapper(left, right, name, na_op)
671 return NotImplemented
672
--> 673 left, right = _align_method_SERIES(left, right)
674
675 converted = _Op.get_op(left, right, name, na_op)
/home/mrocklin/Software/anaconda/lib/python3.5/site-packages/pandas/core/ops.py in _align_method_SERIES(left, right, align_asobject)
613 return_indexers=True)
614 # if DatetimeIndex have different tz, convert to UTC
--> 615 left.index = index
616 right.index = index
617
/home/mrocklin/Software/anaconda/lib/python3.5/site-packages/pandas/core/generic.py in __setattr__(self, name, value)
2754 try:
2755 object.__getattribute__(self, name)
-> 2756 return object.__setattr__(self, name, value)
2757 except AttributeError:
2758 pass
pandas/src/properties.pyx in pandas.lib.AxisProperty.__set__ (pandas/lib.c:44336)()
/home/mrocklin/Software/anaconda/lib/python3.5/site-packages/pandas/core/series.py in _set_axis(self, axis, labels, fastpath)
306 object.__setattr__(self, '_index', labels)
307 if not fastpath:
--> 308 self._data.set_axis(axis, labels)
309
310 def _set_subtyp(self, is_all_dates):
/home/mrocklin/Software/anaconda/lib/python3.5/site-packages/pandas/core/internals.py in set_axis(self, axis, new_labels)
2774 raise ValueError('Length mismatch: Expected axis has %d elements, '
2775 'new values have %d elements' %
-> 2776 (old_len, new_len))
2777
2778 self.axes[axis] = new_labels
ValueError: Length mismatch: Expected axis has 3 elements, new values have 5 elements
```
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MDExOlB1bGxSZXF1ZXN0ODU0Mzk1Njg=
| 14,228 |
DOC: #14195. to_csv warns regarding quoting behaviour for floats
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| 6 | 2016-09-15T16:45:57Z | 2016-10-06T16:04:58Z | 2016-10-06T16:04:57Z |
CONTRIBUTOR
| null |
- [x] closes #14195
- [ ] passes `git diff upstream/master | flake8 --diff`
Added a small warning that if `float_format` is set then floats will be quoted even if csv.QUOTE_NONNUMERIC is set
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Bug in timezone conversion when datetime index has duplicates?
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| 2 | 2016-09-15T17:03:07Z | 2016-09-15T17:18:33Z | 2016-09-15T17:15:22Z |
NONE
| null |
Hello the Pandas team,
I ran this morning into some nasty bug with Pandas 18.1.
I was not able to find whether you are aware of it or not. Here it is
```
idx=[pd.to_datetime('2012-02-27 14:23:00') , pd.to_datetime('2012-08-27 14:33:00'), pd.to_datetime('2012-02-27 14:23:00')]
test=pd.DataFrame({'A':['one','two', 'three']},index=idx)
test['alt']=test.index.tz_localize('UTC').tz_convert('US/Eastern').tz_localize(None)
```
```
Out[9]:
A alt
2012-02-27 14:23:00 one 2012-02-27 09:23:00
2012-08-27 14:33:00 two 2012-08-27 10:33:00
2012-02-27 14:23:00 three 2012-02-27 10:23:00
```
where the third column is obviously wrong. What have I done wrong here? Are indices not meant to work with duplicate values?
Thanks!
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BUG: fix alignment in series ops (GH14227)
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| 5 | 2016-09-15T17:55:24Z | 2016-09-30T21:23:56Z | 2016-09-30T21:23:35Z |
MEMBER
| null |
- [x] closes #14227
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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DOC: Series.map na_action confusion
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[] | null | 0 | 2016-09-15T23:03:52Z | 2016-09-16T23:20:39Z | 2016-09-16T23:20:39Z |
CONTRIBUTOR
| null |
A user on [stackoverflow](http://stackoverflow.com/questions/39461328/in-pandas-what-does-the-na-action-parameter-to-series-map-do/39461948?noredirect=1#comment66278752_39461948) was confused about the use of na_action in Series.map and so I came up with a simple example to explain it. @jorisvandenbossche asked me to submit a PR, so I created this issue to log the issue and will submit a PR now.
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| 2 | 2016-09-15T23:16:35Z | 2016-09-16T23:20:51Z | 2016-09-16T23:20:39Z |
CONTRIBUTOR
| null |
- [x] closes #14231
- [x] tests passed
- [x] passes `git diff upstream/master | flake8 --diff`
Added example to Series.map showing use of na_action parameter.
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| 1 | 2016-09-16T01:16:44Z | 2017-01-24T18:46:27Z | 2017-01-24T18:46:27Z |
CONTRIBUTOR
| null |
I start with a dataframe (`df`) containing staggered measurements (select->copy->`from_clipboard()`):
```
CO2 CO2 CO2 CO2
2016-09-08 00:00:00 1581.0
2016-09-08 00:00:01 1415.0
2016-09-08 00:00:03 1455.0 1446.0
2016-09-08 00:00:05 1581.0
2016-09-08 00:00:06 1415.0
2016-09-08 00:00:08 1456.0 1445.0
2016-09-08 00:00:10 1581.0
2016-09-08 00:00:11 1415.0
2016-09-08 00:00:13 1456.0 1445.0
2016-09-08 00:00:15 1581.0
2016-09-08 00:00:17 1415.0
2016-09-08 00:00:18 1456.0 1445.0
2016-09-08 00:00:20 1581.0
2016-09-08 00:00:22 1415.0
2016-09-08 00:00:23 1456.0 1445.0
2016-09-08 00:00:25 1581.0
2016-09-08 00:00:27 1415.0
2016-09-08 00:00:28 1457.0 1444.0
2016-09-08 00:00:30 1581.0
2016-09-08 00:00:32 1415.0
2016-09-08 00:00:33 1456.0 1444.0
2016-09-08 00:00:35 1581.0
2016-09-08 00:00:37 1415.0
2016-09-08 00:00:38 1456.0 1444.0
2016-09-08 00:00:40 1580.0
2016-09-08 00:00:42 1415.0
2016-09-08 00:00:44 1456.0 1444.0
2016-09-08 00:00:45 1580.0
2016-09-08 00:00:47 1415.0
2016-09-08 00:00:49 1456.0 1445.0
2016-09-08 00:00:50 1579.0
2016-09-08 00:00:52 1415.0
2016-09-08 00:00:54 1456.0 1445.0
2016-09-08 00:00:55 1579.0
2016-09-08 00:00:57 1415.0
2016-09-08 00:00:59 1456.0 1445.0
```
When I try to aggregate measurements into 5-second intervals using `df.resample('5s').median()`, I get this traceback:
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py in median(self)
980 try:
--> 981 return self._cython_agg_general('median')
982 except GroupByError:
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py in _cython_agg_general(self, how, numeric_only)
3047 new_items, new_blocks = self._cython_agg_blocks(
-> 3048 how, numeric_only=numeric_only)
3049 return self._wrap_agged_blocks(new_items, new_blocks)
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py in _cython_agg_blocks(self, how, numeric_only)
3084 result, _ = self.grouper.aggregate(
-> 3085 block.values, how, axis=agg_axis)
3086
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py in aggregate(self, values, how, axis)
1821 def aggregate(self, values, how, axis=0):
-> 1822 return self._cython_operation('aggregate', values, how, axis)
1823
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py in _cython_operation(self, kind, values, how, axis)
1757 func, dtype_str = self._get_cython_function(
-> 1758 kind, how, values, is_numeric)
1759 except NotImplementedError:
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py in _get_cython_function(self, kind, how, values, is_numeric)
1698
-> 1699 ftype = self._cython_functions[kind][how]
1700
KeyError: 'median'
During handling of the above exception, another exception occurred:
AssertionError Traceback (most recent call last)
<ipython-input-55-c17a77e187f3> in <module>()
1 df = chamber_k30['20160908':'20160908T0000']
----> 2 df.resample('5s').median()
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\tseries\resample.py in f(self, _method)
508
509 def f(self, _method=method):
--> 510 return self._downsample(_method)
511 f.__doc__ = getattr(GroupBy, method).__doc__
512 setattr(Resampler, method, f)
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\tseries\resample.py in _downsample(self, how, **kwargs)
661 # we want to call the actual grouper method here
662 result = obj.groupby(
--> 663 self.grouper, axis=self.axis).aggregate(how, **kwargs)
664
665 result = self._apply_loffset(result)
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py in aggregate(self, arg, *args, **kwargs)
3595 @Appender(SelectionMixin._agg_doc)
3596 def aggregate(self, arg, *args, **kwargs):
-> 3597 return super(DataFrameGroupBy, self).aggregate(arg, *args, **kwargs)
3598
3599 agg = aggregate
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py in aggregate(self, arg, *args, **kwargs)
3112
3113 _level = kwargs.pop('_level', None)
-> 3114 result, how = self._aggregate(arg, _level=_level, *args, **kwargs)
3115 if how is None:
3116 return result
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\base.py in _aggregate(self, arg, *args, **kwargs)
426 _level = kwargs.pop('_level', None)
427 if isinstance(arg, compat.string_types):
--> 428 return getattr(self, arg)(*args, **kwargs), None
429
430 if isinstance(arg, dict):
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py in median(self)
990 x = Series(x)
991 return x.median(axis=self.axis)
--> 992 return self._python_agg_general(f)
993
994 @Substitution(name='groupby')
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py in _python_agg_general(self, func, *args, **kwargs)
775 for name, obj in self._iterate_slices():
776 try:
--> 777 result, counts = self.grouper.agg_series(obj, f)
778 output[name] = self._try_cast(result, obj)
779 except TypeError:
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py in agg_series(self, obj, func)
2063 dummy = obj[:0]
2064 grouper = lib.SeriesBinGrouper(obj, func, self.bins, dummy)
-> 2065 return grouper.get_result()
2066
2067 # ----------------------------------------------------------------------
pandas\src\reduce.pyx in pandas.lib.SeriesBinGrouper.get_result (pandas\lib.c:35367)()
pandas\src\reduce.pyx in pandas.lib.Slider.__init__ (pandas\lib.c:40335)()
AssertionError:
```
The other documented dispatching methods (`sum`, `mean`, `std`, `sem`, `max`, `min`, `first`, `last`) work just fine (except for `ohlc`, which produces an `InvalidIndexError`).
I can work around the problem like so: `df.resample('5s').apply(lambda x: x.median())`. But it seems like dispatching should work here...
#### output of `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 23 Stepping 10, 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: 1.11.0
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.3.1
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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MDExOlB1bGxSZXF1ZXN0ODU1MTY0MDg=
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ENH: Allow usecols to accept callable (GH14154)
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| 11 | 2016-09-16T04:08:04Z | 2017-12-12T15:39:47Z | 2016-12-06T11:38:06Z |
MEMBER
| null |
- [ X] closes #14154
- [ X] tests added / passed
- [ X] passes `git diff upstream/master | flake8 --diff`
- [X ] whatsnew entry
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BUG: Python operator 'in' behaves in two different ways on Series object
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[] | null | 3 | 2016-09-16T07:19:11Z | 2021-07-22T07:42:35Z | null |
NONE
| null |
The python operator 'in' acting on a pandas.Series object behaves in two different ways.
When iterating over the series it acts on the list of values. But for membership testing it acts on the index. Is this the desired behavior?
```
import pandas as pd
s = pd.Series([1, 2, 3])
for v in s: print v
# prints 1 2 3, i.e. 'in' is acting on the values
for v in [1, 2, 3]: print v in s
# prints True True False, i.e. the latter 'in' is acting on the index
```
This behavior also leads to peculiar behavior when working with DataFrames.
```
df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
df
# a b
# 0 1 4
# 1 2 5
# 2 3 6
for value in df['a']:
print value,
if value in df['b']:
print 'also in column b'
# 1 also in column b
# 2 also in column b
# 3
```
This is not what one would naively expect from that code snippet and it can potentially lead to errors that might be difficult to spot.
This is the behavior that is also mentioned in the docs (in a pretty hidden way -- one has to actively search for it), but I suggest that this should not be the desired behavior. Instead, I'd find it much more natural, if 'in' worked always on the values and never on the index. If needed one can always use 'in s.index', but the standard way should be acting on the values always.
pandas version 0.18.1
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BUG: set_levels set illegal levels.
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| 9 | 2016-09-16T18:53:30Z | 2016-10-10T12:30:37Z | 2016-10-10T12:30:22Z |
CONTRIBUTOR
| null |
- [x] closes #13754
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
`MultiIndex.set_levels`, when given illegal level values, raises an error.
When `inplace=True`, though, the illegal level values are still accepted. This
commit fixes that behavior by checking that the proposed level values are legal
before setting them.
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| 14,237 |
BUG: Check for overflow in TimedeltaIndex addition.
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| 53 | 2016-09-16T19:05:55Z | 2016-09-28T22:17:31Z | 2016-09-27T10:43:45Z |
MEMBER
| null |
Title is self-explanatory. Closes #14068.
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MDU6SXNzdWUxNzc1NDcwNjY=
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Faulty nogil assumption in _Py_HashDouble
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| 4 | 2016-09-17T01:00:50Z | 2016-09-17T20:07:23Z | 2016-09-17T01:09:59Z |
NONE
| null |
Pandas uses _Py_HashDouble as the hash function for hashing floats. () in the 0.17.x and 0.18.x releases. This hash function is used within nogil sections within hashtable.pyx on both these releases. However the definition of this function as implemented on Python 2.x can call into the Python api depending on the values being hashed. This causes crashes if there is another thread using holding the GIL which is executing simultaneously. I see that master no longer uses this hash function. But can this fix be backported for the 0.17.x / 0.18.x branches?
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| 177,562,335 |
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| 14,239 |
PERF: Spearman correlation
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| 7 | 2016-09-17T07:45:24Z | 2019-09-10T21:42:06Z | 2019-09-10T20:12:10Z |
NONE
| null |
I'm trying to compute Spearman correlation for a relatively small DataFrame (~500x~8000), but the calculation appears to be orders of magnitude slower than Pearson correlation.
Any help to make Spearman's speed comparable to Pearson's would be greatly appreciated.
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TST/TEMP: fix pyqt to 4.x for plotting tests
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[] | null | 2 | 2016-09-17T10:05:09Z | 2016-09-18T09:39:00Z | 2016-09-18T09:39:00Z |
MEMBER
| null |
To fix breaking tests (latest travis build on master broken, and also all recent PR builds), see https://github.com/matplotlib/matplotlib/issues/7124
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DataFrame Query Rounding Error
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| 1 | 2016-09-17T22:57:06Z | 2016-09-22T10:20:26Z | 2016-09-22T10:20:26Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
import pandas as pd
df = pd.DataFrame([{"A": 1000000000.0099}])
cutoff = 1000000000.006
local_res = "works" if df.query("A < @cutoff").empty else "fails"
print("query by local var %s" % local_res)
explicit_res = "works" if df.query("A < %.3f" % cutoff).empty else "fails"
print("explicit query %s" % explicit_res)
#### Expected Output
query by local var works
explicit query fails
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-42-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: 20.3
Cython: 0.23.4
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.6.1
xarray: None
IPython: 5.0.0
sphinx: 1.3.5
patsy: 0.4.1
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
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: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
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| 177,640,712 |
MDU6SXNzdWUxNzc2NDA3MTI=
| 14,242 |
Recoding as numerical categories with multiple columns
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| 2 | 2016-09-18T09:10:59Z | 2016-09-18T10:48:28Z | 2016-09-18T10:48:27Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
This SO questions asks the simple question of how to recode strings in a data frame as numerical categories http://stackoverflow.com/questions/39475187/how-to-speed-up-recoding-into-integers .
The pandas solution x = df.apply(lambda x: x.astype('category').cat.codes) Is by far the fastest. However it doesn't give a consistent answer if the data frame has more than one column.
E.g.
g,k
a,h
c,i
j,e
d,i
i,h
b,b
d,d
i,a
d,h
gets recoded to:
0 1
0 4 6
1 0 4
2 2 5
3 6 3
4 3 5
5 5 4
6 1 1
7 3 2
8 5 0
9 3 4
Notice that 'd' is mapped to 3 in the first column but 2 in the second.
It would be great if pandas could do this recoding consistently.
#### Expected Output
#### output of `pd.show_versions()`
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Revert "TST/TEMP: fix pyqt to 4.x for plotting tests"
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| 4 | 2016-09-18T09:39:40Z | 2018-09-13T14:19:38Z | 2016-11-25T16:19:02Z |
MEMBER
| null |
Reverts pydata/pandas#14240
For when https://github.com/ContinuumIO/anaconda-issues/issues/1068 is solved
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TST: Some nose generator tests not running
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| 1 | 2016-09-18T14:29:02Z | 2016-09-19T20:11:32Z | 2016-09-19T20:11:32Z |
CONTRIBUTOR
| null |
e.g. pandas/computation/tests/test_eval.py:TestTypeCasting
``` bash
nosetests pandas/computation/tests/test_eval.py:TestTypeCasting -vs -x --with-id
#431 test_binop_typecasting (pandas.computation.tests.test_eval.TestTypeCasting) ... ok
```
The obvious difference is that `TestTypeCasting` inherits from `tm.TestCase`. I _thought_ nose was supposed to run these still, but apparently not.
EDIT: OK, nose [doesn't support those](http://nose.readthedocs.io/en/latest/writing_tests.html#test-classes):
> Test classes that do not descend from unittest.TestCase may also include generator methods and class-level fixtures
This came up as part of verifying that https://github.com/pydata/pandas/pull/13856 isn't dropping any tests.
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TST: Fix generator tests to run
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| 2 | 2016-09-18T14:38:51Z | 2017-04-05T02:08:32Z | 2016-09-19T20:11:32Z |
CONTRIBUTOR
| null |
Closes #14244
This class inheriting from TestCase caused
the tests the yield fixture tests to not actually
run. The new output is
```
nosetests pandas/computation/tests/test_eval.py:TestTypeCasting
........................................
----------------------------------------------------------------------
Ran 40 tests in 0.264s
OK
```
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| 14,246 |
Pivot table drops column/index names=nan when dropna=false
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[] | null | 12 | 2016-09-18T17:53:08Z | 2017-04-26T05:57:13Z | 2017-04-26T05:53:04Z |
CONTRIBUTOR
| null |
- [x] closes #14072
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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| 14,247 |
COMPAT: numexpr interop of logical operations with non-boolean dtypes
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| 3 | 2016-09-19T07:04:47Z | 2020-04-10T03:47:36Z | null |
NONE
| null |
```
#### Code Sample, a copy-pastable example if possible
# encoding: UTF-8
import pandas as pd
import numpy as np
data1 = {"x": np.random.randint(2, size=6000),
"y":np.random.randint(2, size=6000),
}
df1 = pd.DataFrame(data1)
data2 = {"x": np.random.randint(2, size=6000),
"y":np.random.randint(2, size=6000),
}
df2 = pd.DataFrame(data2)
df = df1&df2
print df
#### Expected Output
Print the dataframe fill with 0 or 1
#### output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 32
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: None
setuptools: None
Cython: None
numpy: 1.11.1
scipy: 0.16.1
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: 2.5.2
matplotlib: 1.4.3
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
None
```
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str.extractall gives ValueError: Length of names must match number of levels in MultiIndex.
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| 2 | 2016-09-19T07:41:38Z | 2016-09-19T10:34:01Z | 2016-09-19T10:18:32Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
s = Series(["HCPSS_000_123", "HCPSS_100_1234", "HCPSS_200_1234"], index=["CM_1","CM_2","CM_3"])
module = s.str.extractall("HCPSS_(\d{3})")
## Gives the following error
ValueError Traceback (most recent call last)
<ipython-input-43-0208f0c2a4e9> in <module>()
----> 1 module = s.str.extractall("HCPSS_(\d{3})")
C:\Users\lmudge\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\strings.pyc in extractall(self, pat, flags)
1619 @copy(str_extractall)
1620 def extractall(self, pat, flags=0):
-> 1621 return str_extractall(self._orig, pat, flags=flags)
1622
1623 _shared_docs['find'] = ("""
C:\Users\lmudge\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\strings.pyc in str_extractall(arr, pat, flags)
711 if 0 < len(index_list):
712 index = MultiIndex.from_tuples(
--> 713 index_list, names=arr.index.names + ["match"])
714 else:
715 index = None
C:\Users\lmudge\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\indexes\multi.pyc in from_tuples(cls, tuples, sortorder, names)
893 arrays = lzip(*tuples)
894
--> 895 return MultiIndex.from_arrays(arrays, sortorder=sortorder, names=names)
896
897 @classmethod
C:\Users\lmudge\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\indexes\multi.pyc in from_arrays(cls, arrays, sortorder, names)
848
849 return MultiIndex(levels=levels, labels=labels, sortorder=sortorder,
--> 850 names=names, verify_integrity=False)
851
852 @classmethod
C:\Users\lmudge\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\indexes\multi.pyc in **new**(cls, levels, labels, sortorder, names, copy, verify_integrity, _set_identity, name, **kwargs)
95 if names is not None:
96 # handles name validation
---> 97 result._set_names(names)
98
99 if sortorder is not None:
C:\Users\lmudge\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\indexes\multi.pyc in _set_names(self, names, level, validate)
450 raise ValueError('Length of names must match length of level.')
451 if validate and level is None and len(names) != self.nlevels:
--> 452 raise ValueError('Length of names must match number of levels in '
453 'MultiIndex.')
454
ValueError: Length of names must match number of levels in MultiIndex.
#### Expected Output
No error
#### output of `pd.show_versions()`
## 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 78 Stepping 3, 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: 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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BUG: NaN in multi-index and to_latex()
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| 1 | 2016-09-19T09:44:29Z | 2018-04-25T01:28:01Z | 2018-04-25T01:28:01Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
I was trying to find a small reproducible code for a [larger issue ](http://stackoverflow.com/questions/39569791/pandas-replaces-nan-with-arbitrary-number-when-to-latex)that I wanted to report. On the way there, I stumbled across this (probably related) issue:
```
# test data frame:
>>> test
Out[36]:
robustness value change
0 baseline NaN -14.5
```
```
# as expected:
>>> test.set_index(['robustness', 'value']).to_csv()
Out[34]: 'robustness,value,change\nbaseline,,-14.5\n'
```
And here's what `to_latex()` makes out of it:
```
>>> test.set_index(['robustness', 'value']).to_latex()
Traceback (most recent call last):
File "/usr/local/anaconda2/envs/myenv2/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2885, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-35-901bbd119ed6>", line 1, in <module>
test.set_index(['robustness', 'value']).to_latex()
File "/usr/local/anaconda2/envs/myenv2/lib/python2.7/site-packages/pandas/core/frame.py", line 1601, in to_latex
encoding=encoding)
File "/usr/local/anaconda2/envs/myenv2/lib/python2.7/site-packages/pandas/core/format.py", line 638, in to_latex
latex_renderer.write_result(self.buf)
File "/usr/local/anaconda2/envs/myenv2/lib/python2.7/site-packages/pandas/core/format.py", line 824, in write_result
blank = ' ' * len(lev2[0])
IndexError: list index out of range
```
#### Expected Output
#### output of `pd.show_versions()`
> > > pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-36-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: None
pip: 8.1.1
setuptools: 21.2.1
Cython: None
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: None
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: None
xlrd: None
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
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| 177,819,967 |
MDU6SXNzdWUxNzc4MTk5Njc=
| 14,250 |
rename() take datetime.date as a function
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[] | null | 3 | 2016-09-19T15:40:46Z | 2016-09-20T09:45:51Z | 2016-09-20T09:45:51Z |
NONE
| null |
I would like to rename a pd.Series but got two results on two different platforms.
For example, if I have a pd.Series as `a` and a datetime.date as `one_date`, and I do:
```
a.rename(one_date)
```
On my own laptop (Window 7, Python 2.7), I got no error.
But when I do this on a server (Linux, Python 2.7), I got an error:
Traceback (most recent call last):
File "as.py", line 626, in <module>
run_batch(sys.argv[1:])
File "as.py", line 549, in run_batch
analyzer.analyze_batch(one_user, batch_index_user, user_index)
File "as.py", line 467, in analyze_batch
clicks_one_date)
File "as.py", line 434, in analyze_one_date_batch
return counts_on_date.rename(one_date)
File "/home/xxx/anaconda2/anaconda/lib/python2.7/site-packages/pandas/core/series.py", line 2264, in rename
return super(Series, self).rename(index=index, **kwargs)
File "/home/xxx/anaconda2/anaconda/lib/python2.7/site-packages/pandas/core/generic.py", line 606, in rename
result._data = result._data.rename_axis(f, axis=baxis, copy=copy)
File "/home/xxx/anaconda2/anaconda/lib/python2.7/site-packages/pandas/core/internals.py", line 2588, in rename_axis
obj.set_axis(axis, _transform_index(self.axes[axis], mapper))
File "/home/xxx/anaconda2/anaconda/lib/python2.7/site-packages/pandas/core/internals.py", line 4391, in _transform_index
items = [func(x) for x in index]
TypeError: 'datetime.date' object is not callable
---
It seems like the method `rename` take the `one_date` as a function instead of a scalar value. Then it tried to call the `datetime.date` as a function but it is not callable.
Anyone has some idea about how to deal with it?
Thanks.
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DataFrame.reset_index(inplace=True) does not return self, harming composability
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[] | null | 7 | 2016-09-19T17:29:22Z | 2021-10-29T02:01:41Z | null |
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```
df = pd.DataFrame({'foo': [1, 2, 3, 4]},
index=pd.Index(['a', 'b', 'c', 'd'], name='names'))
df.reset_index(inplace=True)
```
returns None
#### Expected Output
Same as output of
```
df.reset_index()
```
#### output of `pd.show_versions()`
This is on trunk. Caused by the lines in DataFrame.reset_index
```
if not inplace:
return new_obj
```
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BUG: keys argument in pandas.concat does not preserve Index name
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| 0 | 2016-09-19T17:32:59Z | 2016-10-09T17:38:05Z | 2016-10-09T17:38:05Z |
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```
df = pd.DataFrame({'foo': [1, 2, 3, 4],
'bar': [0.1, 0.2, 0.3, 0.4]})
index = pd.Index(['a', 'b'], name='baz')
concatted = pd.concat([df, df], keys=index)
concatted.index.names
## -- End pasted text --
Out[3]: FrozenList([None, None])
```
#### Expected Output
```
FrozenList(['baz', None])
```
#### output of `pd.show_versions()`
A few days older than master:
```
commit: 5e2f9da6e8e713bd89cfe8760e63583ea7d29879
```
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MDU6SXNzdWUxNzc4NjQ0NTg=
| 14,253 |
Add left_by/right_by and left_index/right_index to merge_asof()
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| 4 | 2016-09-19T18:45:34Z | 2016-12-14T16:10:42Z | 2016-12-14T16:10:42Z |
CONTRIBUTOR
| null |
The current `merge_asof()` has only one parameter for `by`, assuming that both DataFrames have the same column name. It does have `left_on` and `right_on`, so perhaps something similar for `by` would be helpful.
Also, there is no `left_index` or `right_index` parameter in `merge_asof()`. Adding these might be helpful too.
For example:
```
pd.merge_asof(df1, df2,
left_index=True, right_on='timestamp',
left_by='Ticker', right_by='ticker')
```
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| 14,254 |
QuarterBegin wrong date
|
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| 16 | 2016-09-19T19:30:54Z | 2016-09-20T15:35:35Z | 2016-09-19T19:40:54Z |
NONE
| null |
Would expect this to point to one of (1, 4, 7, 10)...
```
#### Code Sample, a copy-pastable example if possible
import pandas as pd
pd.Timestamp.today() - pd.tseries.offsets.QuarterBegin()
>Timestamp('2016-09-01 15:29:06.556269')
#### Expected Output
Timestamp('2016-07-01 15:29:06.556269')
#### output of ``pd.show_versions()``
In [4]: 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: 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: None
IPython: 5.1.0
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: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
```
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| 14,255 |
BUG: float truncation in eval with py 2
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| 4 | 2016-09-20T00:43:22Z | 2016-09-22T10:20:37Z | 2016-09-22T10:20:26Z |
CONTRIBUTOR
| null |
- [x] closes #14241
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Python 2 only - apparently `str()` rounds shorter than `repr()`
```
In [1]: f = 1000000000.006
In [2]: str(f)
Out[2]: '1000000000.01'
In [3]: repr(f)
Out[3]: '1000000000.006'
```
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MDU6SXNzdWUxNzgwMDM5MzU=
| 14,256 |
BUG: to_json with objects causing segfault
|
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| 14 | 2016-09-20T08:58:43Z | 2018-03-21T16:10:57Z | 2017-10-14T14:36:39Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
Creating an bson objectID, without giving an objectID exclusively is ok.
``` python
>>> import bson
>>> import pandas as pd
>>> pd.DataFrame({'A': [bson.objectid.ObjectId()]}).to_json()
Out[4]: '{"A":{"0":{"binary":"W\\u0e32\\u224cug\\u00fcR","generation_time":1474361586000}}}'
>>> pd.DataFrame({'A': [bson.objectid.ObjectId()], 'B': [1]}).to_json()
Out[5]: '{"A":{"0":{"binary":"W\\u0e4e\\u224cug\\u00fcS","generation_time":1474361614000}},"B":{"0":1}}'
```
However, if you provide an ID explicitly, an exception is raised
``` python
>>> pd.DataFrame({'A': [bson.objectid.ObjectId('574b4454ba8c5eb4f98a8f45')]}).to_json()
Traceback (most recent call last):
File "/auto/energymdl2/anaconda/envs/commod_20160831/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2885, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-7-c9a20090d481>", line 1, in <module>
pd.DataFrame({'A': [bson.objectid.ObjectId('574b4454ba8c5eb4f98a8f45')]}).to_json()
File "/auto/energymdl2/anaconda/envs/commod_20160831/lib/python2.7/site-packages/pandas/core/generic.py", line 1056, in to_json
default_handler=default_handler)
File "/auto/energymdl2/anaconda/envs/commod_20160831/lib/python2.7/site-packages/pandas/io/json.py", line 36, in to_json
date_unit=date_unit, default_handler=default_handler).write()
File "/auto/energymdl2/anaconda/envs/commod_20160831/lib/python2.7/site-packages/pandas/io/json.py", line 79, in write
default_handler=self.default_handler)
OverflowError: Unsupported UTF-8 sequence length when encoding string
```
And worse, if the column is not the only column, the entire process dies.
``` python
>>> pd.DataFrame({'A': [bson.objectid.ObjectId('574b4454ba8c5eb4f98a8f45')], 'B': [1]}).to_json()
Process finished with exit code 139
```
#### Expected Output
#### output of `pd.show_versions()`
```
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 26.1.1
Cython: 0.24
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: 0.7.2
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.1
dateutil: 2.5.2
pytz: 2016.6.1
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: 4.3.2
html5lib: 0.999
httplib2: 0.9.2
apiclient: 1.5.0
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
```
pymongo version is 3.3.0
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| 178,076,651 |
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| 14,257 |
Installation issue with --editable option
|
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| 21 | 2016-09-20T14:30:24Z | 2016-09-21T12:25:32Z | 2016-09-20T22:20:18Z |
NONE
| null |
Hello,
I'm trying to install pandas on Mac OS X 10.11.6 in a Django project. Pandas library is used with one of the 3rd party libraries that I am using in the project.
Since I don't want to switch to different package manager (Anakonda) just for installing pandas (I'm using pip now), in order for me to get the C files compiled, I decided to install pandas by including `--editable git+git://github.com/pydata/[email protected]#egg=pandas` in the `requirements.txt` file and the library is now installed in my `src` dir and there's a symlink in my virtualenv's `site-packages` which points to it.
This gets these headers compiled, but causes many strange importing errors in pandas source code itself when it is imported by a third party library that is also installed with `--editable` mode. F. ex. file at `Users/eimantas/Desktop/Projects/WeShareSolar/mijnstroom/virtualenv/src/knmi-py/knmi/parsers.py` has this line `import pandas as pd`. And I had to edit this code block in `virtualenv/src/pandas/pandas/__init__.py` for things to work:
```
try:
# XXX: I had to comment this out for things to work:
# from pandas import hashtable, tslib, lib
import hashtable, tslib, lib
except ImportError as e: # pragma: no cover
module = str(e).lstrip('cannot import name ') # hack but overkill to use re
raise ImportError("C extension: {0} not built. If you want to import "
"pandas from the source directory, you may need to run "
"'python setup.py build_ext --inplace' to build the C "
"extensions first.".format(module))
```
But then another importing error appears here: `virtualenv/src/pandas/pandas/core/config.py`:
```
File "/Users/eimantas/Desktop/Projects/WeShareSolar/mijnstroom/virtualenv/src/pandas/pandas/core/config.py", line 57, in <module>
import pandas.compat as compat
AttributeError: 'module' object has no attribute 'compat'
```
What should I do to get these things fixed ? I'd love to be able to use this library with pip and Django.
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| 178,103,848 |
MDU6SXNzdWUxNzgxMDM4NDg=
| 14,258 |
BUG: MultiIndex set_levels is not symmetrical with get_level_values
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| 6 | 2016-09-20T16:04:31Z | 2016-09-22T12:47:23Z | 2016-09-20T22:21:21Z |
NONE
| null |
#### Code Sample
```
import pandas as pd
import io
text = """
A B C
1 1 1
1 2 2
2 1 3
2 2 4
"""
df = pd.read_csv(io.StringIO(text), delimiter = ' ')
# Note the output of get_level_values is the list of all values [1, 1, 2, 2]
print(df.index.get_level_values('A').tolist())
df.set_index(['A','B'], inplace = True)
df.index.set_levels(df.index.get_level_values('A').map(lambda x: x * 2), level='A', inplace=True)
print(df.index.get_level_values('A').tolist())
# outputs [2, 2, 2, 2]
```
#### Expected Output
`[2, 2, 4, 4]`
```
# If I re-order the input as:
text = """
A B C
1 1 1
2 1 2
1 2 3
2 2 4
"""
# it works as I expected giving [2, 4, 2, 4]
```
Is this a bug or expected behaviour?
#### output of `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: None
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: 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: 2.5
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: 0.7.5.None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
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| 14,259 |
[0.19dev] Time-series aware rolling window with MultiIndex with a time-frequency index fails
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| 5 | 2016-09-20T17:08:02Z | 2016-09-20T19:50:51Z | 2016-09-20T19:27:53Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```
dft = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
index=pd.date_range('20130101 09:00:00', periods=5, freq='s'))
dft['another'] = ['a', 'a', 'c', 'd', 'e']
dft.set_index([dft.index, 'another'])
dft.rolling('2s')
```
#### Expected Output
Expected to be able to use a time-series aware rolling window, instead got `ValueError: window must be an integer`
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.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
LOCALE: en_US.UTF-8
pandas: 0.19.0rc1
nose: None
pip: 6.0.8
setuptools: 27.2.0
Cython: None
numpy: 1.11.1
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
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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Styler.background_gradient needs to handle NaN values
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| 19 | 2016-09-20T17:40:26Z | 2019-12-27T22:53:28Z | 2019-08-22T17:23:51Z |
CONTRIBUTOR
| null |
This code is ok under Pandas-0.18.1 (from a @jreback demo)
```
%matplotlib inline
# Pandas interactive
import pandas as pd
import numpy as np
import seaborn as sns
# create a df with random datas
np.random.seed(24)
df = pd.DataFrame({'A': np.linspace(1, 10, 10)})
df = pd.concat([df, pd.DataFrame(np.random.randn(10, 4), columns=list('BCDE'))],
axis=1)
df.iloc[0, 2] = np.nan
# interactive
from IPython.html import widgets
@widgets.interact
def f(h_neg=(0, 359, 1), h_pos=(0, 359), s=(0., 99.9), l=(0., 99.9)):
return (df
.style
.background_gradient(
cmap=sns.palettes.diverging_palette(
h_neg=h_neg, h_pos=h_pos, s=s, l=l, as_cmap=True)
).highlight_null()
)
```
With Pandas-0.19rc1, I get a warning:
see https://github.com/matplotlib/matplotlib/issues/7129
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MDExOlB1bGxSZXF1ZXN0ODYwMjQ3OTQ=
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BUG: fix for GH14252
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[] | null | 0 | 2016-09-20T17:54:33Z | 2016-09-20T18:39:53Z | 2016-09-20T18:39:53Z |
NONE
| null |
- [x] closes #14252
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
This should fix #14252 . It's possible that not every one of these changes is needed, or there is a better way to fix it, but I think this should do it.
|
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multiple dispatch problem when comparing pandas Series against numpy scalars
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| 1 | 2016-09-20T18:01:30Z | 2016-09-20T18:13:17Z | 2016-09-20T18:12:59Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
import numpy as np
# this works
pd.Series([7,8,9]) >= np.int64(8)
# this doesn't
np.int64(8) <= pd.Series([7,8,9])
```
This happens with all numpy scalar types I tested
#### Expected Output
I would expect the output should be the same regardless of the order and that neither should fail
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-573.22.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.4
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.21
numpy: 1.10.2
scipy: 0.15.1
statsmodels: 0.6.1
xarray: None
IPython: 4.0.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 2.3.2
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.32.1
pandas_datareader: None
```
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read_hdf crash python process when use it in multithread code
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| 6 | 2016-09-20T18:21:59Z | 2016-09-21T16:54:57Z | 2016-09-20T19:29:03Z |
NONE
| null |
one of my folder contains multiple h5 files, and I tried to load them into dataframes and then concat these df into one.
the python process crashes when the num_tasks>1, if I debug thread by thread, it works, in another, it crashes simply when two threads run at the same time, even though they read different files.
```
from multiprocessing.pool import ThreadPool
import pandas as pd
num_tasks=2
def readjob(x):
path = x
return pd.read_hdf(path,"df",mode='r')
pool = ThreadPool(num_tasks)
results = pool.map(readjob,files)
```
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to_numeric fails to coerce on TypeError, only works on ValueError
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| 3 | 2016-09-20T18:30:02Z | 2016-09-20T18:51:23Z | 2016-09-20T18:50:42Z |
NONE
| null |
Calling to_numeric with errors='coerce' should coerce all non-numeric values to NaN. However, it only seems to work when ValueError is thrown and not for TypeErrors.
#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
print "Pandas Version %s\n" % pd.__version__
for badval in ["hello", [], {}]:
series = pd.Series([badval])
coerced = pd.to_numeric(series, errors='coerce')
try:
pd.to_numeric(series)
except Exception as e:
error = e
print "Bad Value: %s error=%s coerced=%s" % (badval, type(error).__name__, coerced.dtype)
print "DataFrame:"
print coerced
print ""
```
#### Expected Output
```
Pandas Version 0.18.1
Bad Value: hello error=ValueError coerced=float64
DataFrame:
0 NaN
dtype: float64
Bad Value: [] error=TypeError coerced=object
DataFrame:
0 []
dtype: object
Bad Value: {} error=TypeError coerced=object
DataFrame:
0 {}
dtype: object
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.0.9-63_fbk11_3989_g7d5ca22
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.4
pip: 8.1.2
setuptools: 25.1.6
Cython: 0.22
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.6.1
xarray: None
IPython: 5.0.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.4.1
pytz: 2015.2
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.6.1
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.6.7
lxml: 3.4.2
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.9
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.36.0
pandas_datareader: None
```
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| 178,141,962 |
MDU6SXNzdWUxNzgxNDE5NjI=
| 14,265 |
astype(np.int32) converts np.inf to int32 min value
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| 2 | 2016-09-20T18:42:29Z | 2016-12-11T22:23:50Z | 2016-12-11T22:23:50Z |
NONE
| null |
It seems that converting np.inf to int32 and int64 using pd.astype converts them to the min int value rather than throwing an exception. Presumably this is because they raise OverflowError and not ValueError
#### Code Sample, a copy-pastable example if possible
```
for typ in [np.int32, np.int64]:
for badval in [np.nan, np.inf]:
# Print the exception thrown by casting to numpy directly
try:
typ(badval)
except Exception as e:
print "np.%s(np.%s) throws %s: %s" % (typ.__name__, badval, type(e).__name__, e)
# Print the exception or resulting value by casting using pd.astype
try:
typed = pd.Series([badval]).astype(typ)
print "pd.Series([np.%s]).astype(np.%s) returns dataframe containing %s" % (badval, typ.__name__, typed[0])
print "Returned DataFrame:"
print typed
except Exception as e:
print "pd.Series([np.%s]).astype(np.%s) throws %s: %s" % (badval, typ.__name__, type(e).__name__, e)
print ""
```
#### Expected Output
```
np.int32(np.nan) throws ValueError: cannot convert float NaN to integer
pd.Series([np.nan]).astype(np.int32) throws ValueError: Cannot convert NA to integer
np.int32(np.inf) throws OverflowError: cannot convert float infinity to integer
pd.Series([np.inf]).astype(np.int32) returns dataframe containing -2147483648
Returned DataFrame:
0 -2147483648
dtype: int32
np.int64(np.nan) throws ValueError: cannot convert float NaN to integer
pd.Series([np.nan]).astype(np.int64) throws ValueError: Cannot convert NA to integer
np.int64(np.inf) throws OverflowError: cannot convert float infinity to integer
pd.Series([np.inf]).astype(np.int64) returns dataframe containing -9223372036854775808
Returned DataFrame:
0 -9223372036854775808
dtype: int64
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.0.9-63_fbk11_3989_g7d5ca22
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.4
pip: 8.1.2
setuptools: 25.1.6
Cython: 0.22
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.6.1
xarray: None
IPython: 5.0.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.4.1
pytz: 2015.2
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.6.1
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.6.7
lxml: 3.4.2
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.9
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.36.0
pandas_datareader: None
```
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MDU6SXNzdWUxNzgyNzk2ODI=
| 14,266 |
Large Monotonic Index Objects Always Allocate Hash Tables on get_loc
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| 18 | 2016-09-21T08:28:34Z | 2016-09-22T10:03:25Z | 2016-09-22T10:03:24Z |
CONTRIBUTOR
| null |
Historically, large monotonically-increasing `Index` objects would attempt to avoid creating a large hash table on `get_loc` calls. In service of this goal, many `IndexEngine` methods have guards like the one in `DatetimeEngine.get_loc`:
``` python
if self.over_size_threshold and self.is_monotonic_increasing:
if not self.is_unique:
val = _to_i8(val)
return self._get_loc_duplicates(val)
# Do lookup using `searchsorted`
# Do lookup using hash table.
```
Since at least 5eecdb2dd94719e1fd097ce3fb046697445a3d7f, `self.is_unique` has been implemented as a property that would force a hash table to be created **unless the index had already been marked as unique**. Until https://github.com/pydata/pandas/pull/10199, the `is_monotonic_increasing` property would perform a check that would [sometimes set `self.unique` to True](https://github.com/pydata/pandas/pull/10199/files#diff-4f3357a9f53e943087e2778134494905L236), which would prevent the large hash table allocation. After the commit linked above, however, the only code path that ever sets `IndexEngine.unique` is in [`IndexEngine.initialize`](https://github.com/pydata/pandas/blob/a7469cf98275a183ad2e4bfafa9706a1ef8d035e/pandas/index.pyx#L269-L270), which [unconditionally creates a hash table before setting the unique flag.](https://github.com/pydata/pandas/blob/a7469cf98275a183ad2e4bfafa9706a1ef8d035e/pandas/index.pyx#L266-L267).
#### Code Sample, a copy-pastable example if possible
``` python
import os
import humanize
import psutil
import pandas as pd
def get_mem_usage():
pid = os.getpid()
proc = psutil.Process(pid)
return humanize.naturalsize(proc.memory_full_info().uss)
print("Pandas Version: " + pd.__version__)
print("Before Index Creation: " + get_mem_usage())
# The cutoff for allocating a hash table inside the index is supposed to be
#1,000,000 entries in the index. This index is about 10x larger.
data = pd.date_range('1990', '2016', freq='min')
print("After Index Creation: " + get_mem_usage())
# Trigger a hash of the index's contents.
data.get_loc(data[5])
print("After get_loc() call: " + get_mem_usage())
```
#### Output (Old Pandas):
```
$ python repro.py
Pandas Version: 0.16.1
Before Index Creation: 36.8 MB
After Index Creation: 146.5 MB
After get_loc() call: 146.6 MB
```
#### Output (Pandas 0.18.1)
```
$ python repro.py
Pandas Version: 0.18.1
Before Index Creation: 47.6 MB
After Index Creation: 157.4 MB
After get_loc() call: 698.7 MB
```
For some context, I found this after the internal Jenkins build for Zipline (which makes heavy use of large minutely `DatetimeIndex`es to represent trading calendars) started failing with memory errors after merging https://github.com/quantopian/zipline/pull/1339.
Assuming that the memory-saving behavior of older pandas is still desired, I think the right immediate fix for this is to change `IndexEngine._do_unique_check` to actually do a uniqueness check instead of just forcing a hash table creation. Reading through the code, however, there are a bunch of ways that large `Index`es could still hit code paths that trigger hash table allocations. For example, `DatetimeEngine.__contains__` guards against `self.over_size_threshold`, but none of the other `IndexEngine` subclasses do. A more significant refactor is probably needed to provide a meaningful guarantee that indices don't consume too much memory.
#### output of `pd.show_versions()`
<details>
In [4]: pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-16-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.1
nose: 1.3.7
Cython: 0.22.1
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 5.1.0
sphinx: 1.3.4
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.4
bottleneck: 1.0.0
tables: None
numexpr: 2.4.6
matplotlib: 1.4.3
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.8
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
</details>
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MDU6SXNzdWUxNzgzMTkwNzg=
| 14,267 |
Problem reading HTML tables with colspan
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| 2 | 2016-09-21T11:25:57Z | 2017-07-23T14:31:37Z | 2017-07-23T14:31:26Z |
NONE
| null |
Hi, guys! First of all, thanks for making this open source! =D Keep up the good work!
**SCRIPT**
```
import pandas as pd
dfs = pd.read_html('https://en.wikipedia.org/wiki/List_of_states_and_territories_of_the_United_States')
print(dfs[0])
```
The output we found where there is any colspan is considering only the first column, and the other values are pulled back in relation to the colspan amount, making the values on the tail's row as "NaN"
**#### Expected Output**
In this web page, there is a colspan in the "capital" and "largest city" columns when they are the same, I think that in a general way we would expect that the value would be duplicate on the following <colspan's amount> columns in the generated DataFrame
#### output of `pd.show_versions()`
## 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 15 Stepping 13, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: None
xarray: None
IPython: 5.1.0
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: 4.5.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: 0.2.1
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| 178,330,871 |
MDExOlB1bGxSZXF1ZXN0ODYxNTE3NzU=
| 14,268 |
Update Github issue template
|
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closed
| false | null |
[] | null | 5 | 2016-09-21T12:22:46Z | 2017-04-05T02:08:33Z | 2016-09-23T21:38:17Z |
CONTRIBUTOR
| null |
Mostly just using the `details` tag to reduce some clutter in all the issues, e.g.
---
#### A small, complete example of the issue
``` python
# Your code here
def fib(n):
return fib(n-1) + fib(n-2)
```
#### Expected Output
Nothing
#### Output of `pd.show_versions()`
<details>
## 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: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0rc1+21.ge596cbf
nose: 1.3.7
pip: 8.1.2
setuptools: 26.1.1
Cython: 0.25a0
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.8.0rc1
xarray: 0.7.2
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: 1.4.1
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.2
openpyxl: 2.3.5
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: None
lxml: 3.4.4
bs4: 4.4.1
html5lib: 0.9999999
httplib2: 0.9.2
apiclient: 1.5.1
sqlalchemy: 1.0.12
pymysql: 0.7.6.None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
</details>
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MDU6SXNzdWUxNzgzMzQzNjI=
| 14,269 |
test_expressions.test_integer* fail badly while building on Debian sid amd64 python3.5
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closed
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[] | null | 3 | 2016-09-21T12:36:53Z | 2021-05-01T23:49:00Z | 2021-05-01T23:49:00Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
Here is a sample (full build log and all 4 failures are at http://neuro.debian.net/_files/_buildlogs/pandas/0.18.1+git25-ga7469cf/pandas_0.18.1+git25-ga7469cf-1_amd64.build )
edit 1: it is all fine with python 2.7
```
======================================================================
FAIL: test_integer_with_zeros (pandas.tests.test_expressions.TestExpressions)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/build/pandas-0.18.1+git25-ga7469cf/debian/tmp/usr/lib/python3/dist-packages/pandas/tests/test_expressions.py", line 244, in test_integer_with_zeros
assert_frame_equal)
File "/build/pandas-0.18.1+git25-ga7469cf/debian/tmp/usr/lib/python3/dist-packages/pandas/tests/test_expressions.py", line 100, in run_arithmetic_test
assert_func(expected, result)
File "/build/pandas-0.18.1+git25-ga7469cf/debian/tmp/usr/lib/python3/dist-packages/pandas/util/testing.py", line 1313, in assert_frame_equal
obj='DataFrame.iloc[:, {0}]'.format(i))
File "/build/pandas-0.18.1+git25-ga7469cf/debian/tmp/usr/lib/python3/dist-packages/pandas/util/testing.py", line 1181, in assert_series_equal
obj='{0}'.format(obj))
File "pandas/src/testing.pyx", line 59, in pandas._testing.assert_almost_equal (pandas/src/testing.c:3783)
File "pandas/src/testing.pyx", line 173, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2987)
File "/build/pandas-0.18.1+git25-ga7469cf/debian/tmp/usr/lib/python3/dist-packages/pandas/util/testing.py", line 1018, in raise_assert_detail
raise AssertionError(msg)
AssertionError: DataFrame.iloc[:, 0] are different
DataFrame.iloc[:, 0] values are different (0.57994 %)
[left]: [-9223372036854775808, -9223372036854775808, 1, 1, 1, -9223372036854775808, 1, 1, -9223372036854775808, 10000000000, 1, 1, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, 1, -9223372036854775808, 1, 1, -9223372036854775808, -9223372036854775808, 3125, 1, -9223372036854775808, 1, 1, -9223372036854775808, 1, -9223372036854775808, 4, 1, 1, 1, -9223372036854775808, 1, 1, -9223372036854775808, 1, -9223372036854775808, -9223372036854775808, 1, 1, 1, -9223372036854775808, 1, -9223372036854775808, 387420489, 1, 1, -9223372036854775808, 1, 1, 1, -9223372036854775808, 1, 1, -9223372036854775808, -9223372036854775808, -9223372036854775808, 437893890380859392, -9223372036854775808, -9223372036854775808, -9223372036854775808, 1, 1, 1, 1, -9223372036854775808, -9223372036854775808, 1, 1, -9223372036854775808, 1, 1, -9223372036854775808, 1, 1, 1, 302875106592253, -9223372036854775808, 1, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, 1, 1, -9223372036854775808, 1, 1, 1, -9223372036854775808, 302875106592253, 1, -9223372036854775808, 1, 1, -9223372036854775808, ...]
[right]: [-9223372036854775808, -9223372036854775808, 1, 1, 1, -9223372036854775808, 1, 1, -9223372036854775808, 10000000000, 1, 1, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, 1, -9223372036854775808, 1, 1, -9223372036854775808, -9223372036854775808, 3125, 1, -9223372036854775808, 1, 1, -9223372036854775808, 1, -9223372036854775808, 4, 1, 1, 1, -9223372036854775808, 1, 1, -9223372036854775808, 1, -9223372036854775808, -9223372036854775808, 1, 1, 1, -9223372036854775808, 1, -9223372036854775808, 387420489, 1, 1, -9223372036854775808, 1, 1, 1, -9223372036854775808, 1, 1, -9223372036854775808, -9223372036854775808, -9223372036854775808, 437893890380859375, -9223372036854775808, -9223372036854775808, -9223372036854775808, 1, 1, 1, 1, -9223372036854775808, -9223372036854775808, 1, 1, -9223372036854775808, 1, 1, -9223372036854775808, 1, 1, 1, 302875106592253, -9223372036854775808, 1, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, 1, 1, -9223372036854775808, 1, 1, 1, -9223372036854775808, 302875106592253, 1, -9223372036854775808, 1, 1, -9223372036854775808, ...]
----------------------------------------------------------------------
Ran 10884 tests in 932.762s
FAILED (SKIP=542, failures=4)
True
WHAT THE
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2500 entries, 0 to 2499
Data columns (total 2 columns):
int64 2500 non-null int64
category 2500 non-null category
dtypes: category(1), int64(1)
memory usage: 22.1 KB
<class 'pandas.core.frame.DataFrame'>
Int64Index: 275 entries, 0 to 2499
Data columns (total 2 columns):
int64 275 non-null int64
category 275 non-null category
dtypes: category(1), int64(1)
memory usage: 4.6 KB
zounds!
default called array('d', [1.1, 2.2, 3.3])
ext_hook called 123 b'\x9a\x99\x99\x99\x99\x99\xf1?\x9a\x99\x99\x99\x99\x99\x01@ffffff\n@'
debian/rules:100: recipe for target 'python-test3.5' failed
make[1]: *** [python-test3.5] Error 1
make[1]: Leaving directory '/build/pandas-0.18.1+git25-ga7469cf'
debian/rules:48: recipe for target 'binary' failed
make: *** [binary] Error 2
dpkg-buildpackage: error: fakeroot debian/rules binary gave error exit status 2
I: copying local configuration
```
#### output of `pd.show_versions()`
``` shell
> PYTHONPATH=lib.linux-x86_64-3.5 python3.5 -c 'import pandas; pandas.show_versions()'
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.8.0-rc5+
machine: x86_64
processor:
byteorder: little
LC_ALL: C
LANG: C
LOCALE: None.None
pandas: 0.18.1+git25-ga7469cf
nose: 1.3.7
pip: None
setuptools: 27.1.2
Cython: 0.24.1
numpy: 1.11.1rc1
scipy: 0.18.0
statsmodels: None
xarray: None
IPython: None
sphinx: 1.4.6
patsy: None
dateutil: 2.5.3
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
any clear ideas?
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MDExOlB1bGxSZXF1ZXN0ODYxNTg1MTA=
| 14,270 |
PERF: use uniqueness_check from monotonic check when possible
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| 10 | 2016-09-21T13:06:43Z | 2016-09-22T10:03:25Z | 2016-09-22T10:03:25Z |
CONTRIBUTOR
| null |
closes #14266
|
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| 178,353,221 |
MDExOlB1bGxSZXF1ZXN0ODYxNjY0NTg=
| 14,271 |
DOC: Typo fix in ordered_merge warning
|
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| 2 | 2016-09-21T13:52:06Z | 2016-09-22T16:39:19Z | 2016-09-22T16:39:03Z |
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MDU6SXNzdWUxNzgzNTc3Nzc=
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Please don't drop pandas.rpy.common.convert_robJ
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| 11 | 2016-09-21T14:08:49Z | 2017-01-16T20:17:43Z | 2017-01-16T15:23:23Z |
NONE
| null |
Currently pandas is the only well-known package supporting conversion from rpy2 object to dictionary.
There is warning message for rpy
```
warnings.warn("The pandas.rpy module is deprecated and will be "
"removed in a future version. We refer to external packages "
"like rpy2. "
"\nSee here for a guide on how to port your code to rpy2: "
"http://pandas.pydata.org/pandas-docs/stable/r_interface.html",
FutureWarning, stacklevel=2)
```
Since the user primarily use rpy2 with pandas dataframe inputs, I strongly recommend keep this conversion functionality publicly available after rpy is retired.
We will be very grateful !!
|
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MDU6SXNzdWUxNzgzOTY0MjA=
| 14,273 |
Consider extending hashtable-free indexing algorithms for large sorted indexes
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| 0 | 2016-09-21T16:22:32Z | 2019-10-21T20:03:54Z | null |
CONTRIBUTOR
| null |
See discussion in https://github.com/pydata/pandas/issues/14266.
`IndexEngine.get_loc` currently does lookups without building a hash table for large indices, as does `DatetimeEngine.__contains__`. `get_indexer`, however, still always populates a table, as does `__contains__` on non-datetime indices.
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MDU6SXNzdWUxNzg0NDYzMTk=
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bug when filling missing values with transform?
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| 16 | 2016-09-21T19:52:48Z | 2021-10-31T02:29:17Z | 2021-10-31T02:29:16Z |
NONE
| null |
Hello there,
Consider this
```
df = pd.DataFrame({'group' : ['A', 'A', 'A', 'B',
'B', 'B', 'B', 'B'],
'B' : [np.nan,np.nan,np.nan,-4,-2,5,8,7],
'C' : [-5,5,-20,0,np.nan,5,4,-4]})
df
Out[13]:
B C group
0 NaN -5.0 A
1 NaN 5.0 A
2 NaN -20.0 A
3 -4.0 0.0 B
4 -2.0 NaN B
5 5.0 5.0 B
6 8.0 4.0 B
7 7.0 -4.0 B
```
Now I want to **fill forward** the missing values in `C` for each group
```
df.groupby('group').C.fillna(method ='ffill')
Out[11]:
0 -5.0
1 5.0
2 -20.0
3 0.0
4 0.0
5 5.0
6 4.0
7 -4.0
Name: C, dtype: float64
df.groupby('group').C.transform('ffill')
Out[12]:
0 -5.0
1 -5.0
2 -5.0
3 5.0
4 5.0
5 5.0
6 5.0
7 5.0
dtype: float64
```
the transform output is wrong.
Is that expected? Pandas 18.1
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MDU6SXNzdWUxNzg0Nzk5NTM=
| 14,275 |
TST: 3.5 c-locale
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| 1 | 2016-09-21T22:40:12Z | 2016-10-03T08:52:29Z | 2016-10-02T11:56:58Z |
CONTRIBUTOR
| null |
https://travis-ci.org/pydata/pandas/jobs/161159736
xref #14114, #12337
```
======================================================================
ERROR: test_to_latex_filename (pandas.tests.formats.test_format.TestDataFrameFormatting)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/pydata/pandas/pandas/tests/formats/test_format.py", line 2825, in test_to_latex_filename
df.to_latex(path)
File "/home/travis/build/pydata/pandas/pandas/core/frame.py", line 1661, in to_latex
encoding=encoding)
File "/home/travis/build/pydata/pandas/pandas/formats/format.py", line 662, in to_latex
latex_renderer.write_result(f)
File "/home/travis/build/pydata/pandas/pandas/formats/format.py", line 906, in write_result
buf.write(' & '.join(crow))
UnicodeEncodeError: 'ascii' codec can't encode character '\xdf' in position 7: ordinal not in range(128)
----------------------------------------------------------------------
Ran 10273 tests in 375.734s
```
cc @nbonnotte
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MDU6SXNzdWUxNzg0ODA2Mjc=
| 14,276 |
TST: 2.7-compat build
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| 4 | 2016-09-21T22:44:42Z | 2016-09-25T15:30:12Z | 2016-09-25T15:30:11Z |
CONTRIBUTOR
| null |
https://travis-ci.org/pydata/pandas/jobs/161159735
xref #14208
cc @llllllllll
`np.full_like` must be newer, can you skip on older builds (this build is with 1.7.1)
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| 14,277 |
TST: remove usages of np.full_like for numpy<1.8.0 compat
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| 2 | 2016-09-22T01:20:03Z | 2016-09-22T10:02:14Z | 2016-09-22T10:02:13Z |
CONTRIBUTOR
| null |
- [x] closes ##14276
- [x] passes `git diff upstream/master | flake8 --diff`
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Inconsistent use of `_` to separate words in method names
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open
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[] | null | 1 | 2016-09-22T08:36:24Z | 2020-05-07T03:45:38Z | null |
NONE
| null |
Sometimes, method names contain underscores to separate words (`to_string`) and sometimes not (`tolist`). This catches me of regularly. Particulary annoying are the cases where the same prefix (eg. `to`) sometimes takes an underscore and sometimes not.
Is this going to change for future major versions?
Example: `dir(Index)`
- Without underscore between words, but the compound is well-established: 'argmax', 'argmin', 'argsort', 'nbytes', 'ndim', 'nlevels', 'nunique', 'putmask'
- Without underscore: 'astype', 'fillna', 'hasnans', 'isin', 'itemsize', 'searchsorted', 'sortlevel', 'tolist'
- With underscore: 'drop_duplicates', 'get_duplicates', 'get_indexer', 'get_indexer_for', 'get_indexer_non_unique', 'get_level_values', 'get_loc', 'get_slice_bound', 'get_value', 'get_values', 'groupby', 'has_duplicates', 'holds_integer', 'inferred_type', 'is_all_dates', 'is_boolean', 'is_categorical', 'is_floating', 'is_integer', 'is_lexsorted_for_tuple', 'is_mixed', 'is_monotonic', 'is_monotonic_decreasing', 'is_monotonic_increasing', 'is_numeric', 'is_object', 'is_type_compatible', 'is_unique', 'memory_usage', 'set_names', 'set_value', 'slice_indexer', 'slice_locs', 'sort_values', 'sym_diff', 'symmetric_difference', 'to_datetime', 'to_native_types', 'to_series', 'value_counts'
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MDU6SXNzdWUxNzg1Njk3MDQ=
| 14,279 |
DEPR: sortlevel
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| 1 | 2016-09-22T10:08:46Z | 2017-01-11T08:02:34Z | 2017-01-11T08:02:34Z |
CONTRIBUTOR
| null |
when `.sort` and co were changed to be `.sort_values`, `sort_index`. IIRC this was changed to make the change less disruptive. This can be directly replaced by `sort_index`.
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MDU6SXNzdWUxNzg2MDM0MTA=
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(feature request) timeout for pandas.io.json.read_json
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[] | null | 4 | 2016-09-22T12:59:59Z | 2019-02-08T05:00:49Z | 2019-02-08T05:00:49Z |
NONE
| null |
Please allow to set a `timeout` in (all) your internet requests. We have just identified a halting problem in a repeating long runner when using `read_json()`.
Example:
```
pandas.io.json.read_json(url, timeout=10.0)
```
My current workaround is to temporarily set `socket.setdefaulttimeout(10.0)` globally (default is `None`!). Which works, but if your read_json had already allowed to set it locally, I would have probably realized much earlier.
Thanks a lot for considering this. Thanks for pandas, an amazing tool.
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| 14,281 |
Problem using argsort in DataFrame
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| 3 | 2016-09-22T14:41:44Z | 2016-09-22T19:11:10Z | 2016-09-22T19:11:10Z |
NONE
| null |
```
In [90]: df
Out[90]:
A B C D E
0 0.444939 0.407554 0.460148 0.465239 0.462691
1 0.016545 0.850445 0.817744 0.777962 0.757983
2 0.934829 0.831104 0.879891 0.926879 0.721535
3 0.117642 0.145906 0.199844 0.437564 0.100702
In [91]: df2 = df.apply(lambda row: df.columns[np.argsort(row)], axis=1)
In [92]: df2
Out[92]:
```
#### Output
```
A B C D E
0 B A C E D
1 A E D C B
2 E B C D A
3 E A B C D
```
#### Expected Output
```
1 2 3 4 5
0 B A C E D
1 A E D C B
2 E B C D A
3 B C D E A
```
Last row of the dataframe has different values. But rest of the rows has same value as expected.
[http://stackoverflow.com/questions/39605512/twist-dataframe-by-rank/39610093#39610093](url)
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Is there a way to append dataframes together more memory-wised efficient?
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| 1 | 2016-09-22T17:43:26Z | 2016-09-26T17:57:06Z | 2016-09-26T17:57:06Z |
NONE
| null |
for example, i want to concat/append two dataframes into one and only use the final dataframe later on:
`
import numpy as np
import pandas as pd
from pandas.util import testing as tm
from multiprocessing.pool import ThreadPool
num_rows = 100000
def make_df(num_rows=10000):
```
df = pd.DataFrame(np.random.rand(num_rows, 5), columns=list('abcde'))
df['foo'] = 'foo'
df['bar'] = 'bar'
df['baz'] = 'baz'
df['date'] = pd.date_range('20000101 09:00:00',
periods=num_rows,
freq='s')
df['int'] = np.arange(num_rows, dtype='int64')
return df
```
df1 = make_df(num_rows=num_rows)
df2 = make_df(num_rows=num_rows)
df = pd.concat([df1,df2])
`
during the last statement execution, the memory usage got doubled due to the copy assignment.
is it possible that we have some method like df1.append_but_modify_lvalue(df2,ignore_index=True)
, thus we don't need to copy assign return value
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Series.to_csv quotechar missing or to_csv wrong behaviour
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[] | null | 6 | 2016-09-22T21:26:49Z | 2016-09-28T21:15:20Z | 2016-09-28T21:15:20Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
pd.Series(['x','x,x']).to_csv('my_file.txt', index=False)
```
The result of this code when you open the file `my_file.txt` is:
```
x
"x,x"
```
#### Expected Output
I think that either A) we decide that `Series.to_csv` doesn't quote and the expected output is
```
x
x,x
```
or B) we decide that `Series.to_csv` quotes in the same way as `DataFrame.to_csv` in which case I would expect that `Series.to_csv` can take argument `quotechar`.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-68-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.18.1
nose: 1.3.4
pip: 1.5.6
setuptools: 26.1.1
Cython: None
numpy: 1.8.2
scipy: 0.14.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.2
openpyxl: 2.3.5
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: 3.4.2
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.9.2
apiclient: 1.5.3
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.34.0
pandas_datareader: None
```
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DOC: Expand reference doc for read_json
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[] | null | 7 | 2016-09-23T01:41:01Z | 2016-10-17T17:08:39Z | 2016-10-17T17:08:39Z |
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] expanded reference document for `pandas.read_json()`, especially concentrating on the `orient` parameter. Also added some example usage code and explicitly mention `to_json()` as a source of valid JSON strings.
<div class="section" id="pandas-read-json">
<h1>pandas.read_json<a class="headerlink" href="#pandas-read-json" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="pandas.read_json">
<code class="descclassname">pandas.</code><code class="descname">read_json</code><span class="sig-paren">(</span><em>path_or_buf=None</em>, <em>orient=None</em>, <em>typ='frame'</em>, <em>dtype=True</em>, <em>convert_axes=True</em>, <em>convert_dates=True</em>, <em>keep_default_dates=True</em>, <em>numpy=False</em>, <em>precise_float=False</em>, <em>date_unit=None</em>, <em>encoding=None</em>, <em>lines=False</em><span class="sig-paren">)</span><a class="reference external" href="http://github.com/pydata/pandas/blob/master/pandas/io/json.py#L112-L272"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pandas.read_json" title="Permalink to this definition">¶</a></dt>
<dd><p>Convert a JSON string to pandas object</p>
<table class="docutils field-list" frame="void" rules="none">
<colgroup><col class="field-name">
<col class="field-body">
</colgroup><tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>path_or_buf</strong> : a valid JSON string or file-like, default: None</p>
<blockquote>
<div><p>The string could be a URL. Valid URL schemes include http, ftp, s3, and
file. For file URLs, a host is expected. For instance, a local file
could be <code class="docutils literal"><span class="pre">file://localhost/path/to/table.json</span></code></p>
</div></blockquote>
<p><strong>orient</strong> : string, indicating the expected format of the JSON input.</p>
<blockquote>
<div><p>The set of allowed orients changes depending on the value
of the <code class="docutils literal"><span class="pre">typ</span></code> parameter.</p>
<ul class="simple">
<li>when <code class="docutils literal"><span class="pre">typ</span> <span class="pre">==</span> <span class="pre">'series'</span></code>,<ul>
<li>allowed orients are <code class="docutils literal"><span class="pre">{'split','records','index'}</span></code></li>
<li>default is <code class="docutils literal"><span class="pre">'index'</span></code></li>
<li>The Series index must be unique for orient <code class="docutils literal"><span class="pre">'index'</span></code>.</li>
</ul>
</li>
<li>when <code class="docutils literal"><span class="pre">typ</span> <span class="pre">==</span> <span class="pre">'frame'</span></code>,<ul>
<li>allowed orients are <code class="docutils literal"><span class="pre">{'split','records','index',</span>
<span class="pre">'columns','values'}</span></code></li>
<li>default is <code class="docutils literal"><span class="pre">'columns'</span></code></li>
<li>The DataFrame index must be unique for orients ‘index’ and
‘columns’.</li>
<li>The DataFrame columns must be unique for orients ‘index’,
‘columns’, and ‘records’.</li>
</ul>
</li>
</ul>
<p>The value of <code class="docutils literal"><span class="pre">orient</span></code> specifies the expected format of the
JSON string. The expected JSON formats are compatible with the
strings produced by <code class="docutils literal"><span class="pre">to_json()</span></code> with a corresponding value
of <code class="docutils literal"><span class="pre">orient</span></code>.</p>
<blockquote>
<div><ul class="simple">
<li><code class="docutils literal"><span class="pre">'split'</span></code> : dict like
<code class="docutils literal"><span class="pre">{index</span> <span class="pre">-></span> <span class="pre">[index],</span> <span class="pre">columns</span> <span class="pre">-></span> <span class="pre">[columns],</span> <span class="pre">data</span> <span class="pre">-></span> <span class="pre">[values]}</span></code></li>
<li><code class="docutils literal"><span class="pre">'records'</span></code> : list like
<code class="docutils literal"><span class="pre">[{column</span> <span class="pre">-></span> <span class="pre">value},</span> <span class="pre">...</span> <span class="pre">,</span> <span class="pre">{column</span> <span class="pre">-></span> <span class="pre">value}]</span></code></li>
<li><code class="docutils literal"><span class="pre">'index'</span></code> : dict like <code class="docutils literal"><span class="pre">{index</span> <span class="pre">-></span> <span class="pre">{column</span> <span class="pre">-></span> <span class="pre">value}}</span></code></li>
<li><code class="docutils literal"><span class="pre">'columns'</span></code> : dict like <code class="docutils literal"><span class="pre">{column</span> <span class="pre">-></span> <span class="pre">{index</span> <span class="pre">-></span> <span class="pre">value}}</span></code></li>
<li><code class="docutils literal"><span class="pre">'values'</span></code> : just the values array</li>
</ul>
</div></blockquote>
</div></blockquote>
<p><strong>typ</strong> : type of object to recover (series or frame), default ‘frame’</p>
<p><strong>dtype</strong> : boolean or dict, default True</p>
<blockquote>
<div><p>If True, infer dtypes, if a dict of column to dtype, then use those,
if False, then don’t infer dtypes at all, applies only to the data.</p>
</div></blockquote>
<p><strong>convert_axes</strong> : boolean, default True</p>
<blockquote>
<div><p>Try to convert the axes to the proper dtypes.</p>
</div></blockquote>
<p><strong>convert_dates</strong> : boolean, default True</p>
<blockquote>
<div><p>List of columns to parse for dates; If True, then try to parse
datelike columns default is True; a column label is datelike if</p>
<ul class="simple">
<li>it ends with <code class="docutils literal"><span class="pre">'_at'</span></code>,</li>
<li>it ends with <code class="docutils literal"><span class="pre">'_time'</span></code>,</li>
<li>it begins with <code class="docutils literal"><span class="pre">'timestamp'</span></code>,</li>
<li>it is <code class="docutils literal"><span class="pre">'modified'</span></code>, or</li>
<li>it is <code class="docutils literal"><span class="pre">'date'</span></code></li>
</ul>
</div></blockquote>
<p><strong>keep_default_dates</strong> : boolean, default True</p>
<blockquote>
<div><p>If parsing dates, then parse the default datelike columns</p>
</div></blockquote>
<p><strong>numpy</strong> : boolean, default False</p>
<blockquote>
<div><p>Direct decoding to numpy arrays. Supports numeric data only, but
non-numeric column and index labels are supported. Note also that the
JSON ordering MUST be the same for each term if numpy=True.</p>
</div></blockquote>
<p><strong>precise_float</strong> : boolean, default False</p>
<blockquote>
<div><p>Set to enable usage of higher precision (strtod) function when
decoding string to double values. Default (False) is to use fast but
less precise builtin functionality</p>
</div></blockquote>
<p><strong>date_unit</strong> : string, default None</p>
<blockquote>
<div><p>The timestamp unit to detect if converting dates. The default behaviour
is to try and detect the correct precision, but if this is not desired
then pass one of ‘s’, ‘ms’, ‘us’ or ‘ns’ to force parsing only seconds,
milliseconds, microseconds or nanoseconds respectively.</p>
</div></blockquote>
<p><strong>lines</strong> : boolean, default False</p>
<blockquote>
<div><p>Read the file as a json object per line.</p>
<div class="versionadded">
<p><span class="versionmodified">New in version 0.19.0.</span></p>
</div>
</div></blockquote>
<p><strong>encoding</strong> : str, default is ‘utf-8’</p>
<blockquote>
<div><p>The encoding to use to decode py3 bytes.</p>
<div class="versionadded">
<p><span class="versionmodified">New in version 0.19.0.</span></p>
</div>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><strong>result</strong> : Series or DataFrame, depending on the value of <code class="docutils literal"><span class="pre">typ</span></code>.</p>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">],</span> <span class="p">[</span><span class="s1">'c'</span><span class="p">,</span> <span class="s1">'d'</span><span class="p">]],</span>
<span class="go"> index=['row 1', 'row 2'],</span>
<span class="go"> columns=['col 1', 'col 2'])</span>
<span class="gp">>>> </span><span class="nb">print</span> <span class="n">df</span>
<span class="go"> col 1 col 2</span>
<span class="go">row 1 a b</span>
<span class="go">row 2 c d</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">orient</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'split'</span><span class="p">,</span> <span class="s1">'records'</span><span class="p">,</span> <span class="s1">'index'</span><span class="p">]:</span>
<span class="go"> str = df.to_json(orient=orient)</span>
<span class="go"> print "'{}': '{}'".format(orient, str)</span>
<span class="go"> pd.read_json(str, orient=orient)</span>
<span class="go">'split':</span>
<span class="go">'{"columns":["col 1","col 2"],"index":["row 1","row 2"],"data":[["a","b"],</span>
<span class="go">["c","d"]]}'</span>
<span class="go">'records':</span>
<span class="go">'[{"col 1":"a","col 2":"b"},{"col 1":"c","col 2":"d"}]'</span>
<span class="go">'index':</span>
<span class="go">'{"row 1":{"col 1":"a","col 2":"b"},"row 2":{"col 1":"c","col 2":"d"}}'</span>
</pre></div>
</div>
</dd></dl>
</div>
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MDU6SXNzdWUxNzg3OTc4MDc=
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Support reading random rows in `read_csv`
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| 1 | 2016-09-23T06:45:37Z | 2016-09-23T12:02:15Z | 2016-09-23T12:02:15Z |
NONE
| null |
It is very common to read random rows in a large csv file, typically for testing with a small dataset, or fit the limit of memory. The parameter `nrows` is used for read the first n lines, but I didn't find any feature to read random lines. Such parameter might be named `keeprows` (opposite to `skiprows`), which supports:
- int, e.g. `keeprows=100` means keep 100 random lines (uniformly)
- float in (0, 1), e.g. `keeprows=0.05` means keep 5% of total lines
- list of int(or iterable), e.g. `keeprows=[1, 3, 8]` mean to keep line 1, 3, and 8
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BUG: column formatting with non-standard objects
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| 6 | 2016-09-23T11:21:05Z | 2020-01-20T07:47:42Z | null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```
frame = pd.DataFrame({('test', 'it'): [42], ('yet', 'again'): [43], ('and', 'more'): [44]})
frame.groupby([('and', 'more'), ('test', 'it')]).mean().unstack()
```
results in:
```
TypeError Traceback (most recent call last)
/homes/jwienke/.local/lib/python3.4/site-packages/IPython/core/formatters.py in __call__(self, obj)
670 type_pprinters=self.type_printers,
671 deferred_pprinters=self.deferred_printers)
--> 672 printer.pretty(obj)
673 printer.flush()
674 return stream.getvalue()
/homes/jwienke/.local/lib/python3.4/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()
/homes/jwienke/.local/lib/python3.4/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, '<')
/homes/jwienke/.local/lib/python3.4/site-packages/IPython/lib/pretty.py in _repr_pprint(obj, p, cycle)
699 """A pprint that just redirects to the normal repr function."""
700 # Find newlines and replace them with p.break_()
--> 701 output = repr(obj)
702 for idx,output_line in enumerate(output.splitlines()):
703 if idx:
/usr/lib/python3/dist-packages/pandas/core/base.py in __repr__(self)
53 Yields Bytestring in Py2, Unicode String in py3.
54 """
---> 55 return str(self)
56
57
/usr/lib/python3/dist-packages/pandas/core/base.py in __str__(self)
32
33 if compat.PY3:
---> 34 return self.__unicode__()
35 return self.__bytes__()
36
/usr/lib/python3/dist-packages/pandas/core/frame.py in __unicode__(self)
458 width = None
459 self.to_string(buf=buf, max_rows=max_rows, max_cols=max_cols,
--> 460 line_width=width, show_dimensions=show_dimensions)
461
462 return buf.getvalue()
/usr/lib/python3/dist-packages/pandas/core/frame.py in to_string(self, buf, columns, col_space, colSpace, header, index, na_rep, formatters, float_format, sparsify, nanRep, index_names, justify, force_unicode, line_width, max_rows, max_cols, show_dimensions)
1297 max_cols=max_cols,
1298 show_dimensions=show_dimensions)
-> 1299 formatter.to_string()
1300
1301 if buf is None:
/usr/lib/python3/dist-packages/pandas/core/format.py in to_string(self, force_unicode)
378 text = info_line
379 else:
--> 380 strcols = self._to_str_columns()
381 if self.line_width is None:
382 text = adjoin(1, *strcols)
/usr/lib/python3/dist-packages/pandas/core/format.py in _to_str_columns(self)
310
311 # may include levels names also
--> 312 str_index = self._get_formatted_index()
313 str_columns = self._get_formatted_column_labels()
314
/usr/lib/python3/dist-packages/pandas/core/format.py in _get_formatted_index(self)
589 # empty space for columns
590 if show_col_names:
--> 591 col_header = ['%s' % x for x in self._get_column_name_list()]
592 else:
593 col_header = [''] * columns.nlevels
/usr/lib/python3/dist-packages/pandas/core/format.py in <listcomp>(.0)
589 # empty space for columns
590 if show_col_names:
--> 591 col_header = ['%s' % x for x in self._get_column_name_list()]
592 else:
593 col_header = [''] * columns.nlevels
TypeError: not all arguments converted during string formatting
```
#### Expected Output
No exception, just and unstacked data frame where names are somehow generated.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.18.1-tf1-ll
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.13.1
Cython: None
numpy: 1.8.2
scipy: 0.13.3
statsmodels: None
IPython: 5.1.0
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 2.0
pytz: 2012c
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
sqlalchemy: None
lxml: None
bs4: None
html5lib: 0.999
bq: None
apiclient: None
```
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MDU6SXNzdWUxNzg4NjAwNzM=
| 14,287 |
TestTimedeltaIndexOps
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closed
| false | null |
[] | null | 1 | 2016-09-23T12:07:06Z | 2016-09-23T12:07:24Z | 2016-09-23T12:07:11Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
#### Expected Output
#### output of `pd.show_versions()`
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MDU6SXNzdWUxNzg4NjEwMjU=
| 14,288 |
multiple tests errors and failures while building 0.18.1+git25-ga7469cf on i386 (ok on amd64)
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| 2 | 2016-09-23T12:12:08Z | 2016-09-23T12:23:05Z | 2016-09-23T12:22:24Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
``` shell
% grep -2 '^\(FAIL\|ERROR\|[^ ]*Error:\)' pandas_0.18.1+git25-ga7469cf-1~nd90+1_i386.build
======================================================================
ERROR: test_groupby (pandas.tests.test_categorical.TestCategoricalAsBlock)
----------------------------------------------------------------------
Traceback (most recent call last):
--
File "/build/pandas-0.18.1+git25-ga7469cf/debian/tmp/usr/lib/python2.7/dist-packages/pandas/core/groupby.py", line 4433, in _groupby_indices
counts = np.bincount(codes[mask], minlength=values.categories.size)
TypeError: Cannot cast array data from dtype('int64') to dtype('int32') according to the rule 'safe'
======================================================================
ERROR: test_get_group_empty_bins (pandas.tests.test_groupby.TestGroupBy)
----------------------------------------------------------------------
Traceback (most recent call last):
--
File "/build/pandas-0.18.1+git25-ga7469cf/debian/tmp/usr/lib/python2.7/dist-packages/pandas/core/groupby.py", line 4433, in _groupby_indices
counts = np.bincount(codes[mask], minlength=values.categories.size)
TypeError: Cannot cast array data from dtype('int64') to dtype('int32') according to the rule 'safe'
======================================================================
FAIL: test_nat (pandas.tseries.tests.test_base.TestDatetimeIndexOps)
----------------------------------------------------------------------
Traceback (most recent call last):
--
File "/build/pandas-0.18.1+git25-ga7469cf/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 1018, in raise_assert_detail
raise AssertionError(msg)
AssertionError: numpy array are different
Attribute "dtype" are different
--
======================================================================
FAIL: test_nat (pandas.tseries.tests.test_base.TestPeriodIndexOps)
----------------------------------------------------------------------
Traceback (most recent call last):
--
File "/build/pandas-0.18.1+git25-ga7469cf/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 1018, in raise_assert_detail
raise AssertionError(msg)
AssertionError: numpy array are different
Attribute "dtype" are different
--
======================================================================
FAIL: test_nat (pandas.tseries.tests.test_base.TestTimedeltaIndexOps)
----------------------------------------------------------------------
Traceback (most recent call last):
--
File "/build/pandas-0.18.1+git25-ga7469cf/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 1018, in raise_assert_detail
raise AssertionError(msg)
AssertionError: numpy array are different
Attribute "dtype" are different
--
Ran 10942 tests in 1071.084s
FAILED (SKIP=284, errors=2, failures=3)
True
WHAT THE
```
#### output of `pd.show_versions()`
all the versions and more info is in the full build log: http://neuro.debian.net/_files/_buildlogs/pandas/0.18.1+git25-ga7469cf/pandas_0.18.1+git25-ga7469cf-1_amd64.build but altogether versions are close/the same as in https://github.com/pydata/pandas/issues/14269#issue-178334362
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test_panel4d: on ubuntu 14.04 (amd64 only) -- AssertionError: Did not see expected warning of class 'FutureWarning'
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closed
| false | null |
[] | null | 2 | 2016-09-23T12:16:16Z | 2019-03-10T16:27:41Z | 2019-03-10T16:27:41Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```
% grep -2 '^\(FAIL\|ERROR\|[^ ]*Error:\)' pandas_0.18.1+git25-ga7469cf-1~nd14.04+1_amd64.build | head -20
======================================================================
FAIL: test_table_mixed_dtypes (pandas.io.tests.test_pytables.TestHDFStore)
----------------------------------------------------------------------
Traceback (most recent call last):
--
File "/build/pandas-0.18.1+git25-ga7469cf/debian/tmp/usr/lib/python3/dist-packages/pandas/util/testing.py", line 2536, in assert_produces_warning
% expected_warning.__name__)
AssertionError: Did not see expected warning of class 'FutureWarning'.
======================================================================
FAIL: test_panel4d_concat (pandas.tools.tests.test_concat.TestConcatenate)
----------------------------------------------------------------------
Traceback (most recent call last):
--
File "/build/pandas-0.18.1+git25-ga7469cf/debian/tmp/usr/lib/python3/dist-packages/pandas/util/testing.py", line 2536, in assert_produces_warning
% expected_warning.__name__)
AssertionError: Did not see expected warning of class 'FutureWarning'.
======================================================================
```
full buildlog with all the versions etc at http://neuro.debian.net/_files/_buildlogs/pandas/0.18.1+git25-ga7469cf/pandas_0.18.1+git25-ga7469cf-1~nd14.04+1_amd64.build
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BUG: set_levels set illegal levels.
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closed
| false | null |
[] | null | 0 | 2016-09-23T13:20:23Z | 2016-09-23T13:37:31Z | 2016-09-23T13:37:31Z |
CONTRIBUTOR
| null |
- [x] closes #xxxx
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
`MultiIndex.set_levels`, when passed in illegal level values, raises an error.
When `inplace=True`, though, the illegal level values are still accepted. This
commit fixes that behavior by checking that the proposed level values are legal
before setting them.
Duplicate of https://github.com/pydata/pandas/pull/14236, which kept failing the build with Qt errors. This is a retry with a fresh Travis build. Assuming it passes, I'll close https://github.com/pydata/pandas/pull/14236 in favor of this PR.
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| 178,886,132 |
MDU6SXNzdWUxNzg4ODYxMzI=
| 14,291 |
BUG: DataFrame.insert with allow_duplicates=True fails when already duplicates present
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| 3 | 2016-09-23T14:04:17Z | 2016-10-24T22:51:29Z | 2016-10-24T22:51:28Z |
NONE
| null |
upon DataFrame.insert option allow_duplicates works, but only only once.
When i have 2 columns with same name, additon of third throws
> ValueError: Wrong number of items passed 2, placement implies 1
#### Code Sample, a copy-pastable example if possible
```
a = pd.DataFrame()
a.insert(0, "qwe", [1,2,3,4], allow_duplicates=True)
a.insert(0, "qwe", [1,2,3,4], allow_duplicates=True)
a.insert(0, "qwe", [1,2,3,4], allow_duplicates=True)
```
#### Expected Output
zxc qwe qwe
0 1 1 1
1 2 2 2
2 3 3 3
3 4 4 4
#### output of `pd.show_versions()`
<details>
## 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 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: ru_RU
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: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.3
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: 0.7.6.None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
<\details>
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BUG: Fix concat key name
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| 4 | 2016-09-23T19:44:46Z | 2016-10-09T17:38:30Z | 2016-10-09T17:38:05Z |
CONTRIBUTOR
| null |
- [x] closes #14252
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Fixes a bug where `pd.concat` didn't propagate the names of keys used to create
a hierarchical index.
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| 178,996,443 |
MDU6SXNzdWUxNzg5OTY0NDM=
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Performance of pandas.algos.groupby_int64
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| 11 | 2016-09-23T23:55:38Z | 2016-09-27T22:10:32Z | 2016-09-27T22:10:32Z |
CONTRIBUTOR
| null |
For dask.dataframe shuffle operations (groupby.apply, merge), when running with multiple threads per process, I sometimes find my computations dominated by `pandas.algos.groupby_int64`. Looking at the source code for this it looks like it's using dynamic pure python objects from Cython. I'm curious if there are ways to accelerate this function, particularly in multi-threaded situations (releasing the GIL).
One solution that comes to mind would be to do a single pass over `labels`, pre-compute the length of each `members` list in `results` and then pre-allocate these as arrays. This might allow better GIL-releasing behavior.
Thoughts?
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PERF: faster grouping
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| 8 | 2016-09-24T13:53:05Z | 2016-09-27T22:10:32Z | 2016-09-27T22:10:32Z |
CONTRIBUTOR
| null |
closes #14293
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API: add dtype= option to python parser
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| 8 | 2016-09-24T18:12:19Z | 2016-11-26T09:14:54Z | 2016-11-26T09:12:22Z |
CONTRIBUTOR
| null |
- [x] part of #12686
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Ultimately I'm working towards #8212 (types in excel parser), which should be pretty straightforward after this.
Right now the tests are moved from `c_parser_only.py`, may need to add some more
cc @gfyoung
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I would like to write a hardcore groupby tutorial
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[] | null | 4 | 2016-09-24T21:08:29Z | 2016-09-25T12:28:12Z | 2016-09-25T00:32:29Z |
NONE
| null |
Hello
After some thoughts, I think I would be useful for everybody if I wrote some nasty, hardcore groupby tutorial with all the crazy data manipulations I use in my code.
I work in a large company, have a phd in finance/economics and many times I pushed Pandas to the limits with 80GB datasets and others.
To do the best tutorial possible, I would be nice if someone from the community could review my tutorial and possibly improve it a bit. @TomAugspurger would you like to have a look?
Objective: making pandas better!
Thanks guys!
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resample interpolate gives unexpected results in 0.18.1
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| 8 | 2016-09-25T13:03:43Z | 2021-05-01T23:51:59Z | null |
NONE
| null |
#### A small, complete example of the issue
``` python
a = pd.Series([1.,3.,4.,3.,5.,6.,7.,8.], ['2016-05-25 00:00:35','2016-05-25 00:00:50','2016-05-25 00:01:05','2016-05-25 00:01:35','2016-05-25 00:02:05','2016-05-25 00:03:00','2016-05-25 00:04:00','2016-05-25 00:06:00'])
In [79]: a
Out[79]:
2016-05-25 00:00:35 1.0
2016-05-25 00:00:50 3.0
2016-05-25 00:01:05 4.0
2016-05-25 00:01:35 3.0
2016-05-25 00:02:05 5.0
2016-05-25 00:03:00 6.0
2016-05-25 00:04:00 7.0
2016-05-25 00:06:00 8.0
dtype: float64
In [80]: a.index = pd.to_datetime(a.index)
In [81]: a.resample('15S', base=5).interpolate()
```
#### Expected Output
I expect that I would get valid values, based on the input at 2:05 and later. It appears that the data data after 2:05 is ignored.
#### Output of `pd.show_versions()`
In [146]: pd.show_versions()
## INSTALLED 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: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.2
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: 3.6.0
bs4: 4.4.1
html5lib: 1.0b3
httplib2: 0.9.2
apiclient: 1.5.0
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
<details>
# Paste the output here
Out[81]:
2016-05-25 00:00:35 1.0
2016-05-25 00:00:50 3.0
2016-05-25 00:01:05 4.0
2016-05-25 00:01:20 3.5
2016-05-25 00:01:35 3.0
2016-05-25 00:01:50 4.0
2016-05-25 00:02:05 5.0
2016-05-25 00:02:20 5.0
2016-05-25 00:02:35 5.0
2016-05-25 00:02:50 5.0
2016-05-25 00:03:05 5.0
2016-05-25 00:03:20 5.0
2016-05-25 00:03:35 5.0
2016-05-25 00:03:50 5.0
2016-05-25 00:04:05 5.0
2016-05-25 00:04:20 5.0
2016-05-25 00:04:35 5.0
2016-05-25 00:04:50 5.0
2016-05-25 00:05:05 5.0
2016-05-25 00:05:20 5.0
2016-05-25 00:05:35 5.0
2016-05-25 00:05:50 5.0
Freq: 15S, dtype: float64
I was told that this error does not show up in 0.18.0, but I have not confirmed that.
This comes from my attempts to interpolate some irregular data as shown in this question: https://stackoverflow.com/questions/39599192/fill-in-time-data-in-pandas
</details>
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.info() fails with CategoricalIndex in the columns
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| 1 | 2016-09-25T13:16:59Z | 2016-11-03T11:12:19Z | 2016-11-03T11:12:19Z |
CONTRIBUTOR
| null |
Just in the columns, the index doesn't cause any issues. This came up as the output of `pd.get_dummies` can have a CategoricalIndex in the columns.
#### A small, complete example of the issue
``` python
In [31]: idx = pd.CategoricalIndex(['a', 'b'])
In [32]: df = pd.DataFrame(np.zeros((2, 2)), index=idx, columns=idx)
In [33]: df.info()
```
``` pytb
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-33-857958930b27> in <module>()
----> 1 df.info()
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas-0.19.0rc1+21.ge596cbf-py3.5-macosx-10.11-x86_64.egg/pandas/core/frame.py in info(self, verbose, buf, max_cols, memory_usage, null_counts)
1790 if 'object' in counts or is_object_dtype(self.index):
1791 size_qualifier = '+'
-> 1792 mem_usage = self.memory_usage(index=True, deep=deep).sum()
1793 lines.append("memory usage: %s\n" %
1794 _sizeof_fmt(mem_usage, size_qualifier))
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas-0.19.0rc1+21.ge596cbf-py3.5-macosx-10.11-x86_64.egg/pandas/core/frame.py in memory_usage(self, index, deep)
1827 if index:
1828 result = Series(self.index.memory_usage(deep=deep),
-> 1829 index=['Index']).append(result)
1830 return result
1831
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas-0.19.0rc1+21.ge596cbf-py3.5-macosx-10.11-x86_64.egg/pandas/core/series.py in append(self, to_append, ignore_index, verify_integrity)
1593 to_concat = [self, to_append]
1594 return concat(to_concat, ignore_index=ignore_index,
-> 1595 verify_integrity=verify_integrity)
1596
1597 def _binop(self, other, func, level=None, fill_value=None):
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas-0.19.0rc1+21.ge596cbf-py3.5-macosx-10.11-x86_64.egg/pandas/tools/merge.py in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)
1323 keys=keys, levels=levels, names=names,
1324 verify_integrity=verify_integrity,
-> 1325 copy=copy)
1326 return op.get_result()
1327
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas-0.19.0rc1+21.ge596cbf-py3.5-macosx-10.11-x86_64.egg/pandas/tools/merge.py in __init__(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy)
1462 self.copy = copy
1463
-> 1464 self.new_axes = self._get_new_axes()
1465
1466 def get_result(self):
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas-0.19.0rc1+21.ge596cbf-py3.5-macosx-10.11-x86_64.egg/pandas/tools/merge.py in _get_new_axes(self)
1550 new_axes[i] = ax
1551
-> 1552 new_axes[self.axis] = self._get_concat_axis()
1553 return new_axes
1554
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas-0.19.0rc1+21.ge596cbf-py3.5-macosx-10.11-x86_64.egg/pandas/tools/merge.py in _get_concat_axis(self)
1604
1605 if self.keys is None:
-> 1606 concat_axis = _concat_indexes(indexes)
1607 else:
1608 concat_axis = _make_concat_multiindex(indexes, self.keys,
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas-0.19.0rc1+21.ge596cbf-py3.5-macosx-10.11-x86_64.egg/pandas/tools/merge.py in _concat_indexes(indexes)
1622
1623 def _concat_indexes(indexes):
-> 1624 return indexes[0].append(indexes[1:])
1625
1626
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas-0.19.0rc1+21.ge596cbf-py3.5-macosx-10.11-x86_64.egg/pandas/indexes/base.py in append(self, other)
1425 # if any of the to_concat is category
1426 from pandas.indexes.category import CategoricalIndex
-> 1427 return CategoricalIndex._append_same_dtype(self, to_concat, name)
1428
1429 if len(typs) == 1:
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas-0.19.0rc1+21.ge596cbf-py3.5-macosx-10.11-x86_64.egg/pandas/indexes/category.py in _append_same_dtype(self, to_concat, name)
578 ValueError if other is not in the categories
579 """
--> 580 to_concat = [self._is_dtype_compat(c) for c in to_concat]
581 codes = np.concatenate([c.codes for c in to_concat])
582 result = self._create_from_codes(codes, name=name)
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas-0.19.0rc1+21.ge596cbf-py3.5-macosx-10.11-x86_64.egg/pandas/indexes/category.py in <listcomp>(.0)
578 ValueError if other is not in the categories
579 """
--> 580 to_concat = [self._is_dtype_compat(c) for c in to_concat]
581 codes = np.concatenate([c.codes for c in to_concat])
582 result = self._create_from_codes(codes, name=name)
AttributeError: 'Index' object has no attribute '_is_dtype_compat'
```
#### Expected Output
`df.info()`
#### Output of `pd.show_versions()`
<details>
In [34]: 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: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0rc1+21.ge596cbf
nose: 1.3.7
pip: 8.1.2
setuptools: 26.1.1
Cython: 0.25a0
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.8.0rc1
xarray: 0.7.2
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: 1.4.1
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.2
openpyxl: 2.3.5
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: None
lxml: 3.4.4
bs4: 4.4.1
html5lib: 0.9999999
httplib2: 0.9.2
apiclient: 1.5.1
sqlalchemy: 1.0.12
pymysql: 0.7.6.None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
</details>
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| 179,147,243 |
MDU6SXNzdWUxNzkxNDcyNDM=
| 14,299 |
pandas.floor('H') does not work with DST
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| 3 | 2016-09-26T06:15:15Z | 2018-07-29T07:31:46Z | 2018-07-29T07:31:46Z |
CONTRIBUTOR
| null |
Using floor on a DatetimeIndex that has a timezone with DST fails when converting the result back to the tz as there may be ambiguous time (see example below).
``` python
# create 15' date range with Brussels tz
idx = pandas.date_range("2016-01-01","2017-01-01",freq="15T", tz="Europe/Brussels")
# transform the index to get rid of minutes
idx_h = idx.floor("H")
```
#### Expected Output
idx_h rounded to the hour
#### Actual Output
excepection raised when relocalizing the index after applying the rouding
``` python
Traceback (most recent call last):
File "...", line 118, in <module>
idx_h = idx.floor("H")
File "...\lib\site-packages\pandas\tseries\base.py", line 98, in floor
return self._round(freq, np.floor)
File "...\lib\site-packages\pandas\tseries\base.py", line 89, in _round
result = result.tz_localize(self.tz)#, ambiguous=[d.dst() for d in self])
File "...\lib\site-packages\pandas\util\decorators.py", line 91, in wrapper
return func(*args, **kwargs)
File "...\lib\site-packages\pandas\tseries\index.py", line 1857, in tz_localize
ambiguous=ambiguous)
File "pandas\tslib.pyx", line 4087, in pandas.tslib.tz_localize_to_utc (pandas\tslib.c:69556)
pytz.exceptions.AmbiguousTimeError: Cannot infer dst time from Timestamp('2016-10-30 02:00:00'), try using the 'ambiguous' argument
```
#### Possible solution
The code to reconvert to local tz need to have 'ambiguous' defined and can use the original array of dst flags.
Lines 87-89 in ...\Lib\site-packages\pandas\tseries\base.py
``` python
# reconvert to local tz
if getattr(self, 'tz', None) is not None:
result = result.tz_localize(self.tz)
```
to be replaced by
Lines 87-89 in ...\Lib\site-packages\pandas\tseries\base.py
``` python
# reconvert to local tz
if getattr(self, 'tz', None) is not None:
result = result.tz_localize(self.tz, ambiguous=[d.dst() for d in self])
```
Would it be also useful to have a DatetimeIndex.dst function to return a boolean array reuseable in tz_localize ?
#### Output of `pd.show_versions()`
<details>
# Paste the output here
## INSTALLED VERSIONS
commit: None
python: 2.7.11.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: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.1
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: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
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MDU6SXNzdWUxNzkxODk3NTA=
| 14,300 |
BUG: DataFrame.align() should throw a ValueError when trying to align a DataFrame with a Series and specifying axis=1
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[] | null | 4 | 2016-09-26T10:16:34Z | 2016-09-26T22:51:49Z | 2016-09-26T22:51:30Z |
NONE
| null |
#### A small, complete example of the issue
```
import pandas as pd
import numpy as np
N = 1000
L = np.random.randn(N)
F = pd.DataFrame({'X':L})
S = pd.Series(L)
X = F.align(S,axis=1)
```
This code should throw `ValueError: No axis named 1 for object type <class 'pandas.core.series.Series'>` just like `S.align(S,axis=1)` would, instead it goes through and creates a huge NxN-sized dataframe filled with nothing but `NaN`. For bigger values of `N` this behaviour leads to `MemoryError`.
#### Output of `pd.show_versions()`
<details>
python: 2.7.12.final.0
pandas: 0.18.1
</details>
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Greedy date inference in read_csv leads to inconsistent data
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| 2 | 2016-09-26T16:30:41Z | 2016-09-26T17:44:41Z | 2016-09-26T17:44:22Z |
NONE
| null |
#### A small, complete example of the issue
``` python
import pandas as pd
df = pd.read_csv('greedy.txt', parse_dates=['EuroDate'])
print(df)
```
with "greedy.txt" as
```
EuroDate
10/9/2016
30/9/2016
```
#### Expected Output
```
EuroDate
0 2016-09-10
1 2016-09-30
```
#### Actual Output
```
EuroDate
0 2016-10-09
1 2016-09-30
```
[greedy.txt](https://github.com/pydata/pandas/files/493576/greedy.txt)
So `read_csv()` has interpreted the first line as a US-format date, then realised that the second line cannot be a US-formatted date, so switched to European format. But it has _not_ gone back and reevaluated the first line in light of its new information. So the resulting data is inconsistent, and pandas knows this.
Obviously, I appreciate that
- CSV files are a disaster
- This code is _asking_ Pandas to infer the dates
- Going back and re-evaluating previous data in light of new information is slow and annoying.
- It won't always be possible to do anything except interpret a field as a string if there is inconsistent data.
- Some datasets will include dates in multiple formats (e.g. if humans have entered them free-form) and in those cases is might just be useful for Pandas to take its best guess on a row-by-row basis.
However, I contend that in this case the behaviour is _incorrect_ (because there is a consistent interpretation of the column as a date, which is in fact clear by the second record). Even if some people don't regard this as a bug, I contend that it is at the very least dangerous and likely to cause serious (and sometimes baffling) errors. In my view, it would be much better to go back and reinterpret the data according to the information now available or to fail. If even this is considered too much, at the very least Pandas should issue a prominent warning that it has interpreted different rows in the column using different date formats.
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 19.4
Cython: 0.24
numpy: 1.11.1
scipy: None
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: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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Index.copy() ignores name/names arguments
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| 1 | 2016-09-26T17:16:14Z | 2016-09-27T23:44:16Z | 2016-09-27T23:44:16Z |
NONE
| null |
Passing in a name or names arguments to Index.copy doesn't set the name on the resulting index. This fails to work as the docs indicate on both Index and MultiIndex.
``` python
import pandas as pd
index = pd.Index([1,2], name='MyName')
print index.name
cloned_index_1 = index.copy(name='NewName')
print cloned_index_1.name
cloned_index_2 = index.copy(names='NewName')
print cloned_index_2.name
cloned_index_3 = index.copy(names=['NewName'])
print cloned_index_3.name
multi_index = pd.Index([(1, 2), (3, 4)], names=['MyName1', 'MyName2'])
print multi_index.names
multi_index_1 = multi_index.copy(names=['NewName1', 'NewName2'])
print multi_index_1.names
```
```
MyName
MyName
MyName
MyName
[u'MyName1', u'MyName2']
[u'MyName1', u'MyName2']
```
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BUG: Index.copy() honors 'name' parameter (#14302)
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| 5 | 2016-09-26T18:11:42Z | 2016-09-27T23:44:23Z | 2016-09-27T23:44:16Z |
CONTRIBUTOR
| null |
- [x] closes #14302
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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MultiIndex slicing with .loc returns wrong results
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| 1 | 2016-09-27T04:48:53Z | 2016-09-27T10:49:41Z | 2016-09-27T10:49:26Z |
CONTRIBUTOR
| null |
#### A small, complete example of the issue
``` python
import pandas
idx = pandas.date_range("2012", "2016", freq="D")
df = pandas.concat([
pandas.DataFrame(1, index=idx, columns=[ctry]) for ctry in ["BE", "FR", "SP"]
],
axis=1).stack().to_frame()
df.index = df.index.swaplevel(0, 1)
df = df.sortlevel()
df.info()
print()
df_ok = df.loc[pandas.IndexSlice[("SP", "FR"), "2013-01-01":"2014-01-01"], :]
print()
# here, the time indexation is not properly handled as dates < 2013-01-01 are kept
df_not_ok=df.loc[pandas.IndexSlice[("SP", "BE", "FR"), "2013-01-01":"2014-01-01"], :]
```
#### Expected Output
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 4386 entries, (BE, 2012-01-01 00:00:00) to (SP, 2016-01-01 00:00:00)
Data columns (total 1 columns):
0 4386 non-null int64
dtypes: int64(1)
memory usage: 51.4+ KB
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 732 entries, (FR, 2013-01-01 00:00:00) to (SP, 2014-01-01 00:00:00)
Data columns (total 1 columns):
0 732 non-null int64
dtypes: int64(1)
memory usage: 8.6+ KB
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 3290 entries, (BE, **2013**-01-01 00:00:00) to (SP, 2014-01-01 00:00:00)
Data columns (total 1 columns):
0 **2196** non-null int64
dtypes: int64(1)
memory usage: 38.6+ KB
#### Actual output
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 4386 entries, (BE, 2012-01-01 00:00:00) to (SP, 2016-01-01 00:00:00)
Data columns (total 1 columns):
0 4386 non-null int64
dtypes: int64(1)
memory usage: 51.4+ KB
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 732 entries, (FR, 2013-01-01 00:00:00) to (SP, 2014-01-01 00:00:00)
Data columns (total 1 columns):
0 732 non-null int64
dtypes: int64(1)
memory usage: 8.6+ KB
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 3290 entries, (BE, **2012**-01-01 00:00:00) to (SP, 2014-01-01 00:00:00)
Data columns (total 1 columns):
0 **3290** non-null int64
dtypes: int64(1)
memory usage: 38.6+ KB
#### Output of `pd.show_versions()`
<details>
# Paste the output here
INSTALLED VERSIONS
commit: None
python: 2.7.11.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: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
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html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
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MDU6SXNzdWUxNzk1MjM5NTc=
| 14,305 |
read_gbq precision lost for floats above 10^4
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| 0 | 2016-09-27T15:17:47Z | 2017-02-09T17:11:31Z | 2017-02-09T17:11:31Z |
NONE
| null |
`pandas.read_gbq` will silently cast float values over 10k to integeres causing precision lost
#### example of the issue
``` python
In[1]:
import pandas as pd
import numpy as np
df=pd.read_gbq('''select * from
(select pi(),9999.9,10000.1,200000.2),
(select 2.72,9999.9999999,10000.1,300000.3)''',
project_id='CHANGE-ME')
```
#### Actual Output
``` python
In[2]: print(df)
Out[2]:
f0_ f1_ f2_ f3_
0 3.141593 9999.9 10000 200000
1 2.720000 10000.0 10000 300000
In[3]: df.dtypes
Out[3]:
f0_ float64
f1_ float64
f2_ int64
f3_ int64
dtype: object
```
#### Expected Output
``` python
In[2]: print(df)
Out[2]:
f0_ f1_ f2_ f3_
0 3.141593 9999.9 10000.1 200000.2
1 2.720000 10000.0 10000.1 300000.3
In[3]: df.dtypes
Out[3]:
f0_ float64
f1_ float64
f2_ float64
f3_ float64
dtype: object
```
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-36-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: C.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0rc1+33.g7dedbed
nose: 1.3.7
pip: 8.1.2
setuptools: 25.1.6
Cython: 0.24.1
numpy: 1.11.1
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: 3.6.4
bs4: 4.5.1
html5lib: 0.999999999
httplib2: 0.9.2
apiclient: 1.5.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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| 14,306 |
DatetimeIndex.append not working
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| 1 | 2016-09-27T16:05:12Z | 2016-09-27T16:47:04Z | 2016-09-27T16:47:04Z |
NONE
| null |
#### A small, complete example of the issue
``` python
# Your code here
xx = pd.to_datetime(['2000-01-01','2000-02-01'])
xx.append(xx[0])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Anaconda\lib\site-packages\pandas\tseries\index.py", line 1058, in append
return factory(to_concat)
File "C:\Anaconda\lib\site-packages\pandas\tseries\index.py", line 2240, in <lambda>
factory_func = lambda x: klass(concat(x), name=name, **kwargs)
ValueError: all the input arrays must have same number of dimensions
# insert works as expected
xx.insert(0, xx[0])
DatetimeIndex(['2000-01-01', '2000-01-01', '2000-02-01'], dtype='datetime64[ns]', freq=None)
```
#### Expected Output
append one item at the end of xx
#### Output of `pd.show_versions()`
## 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 44 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_GB
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.0
setuptools: 0.6c11
Cython: 0.21
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.7
lxml: 3.5.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.32.1
pandas_datareader: None
<details>
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MDU6SXNzdWUxNzk1ODU5MDY=
| 14,307 |
“Already tz-aware” error when reading h5 file using python 3 (but not 2)
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| 3 | 2016-09-27T19:12:09Z | 2016-11-23T13:12:02Z | 2016-11-23T13:12:01Z |
NONE
| null |
I have an h5 store named `weather.h5`. My default Python environment is 3.5.2. When I try to read this store I get `TypeError: Already tz-aware, use tz_convert to convert`.
I've tried both `pd.read_hdf('weather.h5','weather_history']` and `pd.io.pytables.HDFStore('weather.h5')['weather_history']`, but I get the error no matter what.
I can open the h5 in a Python 2.7 environment, so I'm assuming that this is a bug relating to the Python 3.5 install of pandas.
#### Traceback for `pd.read_hdf('weather.h5','weather_history']`
<details>
```
Traceback (most recent call last)
<ipython-input-21-716ccba8a093> in <module>()
----> 1 pd.read_hdf('weather.h5', 'weather_history')
/Users/jkelleher/anaconda/lib/python3.5/site-packages/pandas/io/pytables.py in read_hdf(path_or_buf, key, **kwargs)
328 'multiple datasets.')
329 key = keys[0]
--> 330 return store.select(key, auto_close=auto_close, **kwargs)
331 except:
332 # if there is an error, close the store
/Users/jkelleher/anaconda/lib/python3.5/site-packages/pandas/io/pytables.py in select(self, key, where, start, stop, columns, iterator, chunksize, auto_close, **kwargs)
678 chunksize=chunksize, auto_close=auto_close)
679
--> 680 return it.get_result()
681
682 def select_as_coordinates(
/Users/jkelleher/anaconda/lib/python3.5/site-packages/pandas/io/pytables.py in get_result(self, coordinates)
1362
1363 # directly return the result
-> 1364 results = self.func(self.start, self.stop, where)
1365 self.close()
1366 return results
/Users/jkelleher/anaconda/lib/python3.5/site-packages/pandas/io/pytables.py in func(_start, _stop, _where)
671 return s.read(start=_start, stop=_stop,
672 where=_where,
--> 673 columns=columns, **kwargs)
674
675 # create the iterator
/Users/jkelleher/anaconda/lib/python3.5/site-packages/pandas/io/pytables.py in read(self, **kwargs)
2777 axes = []
2778 for i in range(self.ndim):
-> 2779 ax = self.read_index('axis%d' % i)
2780 axes.append(ax)
2781
/Users/jkelleher/anaconda/lib/python3.5/site-packages/pandas/io/pytables.py in read_index(self, key)
2365 return self.read_sparse_intindex(key)
2366 elif variety == u('regular'):
-> 2367 _, index = self.read_index_node(getattr(self.group, key))
2368 return index
2369 else: # pragma: no cover
/Users/jkelleher/anaconda/lib/python3.5/site-packages/pandas/io/pytables.py in read_index_node(self, node)
2490 else:
2491 index = factory(
-> 2492 _unconvert_index(data, kind, encoding=self.encoding), **kwargs)
2493
2494 index.name = name
/Users/jkelleher/anaconda/lib/python3.5/site-packages/pandas/indexes/base.py in __new__(cls, data, dtype, copy, name, fastpath, tupleize_cols, **kwargs)
151 is_datetimetz(data)):
152 from pandas.tseries.index import DatetimeIndex
--> 153 result = DatetimeIndex(data, copy=copy, name=name, **kwargs)
154 if dtype is not None and _o_dtype == dtype:
155 return Index(result.to_pydatetime(), dtype=_o_dtype)
/Users/jkelleher/anaconda/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
/Users/jkelleher/anaconda/lib/python3.5/site-packages/pandas/tseries/index.py in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, closed, ambiguous, dtype, **kwargs)
319 # the tz's must match
320 if str(tz) != str(data.tz):
--> 321 raise TypeError("Already tz-aware, use tz_convert "
322 "to convert.")
323
TypeError: Already tz-aware, use tz_convert to convert.
```
</details>
#### Traceback for `pd.io.pytables.HDFStore('weather.h5')['weather_history']`
<details>
```
Traceback (most recent call last)
<ipython-input-25-8aaea36ce932> in <module>()
----> 1 pd.io.pytables.HDFStore('weather.h5')['weather_history']
/Users/jkelleher/anaconda/lib/python3.5/site-packages/pandas/io/pytables.py in __getitem__(self, key)
415
416 def __getitem__(self, key):
--> 417 return self.get(key)
418
419 def __setitem__(self, key, value):
/Users/jkelleher/anaconda/lib/python3.5/site-packages/pandas/io/pytables.py in get(self, key)
632 if group is None:
633 raise KeyError('No object named %s in the file' % key)
--> 634 return self._read_group(group)
635
636 def select(self, key, where=None, start=None, stop=None, columns=None,
/Users/jkelleher/anaconda/lib/python3.5/site-packages/pandas/io/pytables.py in _read_group(self, group, **kwargs)
1270 s = self._create_storer(group)
1271 s.infer_axes()
-> 1272 return s.read(**kwargs)
1273
1274
/Users/jkelleher/anaconda/lib/python3.5/site-packages/pandas/io/pytables.py in read(self, **kwargs)
2777 axes = []
2778 for i in range(self.ndim):
-> 2779 ax = self.read_index('axis%d' % i)
2780 axes.append(ax)
2781
/Users/jkelleher/anaconda/lib/python3.5/site-packages/pandas/io/pytables.py in read_index(self, key)
2365 return self.read_sparse_intindex(key)
2366 elif variety == u('regular'):
-> 2367 _, index = self.read_index_node(getattr(self.group, key))
2368 return index
2369 else: # pragma: no cover
/Users/jkelleher/anaconda/lib/python3.5/site-packages/pandas/io/pytables.py in read_index_node(self, node)
2490 else:
2491 index = factory(
-> 2492 _unconvert_index(data, kind, encoding=self.encoding), **kwargs)
2493
2494 index.name = name
/Users/jkelleher/anaconda/lib/python3.5/site-packages/pandas/indexes/base.py in __new__(cls, data, dtype, copy, name, fastpath, tupleize_cols, **kwargs)
151 is_datetimetz(data)):
152 from pandas.tseries.index import DatetimeIndex
--> 153 result = DatetimeIndex(data, copy=copy, name=name, **kwargs)
154 if dtype is not None and _o_dtype == dtype:
155 return Index(result.to_pydatetime(), dtype=_o_dtype)
/Users/jkelleher/anaconda/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
/Users/jkelleher/anaconda/lib/python3.5/site-packages/pandas/tseries/index.py in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, closed, ambiguous, dtype, **kwargs)
319 # the tz's must match
320 if str(tz) != str(data.tz):
--> 321 raise TypeError("Already tz-aware, use tz_convert "
322 "to convert.")
323
TypeError: Already tz-aware, use tz_convert to convert.
```
</details>
#### Output of `pd.show_versions()`
<details>
## 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: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
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: 1.1.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.3
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
</details>
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