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} | 26 | 2016-03-13T14:46:55Z | 2020-03-24T01:34:03Z | 2017-07-26T23:56:24Z | CONTRIBUTOR | null | I'm working on a solution for issue #3729.
I've identified where to work and put in place a first, crude solution. I need to do more tests.
- [ ] closes #3729.
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
xref #9941, #5456, #6992, #443
---
2016-08-28: here are the tests I think need to be added / copied /adapted:
Basic tests:
- [x] Create a new test class, in a new file
- [ ] test_basic_with_nan with dtypes in object, datetimes, timedelta & datetime64 with tz
- [x] test_groupby_series_dropna
- [x] test_groupby_frame_dropna
- [ ] test_groupby_panel_dropna
- [x] test_groupby_multi_dropna
Tests with data types
- [x] integer, floats
- [x] datetime, timedelta
- [x] strings and other objects
- [x] None
Existing tests to copy or adapt from TestGroupBy:
- [x] test_first_last_nth
- [x] test_nth
- [x] test_nth_multi_index_as_expected
- [x] test_nth_multi_index
- [ ] test_group_selection_cache
- [x] test_basic
- [x] test_with_na
- [ ] test_series_groupby_nunique
- [ ] test_groupby_level_with_nas
- [ ] test_groupby_nat_exclude
- [ ] test_groupby_groups_datetimeindex
- [ ] test_nlargest
- [ ] test_nsmallest
- [ ] test_groupby_as_index_agg
- [ ] test_agg_api
- [ ] test_transform
- [ ] test_builtins_apply
- [ ] test_groupby_whitelist
- [x] test__cython_agg_general
- [x] test_ops_general
- [x] test_groupby_transform_with_nan_group
And also I will need to edit the documentation, for instance this part:
- [ ] Edit http://pandas-docs.github.io/pandas-docs-travis/groupby.html
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| null | 8 | 2016-03-13T15:08:29Z | 2016-05-26T16:15:03Z | 2016-05-26T16:15:03Z | CONTRIBUTOR | null | - [x ] closes https://github.com/pydata/pandas/pull/12268#discussion_r55924987
This is the msg:
more consistent with code in the rest of the document
added more namespace
Generally, also numpy's randn should used as
np.random.randn
isolated namespace changes
Note:
I hope I got the rebase & merge right.
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} | 8 | 2016-03-13T17:06:28Z | 2016-03-17T01:34:41Z | 2016-03-14T12:51:28Z | NONE | null | Updating from 0.17.1 to 0.18.0 breaks `group_keys=False`.
```
>>> data = pd.DataFrame([1, 2, 3, 4], index=pd.date_range("20000101", periods=4))
>>> data.groupby(data.index, group_keys=False).resample('4H', how='last')
```
0.17.1:
```
0
2000-01-01 1
2000-01-02 2
2000-01-03 3
2000-01-04 4
```
0.18.0:
```
0
2000-01-01 2000-01-01 1
2000-01-02 2000-01-02 2
2000-01-03 2000-01-03 3
2000-01-04 2000-01-04 4
```
I also tried `.resample('4H').last()`, and it fails as well. With `sum()` in place of `resample()`, it works correctly.
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-79-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: None
pip: 8.0.2
setuptools: 19.6.2
Cython: None
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: None
sphinx: None
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
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: None
boto: None
```
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} | 2 | 2016-03-13T17:11:38Z | 2016-03-25T13:30:15Z | 2016-03-25T13:30:15Z | CONTRIBUTOR | null | #### Code Sample
```
import pandas as pd
import numpy as np
ndarr = np.array([1,2,3])
series1 = pd.Series(ndarr, name=['series1'])
series2 = pd.Series([1,2,3], name='series2')
series1.name
##['series1']
result_df = pd.concat([series1, series2], axis=1)
result_df
##give **type error**
##also
series3 = pd.Series(ndarr, name=['series1', 'series2'])
series3.name
##['series1', 'series2']
```
#### Expected Output
get error when result_df try to be created with array name attribute,
or give error when try to create Series object with array argument in name attribute.
#### output of `pd.show_versions()`
---
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-30-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.0
setuptools: 20.2.2
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.6
patsy: 0.4.1
dateutil: 2.5.0
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: 2.3.3
xlrd: 0.9.4
xlwt: None
xlsxwriter: 0.8.4
lxml: 3.5.0
bs4: 4.4.1
html5lib: 1.0b8
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
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} | 4 | 2016-03-13T23:25:58Z | 2017-03-23T20:19:56Z | 2017-03-23T20:19:56Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```
$ nosetests-2.7 pandas.io.tests.test_parsers:TestCParserHighMemory.test_yy_format
F
======================================================================
FAIL: test_yy_format (pandas.io.tests.test_parsers.TestCParserHighMemory)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/local/lib/python2.7/site-packages/pandas/io/tests/test_parsers.py", line 1064, in test_yy_format
tm.assert_frame_equal(rs, xp)
File "/usr/local/lib/python2.7/site-packages/pandas/util/testing.py", line 1097, in assert_frame_equal
obj='{0}.index'.format(obj))
File "/usr/local/lib/python2.7/site-packages/pandas/util/testing.py", line 729, in assert_index_equal
obj=obj, lobj=left, robj=right)
File "pandas/src/testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:3809)
File "pandas/src/testing.pyx", line 147, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2685)
File "/usr/local/lib/python2.7/site-packages/pandas/util/testing.py", line 880, in raise_assert_detail
raise AssertionError(msg)
AssertionError: DataFrame.index are different
DataFrame.index values are different (100.0 %)
[left]: DatetimeIndex(['2031-09-01 00:10:00', '2028-09-02 10:20:00',
'2031-09-03 08:30:00'],
dtype='datetime64[ns]', name=u'date_time', freq=None)
[right]: DatetimeIndex(['2009-01-31 00:10:00', '2009-02-28 10:20:00',
'2009-03-31 08:30:00'],
dtype='datetime64[ns]', name=u'date_time', freq=None)
----------------------------------------------------------------------
Ran 1 test in 0.085s
FAILED (failures=1)
```
Also `pandas.io.tests.test_parsers:TestCParserLowMemory.test_yy_format` and `pandas.io.tests.test_parsers:TestPythonParser.test_yy_format`.
This appears to be in the class of bugs described in #3341.
```
$ python
Python 2.7.11 (default, Jan 9 2016, 17:23:07)
[GCC 4.2.1 Compatible FreeBSD Clang 3.4.1 (tags/RELEASE_34/dot1-final 208032)] on freebsd10
Type "help", "copyright", "credits" or "license" for more information.
>>> import dateutil.parser
>>> p = dateutil.parser.parser()
>>> p.parse('090131', dayfirst=False, yearfirst=False)
datetime.datetime(2031, 9, 1, 0, 0)
>>> p.parse('090131', dayfirst=False, yearfirst=True)
datetime.datetime(2009, 1, 31, 0, 0)
>>> p.parse('090131', dayfirst=True, yearfirst=False)
datetime.datetime(2031, 1, 9, 0, 0)
>>> p.parse('090131', dayfirst=True, yearfirst=True)
datetime.datetime(2009, 1, 31, 0, 0)
>>>
```
#### Expected Output
Expect tests to pass.
#### output of `pd.show_versions()`
This is reproducible with both of the following configurations.
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: FreeBSD
OS-release: 10.2-STABLE
machine: amd64
processor: amd64
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.0
nose: 1.3.7
pip: 8.0.2
setuptools: 20.0
Cython: None
numpy: 1.10.4
scipy: 0.16.1
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.0
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.0
openpyxl: 2.3.3
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.8.4
lxml: 3.5.0
bs4: 4.4.1
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: 0.7.10
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.4.final.0
python-bits: 64
OS: FreeBSD
OS-release: 10.2-STABLE
machine: amd64
processor: amd64
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.0
nose: 1.3.7
pip: None
setuptools: 20.0
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.16.1
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.0
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: None
openpyxl: 2.3.3
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.8.4
lxml: 3.5.0
bs4: 4.4.1
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: 0.7.10
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
```
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- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
pandas >= 0.18.0 will no longer support compatibility with Python version 2.6 and since Python 2.6 we can use the abstract base class collections.Hashable.
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COMPAT: dateutil 2.5.0 bugfix causes some tests to fail, skip for now
xref #12611
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} | 14 | 2016-03-14T07:12:22Z | 2016-03-16T15:14:04Z | 2016-03-16T13:17:21Z | CONTRIBUTOR | null | To fix bug #12577 : Crosstab margins ignoring dropna
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- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
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```
# we can create Series contains Periods with mixed freq
s = pd.Series([pd.Period('2011-01', freq='M'), pd.Period('2011-02-01', freq='D')])
# print the created Series raises ValueError
print(s)
# ValueError: Input has different freq=D from PeriodIndex(freq=M)
```
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| null | 4 | 2016-03-14T08:12:58Z | 2016-05-07T18:49:59Z | 2016-05-07T18:49:59Z | NONE | null | ```
>>> from pandas.tseries.holiday import next_monday_or_tuesday
>>> from pandas.tseries.holiday import AbstractHolidayCalendar
>>>from pandas.tseries.holiday import Holiday
>>>class WrongBoxingDay(AbstractHolidayCalendar):
rules = [
Holiday('xmas', month=12, day=25, observance=next_monday_or_tuesday),
Holiday('boxing day', month=12, day=26, observance=next_monday_or_tuesday),
]
>>>import pandas as pd
>>>cal = WrongBoxingDay()
>>>cal.holidays(pd.datetime(2016,12,1), pd.datetime(2016,12,31))
DatetimeIndex(['2016-12-27', '2016-12-27'], dtype='datetime64[ns]', freq=None, tz=None)
```
The next_monday_or_tuesday function in holiday module isn't doing what it is supposed to do.
In the example above, xmas should move to Dec 27 while boxing day should stay on Dec 26, but it was also moved to Dec 27.
This bug still exits in the latest version.
How my commit works is explained in the docstring of the function.
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} | 2 | 2016-03-14T08:17:11Z | 2016-04-10T14:26:28Z | 2016-04-10T14:26:28Z | CONTRIBUTOR | null | `Index.str.partition`'s behaviour changed in 3ab35b40274276e4f9b40918a7e260f1abb941c1 in a backwards incompatible way which doesn't seem intentional.
#### Code Sample, a copy-pastable example if possible
```
python3 -c 'import pandas; pandas.Index(["a,b", "c,d"], name="hello").str.partition(",")'
```
``` pytb
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/pwaller/.local/src/pandas/pandas/core/strings.py", line 1432, in partition
return self._wrap_result(result, expand=expand)
File "/home/pwaller/.local/src/pandas/pandas/core/strings.py", line 1348, in _wrap_result
return MultiIndex.from_tuples(result, names=name)
File "/home/pwaller/.local/src/pandas/pandas/indexes/multi.py", line 889, in from_tuples
return MultiIndex.from_arrays(arrays, sortorder=sortorder, names=names)
File "/home/pwaller/.local/src/pandas/pandas/indexes/multi.py", line 844, in from_arrays
names=names, verify_integrity=False)
File "/home/pwaller/.local/src/pandas/pandas/indexes/multi.py", line 92, in __new__
result._set_names(names)
File "/home/pwaller/.local/src/pandas/pandas/indexes/multi.py", line 446, in _set_names
raise ValueError('Length of names must match number of levels in '
ValueError: Length of names must match number of levels in MultiIndex.
```
#### Expected Output
``` python
In [1]: import pandas; pandas.Index(["a,b", "c,d"], name="hello").str.partition(",")
Out[1]:
MultiIndex(levels=[['a', 'c'], [','], ['b', 'd']],
labels=[[0, 1], [0, 0], [0, 1]])
```
#### Versions
I bisected the problem to 3ab35b40274276e4f9b40918a7e260f1abb941c1 (cc @sinhrks). The problem is not present in 0.17.1 and is present in 0.18.0rc2.
Note: when I use a `name="foo"`, it happens to work because it has the correct length, the index name ends up being `["f", "o", "o"]`, so it's possible this is tested somewhere and happens to work even though it's not correct for a large number of use cases.
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} | 5 | 2016-03-14T10:14:47Z | 2021-01-27T11:51:37Z | 2017-05-22T17:05:18Z | CONTRIBUTOR | null | Hello,
I think it would make sense to make `read_csv()`, `read_table()`, `read_fwf()` able to read multiple files with the same structure. It might be tricky to read multiple files into one string, especially when all of them have header line(s) and when you want to use the following parameters: `skiprows`, `skipfooter`, `nrows`.
The logic for (`skiprows`, `skipfooter`, `nrows`) is already implemented, so IMO it shouldn't be very difficult. The `header` parameter (if header exists) must be read/parsed only from the first (from one) file.
Of course one can always do something like:
`df = pd.concat([pd.read_csv(f, **kwargs) for f in flist], ignore_index=True)
`
but it's not very efficient when working with big files.
In the last days there were plenty of similar questions stackoverflow.com, asking how to merge CSV files with the same structure.
Thank you!
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} | 3 | 2016-03-14T10:50:19Z | 2018-07-02T23:44:12Z | 2018-07-02T23:44:12Z | MEMBER | null | We can create / convert using datetimetz dtype, but it doesn't work some cases.
```
s = pd.Series([pd.Timestamp('2011-01-31', tz='US/Eastern')])
s
#0 2011-01-31 00:00:00-05:00
# dtype: datetime64[ns, US/Eastern]
# OK
s.astype('datetime64[ns, Asia/Tokyo]')
#0 2011-01-31 14:00:00+09:00
# dtype: datetime64[ns, Asia/Tokyo]
```
### astype
```
# numpy (OK)
pd.Series([1296432000000000000]).astype('datetime64[ns]')
#0 2011-01-31
# dtype: datetime64[ns]
# extended (NG)
pd.Series([1296432000000000000]).astype('datetime64[ns, Asia/Tokyo]')
# TypeError: Invalid datetime unit in metadata string "[ns, Asia/Tokyo]"
```
### dtype arg
```
# extended (OK ? I think the result should be 2011-01-31 00:00:00-05:00... see below)
pd.Series([1296432000000000000], dtype='datetime64[ns, US/Eastern]')
#0 2011-01-31 05:00:00-05:00
# dtype: datetime64[ns, US/Eastern]
# ref
pd.Series([1296432000000000000], dtype='datetime64[ns]').dt.tz_localize('US/Eastern')
#0 2011-01-31 00:00:00-05:00
# dtype: datetime64[ns, US/Eastern]
```
```
# extended (NG)
pd.Series([pd.Timestamp('2011-01-01', tz='US/Eastern')], dtype='datetime64[ns, US/Eastern]')
# TypeError: data type not understood
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12620 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12620/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12620/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12620/events | https://github.com/pandas-dev/pandas/issues/12620 | 140,640,306 | MDU6SXNzdWUxNDA2NDAzMDY= | 12,620 | BUG: Concat with tz-aware and timedelta raises AttributeError | {
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"description": "after 0.18.0 of course!",
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"title": "0.18.1",
"updated_at": "2017-08-10T09:01:26Z",
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} | 1 | 2016-03-14T10:56:01Z | 2016-03-17T14:17:46Z | 2016-03-17T14:17:46Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```
pd.concat([pd.Series([pd.Timestamp('2011-01-01', tz='US/Eastern')]),
pd.Series([pd.Timedelta('1 day')])])
# AttributeError: 'numpy.ndarray' object has no attribute 'tz_localize'
```
#### Expected Output
```
pd.Series([pd.Timestamp('2011-01-01', tz='US/Eastern'), pd.Timedelta('1 day')])
#0 2011-01-01 00:00:00-05:00
#1 1 days 00:00:00
# dtype: object
```
#### output of `pd.show_versions()`
current master.
It is caused by the below line, we must check `typs` length must be 1, otherwise the result should be `object` dtype.
https://github.com/pydata/pandas/blob/master/pandas/tseries/common.py#L278
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https://api.github.com/repos/pandas-dev/pandas/issues/12621 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12621/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12621/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12621/events | https://github.com/pandas-dev/pandas/issues/12621 | 140,693,948 | MDU6SXNzdWUxNDA2OTM5NDg= | 12,621 | DataFrame.groupby no longer works after upgrading to latest pandas and numpy | {
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| closed | false | null | []
| null | 2 | 2016-03-14T14:43:49Z | 2016-03-14T16:38:21Z | 2016-03-14T16:38:21Z | NONE | null | #### Code Sample
```
import pandas as pd
df = pd.DataFrame({'group': 'abacb', 'value': [1, 2, 3, 4, 5]})
df.groupby('group').sum()
# Expected output - hopefully obvious :)
```
#### Exception raised
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in f(self)
95 try:
---> 96 return self._cython_agg_general(alias, numeric_only=numeric_only)
97 except AssertionError as e:
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in _cython_agg_general(self, how, numeric_only)
3037 new_items, new_blocks = self._cython_agg_blocks(
-> 3038 how, numeric_only=numeric_only)
3039 return self._wrap_agged_blocks(new_items, new_blocks)
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in _cython_agg_blocks(self, how, numeric_only)
3074 result, _ = self.grouper.aggregate(
-> 3075 block.values, how, axis=agg_axis)
3076
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in aggregate(self, values, how, axis)
1819 def aggregate(self, values, how, axis=0):
-> 1820 return self._cython_operation('aggregate', values, how, axis)
1821
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in _cython_operation(self, kind, values, how, axis)
1737 "supported for the 'how' argument")
-> 1738 out_shape = (self.ngroups,) + values.shape[1:]
1739
pandas/src/properties.pyx in pandas.lib.cache_readonly.__get__ (pandas/lib.c:44357)()
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in ngroups(self)
1611 def ngroups(self):
-> 1612 return len(self.result_index)
1613
pandas/src/properties.pyx in pandas.lib.cache_readonly.__get__ (pandas/lib.c:44357)()
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in result_index(self)
1623 if not self.compressed and len(self.groupings) == 1:
-> 1624 return self.groupings[0].group_index.rename(self.names[0])
1625
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in group_index(self)
2275 if self._group_index is None:
-> 2276 self._make_labels()
2277 return self._group_index
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in _make_labels(self)
2280 if self._labels is None or self._group_index is None:
-> 2281 labels, uniques = algos.factorize(self.grouper, sort=self.sort)
2282 uniques = Index(uniques, name=self.name)
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/algorithms.py in factorize(values, sort, order, na_sentinel, size_hint)
195 uniques = vec_klass()
--> 196 labels = table.get_labels(vals, uniques, 0, na_sentinel, True)
197
hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_labels (pandas/hashtable.c:12817)()
TypeError: get_labels() takes exactly 4 positional arguments (5 given)
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in aggregate(self, arg, *args, **kwargs)
3118 result = self._aggregate_multiple_funcs(
-> 3119 [arg], _level=_level)
3120 result.columns = Index(
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/base.py in _aggregate_multiple_funcs(self, arg, _level)
611
--> 612 return concat(results, keys=keys, axis=1)
613
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/tools/merge.py in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)
833 verify_integrity=verify_integrity,
--> 834 copy=copy)
835 return op.get_result()
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/tools/merge.py in __init__(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy)
866 if len(objs) == 0:
--> 867 raise ValueError('No objects to concatenate')
868
ValueError: No objects to concatenate
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in _aggregate_generic(self, func, *args, **kwargs)
3143 try:
-> 3144 for name, data in self:
3145 result[name] = self._try_cast(func(data, *args, **kwargs),
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in get_iterator(self, data, axis)
1479 """
-> 1480 splitter = self._get_splitter(data, axis=axis)
1481 keys = self._get_group_keys()
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in _get_splitter(self, data, axis)
1485 def _get_splitter(self, data, axis=0):
-> 1486 comp_ids, _, ngroups = self.group_info
1487 return get_splitter(data, comp_ids, ngroups, axis=axis)
pandas/src/properties.pyx in pandas.lib.cache_readonly.__get__ (pandas/lib.c:44357)()
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in group_info(self)
1593 def group_info(self):
-> 1594 comp_ids, obs_group_ids = self._get_compressed_labels()
1595
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in _get_compressed_labels(self)
1600 def _get_compressed_labels(self):
-> 1601 all_labels = [ping.labels for ping in self.groupings]
1602 if len(all_labels) > 1:
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in <listcomp>(.0)
1600 def _get_compressed_labels(self):
-> 1601 all_labels = [ping.labels for ping in self.groupings]
1602 if len(all_labels) > 1:
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in labels(self)
2269 if self._labels is None:
-> 2270 self._make_labels()
2271 return self._labels
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in _make_labels(self)
2280 if self._labels is None or self._group_index is None:
-> 2281 labels, uniques = algos.factorize(self.grouper, sort=self.sort)
2282 uniques = Index(uniques, name=self.name)
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/algorithms.py in factorize(values, sort, order, na_sentinel, size_hint)
195 uniques = vec_klass()
--> 196 labels = table.get_labels(vals, uniques, 0, na_sentinel, True)
197
hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_labels (pandas/hashtable.c:12817)()
TypeError: get_labels() takes exactly 4 positional arguments (5 given)
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-51-a3194810ca8a> in <module>()
----> 1 df.groupby('group').sum()
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in f(self)
98 raise SpecificationError(str(e))
99 except Exception:
--> 100 result = self.aggregate(lambda x: npfunc(x, axis=self.axis))
101 if _convert:
102 result = result._convert(datetime=True)
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in aggregate(self, arg, *args, **kwargs)
3585 @Appender(SelectionMixin._agg_doc)
3586 def aggregate(self, arg, *args, **kwargs):
-> 3587 return super(DataFrameGroupBy, self).aggregate(arg, *args, **kwargs)
3588
3589 agg = aggregate
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in aggregate(self, arg, *args, **kwargs)
3122 name=self._selected_obj.columns.name)
3123 except:
-> 3124 result = self._aggregate_generic(arg, *args, **kwargs)
3125
3126 if not self.as_index:
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in _aggregate_generic(self, func, *args, **kwargs)
3146 data)
3147 except Exception:
-> 3148 return self._aggregate_item_by_item(func, *args, **kwargs)
3149 else:
3150 for name in self.indices:
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in _aggregate_item_by_item(self, func, *args, **kwargs)
3190 # GH6337
3191 if not len(result_columns) and errors is not None:
-> 3192 raise errors
3193
3194 return DataFrame(result, columns=result_columns)
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in _aggregate_item_by_item(self, func, *args, **kwargs)
3175 grouper=self.grouper)
3176 result[item] = self._try_cast(
-> 3177 colg.aggregate(func, *args, **kwargs), data)
3178 except ValueError:
3179 cannot_agg.append(item)
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in aggregate(self, func_or_funcs, *args, **kwargs)
2587 return self._python_agg_general(func_or_funcs, *args, **kwargs)
2588 except Exception:
-> 2589 result = self._aggregate_named(func_or_funcs, *args, **kwargs)
2590
2591 index = Index(sorted(result), name=self.grouper.names[0])
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in _aggregate_named(self, func, *args, **kwargs)
2701 result = {}
2702
-> 2703 for name, group in self:
2704 group.name = name
2705 output = func(group, *args, **kwargs)
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in get_iterator(self, data, axis)
1478 for each group
1479 """
-> 1480 splitter = self._get_splitter(data, axis=axis)
1481 keys = self._get_group_keys()
1482 for key, (i, group) in zip(keys, splitter):
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in _get_splitter(self, data, axis)
1484
1485 def _get_splitter(self, data, axis=0):
-> 1486 comp_ids, _, ngroups = self.group_info
1487 return get_splitter(data, comp_ids, ngroups, axis=axis)
1488
pandas/src/properties.pyx in pandas.lib.cache_readonly.__get__ (pandas/lib.c:44357)()
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in group_info(self)
1592 @cache_readonly
1593 def group_info(self):
-> 1594 comp_ids, obs_group_ids = self._get_compressed_labels()
1595
1596 ngroups = len(obs_group_ids)
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in _get_compressed_labels(self)
1599
1600 def _get_compressed_labels(self):
-> 1601 all_labels = [ping.labels for ping in self.groupings]
1602 if len(all_labels) > 1:
1603 group_index = get_group_index(all_labels, self.shape,
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in <listcomp>(.0)
1599
1600 def _get_compressed_labels(self):
-> 1601 all_labels = [ping.labels for ping in self.groupings]
1602 if len(all_labels) > 1:
1603 group_index = get_group_index(all_labels, self.shape,
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in labels(self)
2268 def labels(self):
2269 if self._labels is None:
-> 2270 self._make_labels()
2271 return self._labels
2272
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/groupby.py in _make_labels(self)
2279 def _make_labels(self):
2280 if self._labels is None or self._group_index is None:
-> 2281 labels, uniques = algos.factorize(self.grouper, sort=self.sort)
2282 uniques = Index(uniques, name=self.name)
2283 self._labels = labels
/home/jon/development/testing/venv/lib/python3.4/site-packages/pandas/core/algorithms.py in factorize(values, sort, order, na_sentinel, size_hint)
194 table = hash_klass(size_hint or len(vals))
195 uniques = vec_klass()
--> 196 labels = table.get_labels(vals, uniques, 0, na_sentinel, True)
197
198 labels = com._ensure_platform_int(labels)
hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_labels (pandas/hashtable.c:12817)()
TypeError: get_labels() takes exactly 4 positional arguments (5 given)
```
#### output of `pd.show_versions()`
commit: None
python: 3.4.0.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-37-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 1.5.4
setuptools: 18.5
Cython: 0.20
numpy: 1.10.4
scipy: 0.15.1
statsmodels: None
xarray: None
IPython: 3.0.0
sphinx: None
patsy: None
dateutil: 2.5.0
pytz: 2016.1
blosc: None
bottleneck: None
tables: None
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: 2.2.6
xlrd: 0.9.3
xlwt: 0.8.0
xlsxwriter: None
lxml: 3.4.2
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: 0.6.6.None
psycopg2: None
jinja2: 2.8
boto: None
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| null | 2 | 2016-03-14T15:35:02Z | 2016-03-25T13:25:14Z | 2016-03-25T13:25:14Z | MEMBER | null | I'm not sure if we can fix this in pandas, but I ran into this recently:
``` python
In [10]: d = {'foo': 1, 'bar': [{'what': {'footttttttttttttttttttttttttttttttttt': pd.Timestamp('2011-01-01')}}] * 10}
In [11]: pprint.pprint(d)
<SNIP>
lib/python3.5/pprint.py in _format_dict_items(self, items, stream, indent, allowance, context, level)
344 self._format(ent, stream, indent + len(rep) + 2,
345 allowance if last else 1,
--> 346 context, level)
347 if not last:
348 write(delimnl)
lib/python3.5/pprint.py in _format(self, object, stream, indent, allowance, context, level)
162 max_width = self._width - indent - allowance
163 if len(rep) > max_width:
--> 164 p = self._dispatch.get(type(object).__repr__, None)
165 if p is not None:
166 context[objid] = 1
TypeError: unhashable type: 'instancemethod'
```
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} | 4 | 2016-03-14T19:38:08Z | 2016-03-17T14:06:48Z | 2016-03-17T14:06:48Z | CONTRIBUTOR | null | ``` python
#### Code Sample, a copy-pastable example if possible
In [6]: s = pd.Series([0, 1])
In [5]: s.rename(pd.Series({0: 10, 1: 20}))
Out[5]:
0 0
1 1
Name: [10, 20], dtype: int64
```
Problem is [here](https://github.com/pydata/pandas/blob/4f5099b4ee5f5c32f6b299de93902c9a074ac228/pandas/core/series.py#L2336) using an `isinstance(,MutableMapping)` instead of `com.is_dict_like`. [`rename_axis`](https://github.com/pydata/pandas/blob/4f5099b4ee5f5c32f6b299de93902c9a074ac228/pandas/core/generic.py#L705) should be fine, but will add tests for both.
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} | 2 | 2016-03-14T20:01:35Z | 2016-03-17T13:54:50Z | 2016-03-17T13:54:50Z | CONTRIBUTOR | null | I'm not sure of `Panel` is still being supported, but `Panel.fillna()` seems to ignore `inplace=True`:
```
In [1]: import numpy as np
In [2]: import pandas as pd
In [5]: p1 = pd.Panel([[[0,1],[2,np.nan]], [[10,11],[12,np.nan]]], items=['a','b'], minor_axis=['x','y'])
In [7]: p1['a']
Out[7]:
x y
0 0.0 1.0
1 2.0 NaN
In [8]: p1['b']
Out[8]:
x y
0 10.0 11.0
1 12.0 NaN
In [9]: p1.fillna(method='ffill', inplace=True)
Out[9]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 2 (major_axis) x 2 (minor_axis)
Items axis: a to b
Major_axis axis: 0 to 1
Minor_axis axis: x to y
In [10]: p1['a']
Out[10]:
x y
0 0.0 1.0
1 2.0 NaN
In [11]: p1['b']
Out[11]:
x y
0 10.0 11.0
1 12.0 NaN
In [12]: p2 = p1.fillna(method='ffill', inplace=True)
In [13]: p2['a']
Out[13]:
x y
0 0.0 1.0
1 2.0 1.0
In [14]: p2['b']
Out[14]:
x y
0 10.0 11.0
1 12.0 11.0
In [15]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.4.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 62 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.0
setuptools: 20.2.2
Cython: None
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: 0.7.1
IPython: 4.1.2
sphinx: None
patsy: 0.4.1
dateutil: 2.5.0
pytz: 2016.1
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.3
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 1.0b8
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12625 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12625/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12625/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12625/events | https://github.com/pandas-dev/pandas/issues/12625 | 140,802,885 | MDU6SXNzdWUxNDA4MDI4ODU= | 12,625 | pandas_datareader failing to get all options for '^ndx' | {
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| null | 2 | 2016-03-14T21:30:22Z | 2016-03-14T21:56:06Z | 2016-03-14T21:48:17Z | NONE | null | The code below works for over 500 symbols but doesn't work for ^ndx.
```
In [1]: from pandas_datareader import data as pddata
In [2]: foo = pddata.Options('^ndx', 'yahoo')
In [3]: foo.expiry_dates
Out[3]:
[datetime.date(2016, 3, 18),
datetime.date(2016, 3, 24),
datetime.date(2016, 4, 1),
datetime.date(2016, 4, 8),
datetime.date(2016, 4, 15),
datetime.date(2016, 4, 22),
datetime.date(2016, 4, 29),
datetime.date(2016, 5, 20),
datetime.date(2016, 6, 17),
datetime.date(2016, 9, 16),
datetime.date(2016, 12, 16),
datetime.date(2017, 6, 16),
datetime.date(2017, 12, 15),
datetime.date(2018, 12, 21)]
In [4]: for d in foo.expiry_dates:
print d
foo.get_options_data(expiry=d)
...:
2016-03-18
2016-03-24
2016-04-01
2016-04-08
2016-04-15
2016-04-22
2016-04-29
2016-05-20
2016-06-17
2016-09-16
2016-12-16
```
```
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-5-dad98abfb543> in <module>()
1 for d in foo.expiry_dates:
2 print d
----> 3 foo.get_options_data(expiry=d)
4
/Users/v/virtenvs/ns/lib/python2.7/site-packages/pandas_datareader/yahoo/options.pyc in get_options_data(self, month, year, expiry)
146 return concat([f(month, year, expiry)
147 for f in (self.get_put_data,
--> 148 self.get_call_data)]).sortlevel()
149
150 def _get_option_frames_from_yahoo(self, expiry):
/Users/v/virtenvs/ns/lib/python2.7/site-packages/pandas_datareader/yahoo/options.pyc in get_put_data(self, month, year, expiry)
360 """
361 expiry = self._try_parse_dates(year, month, expiry)
--> 362 return self._get_data_in_date_range(expiry, put=True, call=False)
363
364 def get_near_stock_price(self, above_below=2, call=True, put=False,
/Users/v/virtenvs/ns/lib/python2.7/site-packages/pandas_datareader/yahoo/options.pyc in _get_data_in_date_range(self, dates, call, put)
606 frame = getattr(self, nam)
607 except AttributeError:
--> 608 frame = self._get_option_data(expiry=expiry_date, name=name)
609 data.append(frame)
610
/Users/v/virtenvs/ns/lib/python2.7/site-packages/pandas_datareader/yahoo/options.pyc in _get_option_data(self, expiry, name)
229 frames = getattr(self, frame_name)
230 except AttributeError:
--> 231 frames = self._get_option_frames_from_yahoo(expiry)
232
233 option_data = frames[name]
/Users/v/virtenvs/ns/lib/python2.7/site-packages/pandas_datareader/yahoo/options.pyc in _get_option_frames_from_yahoo(self, expiry)
150 def _get_option_frames_from_yahoo(self, expiry):
151 url = self._yahoo_url_from_expiry(expiry)
--> 152 option_frames = self._option_frames_from_url(url)
153 frame_name = '_frames' + self._expiry_to_string(expiry)
154 setattr(self, frame_name, option_frames)
/Users/v/virtenvs/ns/lib/python2.7/site-packages/pandas_datareader/yahoo/options.pyc in _option_frames_from_url(self, url)
173
174 root = self._parse_url(url)
--> 175 calls = root.xpath('//*[@id="optionsCallsTable"]/div[2]/div/table')[0]
176 puts = root.xpath('//*[@id="optionsPutsTable"]/div[2]/div/table')[0]
177
IndexError: list index out of range
```
```
In [9]: import pandas_datareader
In [10]: pandas_datareader.version
Out[10]: '0.2.1'
```
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} | 4 | 2016-03-14T22:14:24Z | 2017-04-05T02:06:43Z | 2016-03-17T14:06:48Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/12623
I added com.is_dict_like in https://github.com/pydata/pandas/pull/11980
and failed to use it for the `rename` method. Using that now and did
some refactoring while I was in there. Added more tests for rename.
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| null | 2 | 2016-03-14T23:05:55Z | 2016-03-15T13:18:33Z | 2016-03-14T23:09:08Z | NONE | null | #### Code Sample, a copy-pastable example if possible
Most of the time when I write a csv to file I don't need the index in the CSV output. I end up writing `to_csv('file/path.csv', index=False)` basically everytime. I am wondering if `Index=False` should be the default.
#### Expected Output
`pd.to_csv('file/path.csv')`
#### output of `pd.show_versions()`
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 15.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 1.1.6
Cython: None
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
IPython: 4.0.0
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 1.0b8
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.10
pymysql: None
psycopg2: None
Jinja2: None
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| null | 1 | 2016-03-14T23:59:18Z | 2016-03-15T00:07:12Z | 2016-03-15T00:06:54Z | CONTRIBUTOR | null | It would be nice if there was some way to join based on just some levels of a MultiIndex. For example, given a DataFrame like:
```
>>> df = pd.DataFrame({"a": [1, 1, 2], "b": [11, 12, 13], "c": [21, 22, 23]}).set_index(["a", "b"])
>>> df
c
a b
1 11 21
12 22
2 13 23
```
I'd like to join it to another DataFrame along, for example, just the first level of the index:
```
>>> other = pd.DataFrame({"a": [1, 2], "x": [91, 92]}).set_index("a")
>>> other
x
a
1 91
2 92
>>> df.join(other, left_index=["a"])
c x
a b
1 11 21 91
12 22 91
2 13 23 92
```
Or something similar.
As far as I can tell, the only way to do this now is with a combination of `reset_index` and `set_index`:
```
>>> df.reset_index().join(other, on="a").set_index(["a", "b"])
c x
a b
1 11 21 91
12 22 91
2 13 23 92
```
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} | 7 | 2016-03-15T02:18:14Z | 2016-03-25T13:36:27Z | 2016-03-25T13:25:14Z | CONTRIBUTOR | null | - [x] closes #12622
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
I suspect this is a bug in Cython (I don't think `type(A).func` should be an `instancemethod`). Not sure if we should use this workaround as we encounter issues in the wild, or do this for all the functions we can think of.
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| null | 4 | 2016-03-15T04:39:51Z | 2016-03-16T00:13:32Z | 2016-03-15T23:49:22Z | NONE | null | - [x] closes #12624
- [x] tests added / passed
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Fixes #12624
Author: Pedro M Duarte [email protected]
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} | 1 | 2016-03-15T10:33:30Z | 2016-03-31T13:38:14Z | 2016-03-31T13:38:14Z | MEMBER | null | Normal `Index` ignores `fill_value` (this is desribed in docstring)
```
pd.Index([1, 2, 3]).take(np.array([1, 0, -1]), fill_value=np.nan)
# Int64Index([2, 1, 3], dtype='int64')
```
But `DatetimeIndex` does (used in reshape ops at least).
```
pd.DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01'], tz='US/Eastern').take(np.array([1, 0, -1]), fill_value=pd.NaT)
# DatetimeIndex(['2011-02-01 00:00:00-05:00', '2011-01-01 00:00:00-05:00',
# 'NaT'],
# dtype='datetime64[ns, US/Eastern]', freq=None)
```
Otherwise `PeriodIndex` doesn't.
```
pd.PeriodIndex(['2011-01', '2011-02', '2011-03'], freq='M').take(np.array([1, 0, -1]), fill_value=pd.NaT)
# PeriodIndex(['2011-02', '2011-01', '2011-03'], dtype='int64', freq='M')
```
So I'd like to discuss:
1. All Index should handle `fill_value`?
2. Only `PeriodIndex` should handle `fill_value` to be compat with other datetime-likes.
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} | 20 | 2016-03-15T13:51:01Z | 2016-03-17T13:54:26Z | 2016-03-17T13:54:20Z | CONTRIBUTOR | null | Added if condition for fillna when ndim==3 (for Panel) Issue: https://github.com/pydata/pandas/issues/12624
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| null | 4 | 2016-03-15T15:38:33Z | 2021-04-23T01:17:30Z | 2021-04-23T01:17:30Z | CONTRIBUTOR | null | We now have `codecov` [here](https://codecov.io/github/pydata/pandas) running on every build. You can get the browser extension to look at coverage in [github](https://github.com/codecov/browser-extension#codecov-extension)
- [x] make sure extension is working
- [ ] doc on how to use this / look at coverage
- [x] put additional excludes in `.coveragerc` to exclude things we don't care (#12721)
- [ ] increase coverage!
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} | 2 | 2016-03-15T21:19:19Z | 2016-03-17T14:17:55Z | 2016-03-17T14:17:46Z | MEMBER | null | - [x] closes #12620
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
@jreback 's comment on #12620:
> should be handled at a slightly higher level.
I added number of `dtype` check logic in `tseries/common/_concat_compat`. But it should work on `base/common/_concat_compat`. Lmk which is preferable.
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} | 3 | 2016-03-16T03:03:18Z | 2016-03-16T13:59:51Z | 2016-03-16T13:59:42Z | CONTRIBUTOR | null | Allows user to pass dtype='category' when creating a Series with Categorical, but produces a ValueError is any non-categorical dtype is passed.
- [x] closes #12574
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 14 | 2016-03-16T06:18:29Z | 2016-04-30T15:59:50Z | 2016-04-07T03:38:24Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
It seems misleading to have the `out` parameter in the function signature when it is not used:
```
pd.Series.argmin(self, axis=None, out=None, skipna=True)
pd.Series.argmax(self, axis=None, out=None, skipna=True)
```
(unlike the `numpy.argmin/argmax` methods where `out` can be used)
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| null | 5 | 2016-03-16T12:05:05Z | 2019-08-26T06:54:18Z | 2019-08-26T06:54:18Z | NONE | null | Hi everyone!
Sorry if this is not the correct place to write this, I'm new to GitHub.
I'm really enjoying pandas but I think there is a basic feature it does not implement, which is
easily color by factor when plotting a dataframe. At least I didn't be able to find how this could be done in a "Pythonic" way.
Say we have a dataframe with data records from weather stations. Different columns have the location of hundreds of stations and rows have measurements for different days of the month (say 30 rows). There is an additional column "Continent" that points which continent each weather station belongs to. Or maybe a separated Series object "as.type('categorical')"
The point is that it should be easy to plot this dataframe, keeping my data separated while coloring and labeling in legend with different colors for different groups, in this case 5 continents, so 5 colors and 5 legend labels.
I'm able to do this with some code, but I think it would be nice to have a feature like:
df.plot(color_by_group=df.Continent) or similar.
PD: Sorry if this, or anything really similar is already implemented.
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| null | 9 | 2016-03-16T13:04:54Z | 2021-04-23T01:18:20Z | null | CONTRIBUTOR | null | xref https://github.com/pydata/pandas/pull/12578
so its pretty easy to add (and eventually deprecate) certain top-level functions that could/should be methods. Promotes a cleaner syntax and method chaining.
In 0.18.1 would propose adding these, deprecating in 0.19.0.
This would eliminate the ability to use directly with `np.ndarrays`, but I view this is as a positive (note we effectively did this for the `.rolling/expanding/ewm` with `np.ndarrays` as well, though deprecated for a while).
- [ ] `pd.crosstab` (DataFrame)
- [x] `pd.melt` (DataFrame) (PR #15521); added to ``DataFrame``
- [ ] `pd.get_dummies` (both)
- [ ] `pd.cut/qcut` (only on Series)
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if someone can take a look and see when this was added?
I don't think this is useful at all. Let's just deprecate and remove this function entirely. Could always add a `Period.now()`.
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} | 3 | 2016-03-16T13:19:48Z | 2016-04-17T14:31:40Z | 2016-04-17T14:31:40Z | CONTRIBUTOR | null | xref #12614
```
In [1]: df = pd.DataFrame({'a': [1, 2, 2, 2, 2, np.nan],'b': [3, 3, 4, 4, 4, 4]})
In [2]: pd.crosstab(df.a, df.b, margins=True, dropna=True)
Out[2]:
b 3 4 All
a
1.0 1 0 1
2.0 1 3 4
All 2 3 5
In [3]: pd.crosstab(df.a, df.b, margins=True, dropna=False)
KeyError: 'Level None not found'
```
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| null | 5 | 2016-03-16T15:18:46Z | 2016-03-16T21:09:48Z | 2016-03-16T20:40:49Z | CONTRIBUTOR | null | - [+] closes #12642
- [+] tests added / passed
- [+] passes `git diff upstream/master | flake8 --diff`
- [+] whatsnew entry
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} | 12 | 2016-03-16T15:22:15Z | 2016-05-01T15:11:59Z | 2016-05-01T15:11:59Z | MEMBER | null | A recent spate of issues/PR's stemming from calling functions defined in numpy's `fromnumeric.py` module <a href="https://github.com/numpy/numpy/blob/master/numpy/core/fromnumeric.py">here</a> that have identically-named but differently implemented methods/functions in `pandas` is indicative of a much larger compatibility issue between the two libraries with this module. A thorough overview of all of the functions from the `fromnumeric.py` module and cross-referencing them to implementations in `pandas` is needed to avoid similar issues.
Relevant PRs:
#12413 (issue: #12238)
#12603 (issue: #12600)
#12638
<a href="https://github.com/numpy/numpy/pull/7325">#7325</a> (from `numpy`)
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} | 5 | 2016-03-16T16:46:34Z | 2016-03-16T17:42:38Z | 2016-03-16T17:10:31Z | CONTRIBUTOR | null | This is not a real 'bug' maybe? But it can be annoying.
xref #12614. Independent of issue #12642.
#### Code Sample
```
a = np.array(['foo', 'foo', 'foo', 'bar', 'bar', 'foo', 'foo'], dtype=object)
b = np.array(['one', 'one', 'two', 'one', 'two', 'two', 'two'], dtype=object)
c = np.array(['dull', 'dull', 'dull', 'dull', 'dull', 'shiny', 'shiny'], dtype=object)
res_dropnaF = pd.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c'], dropna=False)
res_dropnaT = pd.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c'], dropna=True)
print res_dropnaF, '\n\n', res_dropnaT
```
Results:
`res_dropnaF` shows no`columns.names` (because they're removed) while `res_dropnaT` does.
```
one two
dull shiny dull shiny
a
bar 1 0 1 0
foo 2 0 1 2
b one two
c dull dull shiny
a
bar 1 1 0
foo 2 1 2
```
#### Expected Output
Extra option or consistency on column names.
Should be fixed by working on `pivot_table(): if not dropna`
#### output of `pd.show_versions()`
## INSTALLED VERSION:
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: 0.17.1
nose: 1.3.3
pip: 8.1.0
setuptools: 18.3.1
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.1.0
sphinx: None
patsy: 0.2.1
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.3.1
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: None
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| null | 7 | 2016-03-16T17:52:07Z | 2016-03-18T19:50:04Z | 2016-03-18T19:50:04Z | NONE | null | Current method is using read_table/read_csv and placing logic in sep field is a bruteforce way to process log files, but isn't clean as you will get two padded columns of NaN at the start and end and does not handle parameter of regex for column names.
#### Code Sample, a copy-pastable example if possible
df = pd.read_table("file.log",
sep=r'^(?P<datetime>[\d-]+? [\d:]+?) (?P<level>.+?) [(?P<name>.+?)] ((?P<filename>.+?):(?P<lineno>\d+?)) - (?P<message>.*?)$',
)
df currently would produce...
| | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 0 | NaN | 2015-01-01 00:00:00 | INFO | main | main.java | 10 | Hello World | NaN |
| 1 | NaN | 2015-01-01 00:00:00 | ERROR | main | main.java | 11 | Ouch! | NaN |
#### Expected Output
| | datetime | level | name | filename | lineno | message |
| --- | --- | --- | --- | --- | --- | --- |
| 0 | 2015-01-01 00:00:00 | INFO | main | main.java | 10 | Hello World |
| 1 | 2015-01-01 00:00:00 | ERROR | main | main.java | 11 | Ouch! |
#### 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 63 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.0
setuptools: 20.2.2
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: 0.7.1
IPython: 4.1.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.0
pytz: 2016.1
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.3
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.2
lxml: 3.5.0
bs4: 4.4.1
html5lib: 0.9999999
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.12
pymysql: 0.7.1.None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
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} | 12 | 2016-03-16T20:44:32Z | 2016-04-22T15:09:57Z | 2016-04-22T15:09:57Z | NONE | null | #### Code Sample, a copy-pastable example if possible
df = pd.read_sas('infilename.sas7bdat')
#### Expected Output
to read a sas7bdat file into a pandas data frame.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.0
setuptools: 20.2.2
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.0.3
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.0
pytz: 2016.1
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: None
psycopg2: None
jinja2: 2.8
## boto: 2.39.0
### Error seen
TypeError Traceback (most recent call last)
<ipython-input-9-3046a8f15981> in <module>()
----> 1 pd.read_sas('mydatainfo.sas7bdat')
C:\Anaconda\lib\site-packages\pandas\io\sas\sasreader.py in read_sas(filepath_or_buffer, format, index, encoding, chunksize, iterator)
52 reader = SAS7BDATReader(filepath_or_buffer, index=index,
53 encoding=encoding,
---> 54 chunksize=chunksize)
55 else:
56 raise ValueError('unknown SAS format')
C:\Anaconda\lib\site-packages\pandas\io\sas\sas7bdat.py in **init**(self, path_or_buf, index, convert_dates, blank_missing, chunksize, encoding)
234 self._path_or_buf = open(self._path_or_buf, 'rb')
235
--> 236 self._get_properties()
237 self._parse_metadata()
238
C:\Anaconda\lib\site-packages\pandas\io\sas\sas7bdat.py in _get_properties(self)
333 self.os_name = buf.rstrip(b'\x00 ').decode()
334 else:
--> 335 buf = self._path_or_buf.read(_os_maker_offset, _os_maker_length)
336 self.os_name = buf.rstrip(b'\x00 ').decode()
337
TypeError: read() takes at most 1 argument (2 given)
---
## Tracking down the error:
It looks like line 335 should either be set up to read a single length from the _path_or_buf bytestream something like:
`buf = self._path_or_buf.read(_os_maker_length)`
or replace it with something like this
`buf = self._read_bytes(_os_maker_offset, _os_maker_length)`
---
*\* note i tried this under python3.5 and got the same error.
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} | 39 | 2016-03-16T23:09:33Z | 2021-04-23T01:19:29Z | null | NONE | null | Unless I am missing something obvious, we can do this:
`df.groupby('x').cumcount()`
but not:
`df.cumcount()`
Obviously, there are workarounds, but seems like we should be able to do on a dataframe/series if we can do on a groupby object?
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} | 26 | 2016-03-17T02:28:38Z | 2021-05-22T18:24:52Z | null | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd
s = pd.Series(['2012-01-01 10:00', '2012-02-10 23:20', '2012-03-22 12:40', '2012-05-02 02:00', '2012-06-11 15:20', '2012-07-22 04:40','2012-08-31 18:00', '2012-10-11 07:20', '2012-11-20 20:40', '2012-12-31 10:00'])
print s
0 2012-01-01 10:00
1 2012-02-10 23:20
2 2012-03-22 12:40
3 2012-05-02 02:00
4 2012-06-11 15:20
5 2012-07-22 04:40
6 2012-08-31 18:00
7 2012-10-11 07:20
8 2012-11-20 20:40
9 2012-12-31 10:00
dtype: object
print pd.to_datetime(s, format='%Y-%m-%d', errors='raise', infer_datetime_format=False, exact=True)
0 2012-01-01 10:00:00
1 2012-02-10 23:20:00
2 2012-03-22 12:40:00
3 2012-05-02 02:00:00
4 2012-06-11 15:20:00
5 2012-07-22 04:40:00
6 2012-08-31 18:00:00
7 2012-10-11 07:20:00
8 2012-11-20 20:40:00
9 2012-12-31 10:00:00
dtype: datetime64[ns]
```
#### Expected Output
A ValueError exception because string `'2012-01-01 10:00'` (and all others of this series) does not comply with format string `'%Y-%m-%d'` and exact conversion is required.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 15.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
pandas: 0.17.1
nose: 1.3.6
pip: 8.0.3
setuptools: 18.0
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.16.0
statsmodels: None
IPython: 3.0.0
sphinx: None
patsy: None
dateutil: 2.5.0
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.4.3
openpyxl: 2.2.2
xlrd: 0.9.3
xlwt: 0.8.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: None
html5lib: None
httplib2: 0.9.1
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
Jinja2: None
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} | 5 | 2016-03-17T02:29:44Z | 2016-04-17T14:31:42Z | 2016-04-17T14:31:40Z | CONTRIBUTOR | null | - [X] closes #12133
- [X] closes #12642, closes #12327 (replace PR)
- [X] tests added / passed
- [X] passes `git diff upstream/master | flake8 --diff`
- [X] whatsnew entry
Bug fixed from pull request #12327.
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} | 2 | 2016-03-17T02:42:05Z | 2016-12-14T14:54:21Z | 2016-12-14T14:54:21Z | CONTRIBUTOR | null | xref #12648
- cross-link doc-strings for .cum\* to the .expanding().\* (for sum,prod,max,min, which are only supported).
- show an example (in computation.rst wher eexpanding is shown? or basics.rst (where cum\* are shown)) how these are related (w.r.t. NaN filling).
```
In [7]: s = Series([1,2,np.nan,3,np.nan,4])
In [8]: s.cumsum()
Out[8]:
0 1.0
1 3.0
2 NaN
3 6.0
4 NaN
5 10.0
dtype: float64
In [9]: s.expanding().sum()
Out[9]:
0 1.0
1 3.0
2 3.0
3 6.0
4 6.0
5 10.0
dtype: float64
In [10]: s.cumsum().ffill()
Out[10]:
0 1.0
1 3.0
2 3.0
3 6.0
4 6.0
5 10.0
dtype: float64
```
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} | 6 | 2016-03-17T03:06:59Z | 2021-05-12T13:27:55Z | 2021-05-12T13:27:55Z | NONE | null | Why doesn't applying the `addColumn` function to a `DataFrameGroupby` object return the expected output below?
#### Code Sample, a copy-pasteable example if possible
```
import pandas as pd
df = pd.DataFrame({
('C', 'julian'): [0.258185, 0.52591899999999991, 0.17491099999999998, 0.94083099999999997, 0.70193700000000003, 0.189361, 0.90364500000000003, 0.56848199999999993, 0.44919799999999993, 0.39054899999999998],
('B', 'geoffrey'): [0.27970200000000001, 0.54119799999999996, 0.36436499999999999, 0.73802900000000005, 0.85527000000000009, 0.37441099999999999, 0.87378500000000003, 0.062140000000000001, 0.008404, 0.171458],
('A', 'julian'): [0.20408199999999999, 0.263235, 0.196243, 0.52878500000000006, 0.85351699999999997, 0.23979699999999998, 0.98073399999999999, 0.59194199999999997, 0.81816699999999998, 0.21742399999999998],
('B', 'julian'): [0.79572500000000002, 0.507324, 0.65340799999999999, 0.65416000000000007, 0.803087, 0.94354400000000005, 0.85009699999999988, 0.56629799999999997, 0.28205000000000002, 0.47193299999999999],
('A', 'geoffrey'): [0.073676000000000005, 0.096733, 0.028613, 0.831569, 0.26324999999999998, 0.069519000000000011, 0.29041400000000001, 0.088387000000000007, 0.061483000000000003, 0.42760200000000004],
('C', 'geoffrey'): [0.25811200000000001, 0.75765199999999999, 0.92473300000000003, 0.29447299999999998, 0.26469799999999999, 0.84664699999999993, 0.11871300000000001, 0.87206399999999995, 0.65837000000000001, 0.23442600000000002]},
columns=pd.MultiIndex.from_tuples([('A','julian'),('A','geoffrey'), ('B','julian'),('B','geoffrey'), ('C','julian'),('C','geoffrey')]))
def addColumn(grouped):
name = grouped.columns[0][1]
grouped['sum', name] = grouped.sum(axis=1)
#print(grouped)
return grouped
result = df.groupby(level=1, axis=1).apply(addColumn)
```
#### Expected Output
```
A B C sum A B C sum
geoffrey geoffrey geoffrey geoffrey julian julian julian julian
0 0.073676 0.279702 0.258112 0.611491 0.204082 0.795725 0.258185 1.257992
1 0.096733 0.541198 0.757652 1.395584 0.263235 0.507324 0.525919 1.296478
2 0.028613 0.364365 0.924733 1.317710 0.196243 0.653408 0.174911 1.024561
3 0.831569 0.738029 0.294473 1.864071 0.528785 0.654160 0.940831 2.123776
4 0.263250 0.855270 0.264698 1.383219 0.853517 0.803087 0.701937 2.358542
5 0.069519 0.374411 0.846647 1.290578 0.239797 0.943544 0.189361 1.372703
6 0.290414 0.873785 0.118713 1.282912 0.980734 0.850097 0.903645 2.734476
7 0.088387 0.062140 0.872064 1.022590 0.591942 0.566298 0.568482 1.726721
8 0.061483 0.008404 0.658370 0.728257 0.818167 0.282050 0.449198 1.549415
9 0.427602 0.171458 0.234426 0.833486 0.217424 0.471933 0.390549 1.079906
```
#### output of `pd.show_versions()`
> ## INSTALLED VERSIONS
>
> commit: None
> python: 3.4.2.final.0
> python-bits: 64
> OS: Windows
> OS-release: 7
> machine: AMD64
> processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
> byteorder: little
> LC_ALL: None
> LANG: None
>
> pandas: 0.17.1
> nose: None
> pip: None
> setuptools: 15.0
> Cython: 0.22
> numpy: 1.9.3
> scipy: 0.16.0c1
> statsmodels: 0.6.1
> IPython: 4.0.0
> sphinx: None
> patsy: 0.4.0
> dateutil: 2.4.2
> pytz: 2015.2
> blosc: None
> bottleneck: None
> tables: None
> numexpr: None
> matplotlib: 1.4.3
> openpyxl: 1.8.6
> xlrd: 0.9.3
> xlwt: None
> xlsxwriter: 0.7.3
> lxml: 3.4.2
> bs4: 4.1.0
> html5lib: 0.999
> httplib2: 0.9.1
> apiclient: None
> sqlalchemy: None
> pymysql: None
> psycopg2: None
> Jinja2: 2.8
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} | 7 | 2016-03-17T03:13:19Z | 2021-09-19T08:32:04Z | 2021-09-19T08:32:03Z | CONTRIBUTOR | null | xref https://github.com/pydata/pandas/issues/8662
xref #12652
This makes pandas code jump thru hoops and is a complete anti-pattern for pandas. let's nuke it. users should _never_ do this.
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} | 11 | 2016-03-17T04:39:45Z | 2019-08-30T07:25:18Z | 2016-04-22T15:09:57Z | NONE | null | Very excited to have this new feature in Pandas. I have a few comments to share:
1. `pd.read_sas()` doesn't read SAS date variable correctly (this is noted in the doc). Dates are read as `numpy.float64`. Note in SAS, dates are recorded as numbers relative to 1960-1-1. It would be helpful to allow some sort of arguments to parse the date variable correctly.
2. Moreover, SAS has some special missing variables such as `.B` or `.R`. I wonder how are these cases treated?
3. Not nearly as fast as `read_csv()`. To read a 700MB SAS data. The time is
```
CPU times: user 1min 47s, sys: 955 ms, total: 1min 48s
Wall time: 1min 48s
```
The time for the same CSV file (I covered the same file to CSV using SAS) is
```
CPU times: user 3.93 s, sys: 343 ms, total: 4.28 s
Wall time: 4.29 s
```
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} | 1 | 2016-03-17T11:41:50Z | 2016-04-01T13:02:56Z | 2016-04-01T13:02:56Z | CONTRIBUTOR | null | According to the 0.17.1 release notes:
> pd.read_\* functions can now also accept pathlib.Path, or py._path.local.LocalPath objects for the filepath_or_buffer argument. (#11033)
Doing so with read_excel gives `ValueError: Must explicitly set engine if not passing in buffer or path for io.`
See also #11773
#### output of `pd.show_versions()`
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.0
setuptools: 20.2.2
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: 0.7.1
IPython: 4.1.2
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: None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: 2.8
boto: 2.39.
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} | 30 | 2016-03-17T12:55:37Z | 2016-04-22T15:11:03Z | 2016-04-22T15:09:57Z | CONTRIBUTOR | null | closes #12659
closes #12654
closes #12647
closes #12809
Major performance improvements through use of Cython
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| null | 1 | 2016-03-17T13:05:42Z | 2016-03-17T13:20:24Z | 2016-03-17T13:20:07Z | CONTRIBUTOR | null | (pandas versions: 0.17.0, 0.18.0)
It seems that `Series.replace` is orders of magnitude slower than `Series.map` when called with a dict, and consumes enormous amounts of RAM:
``` python
>>> np.random.seed(0)
>>> s = pd.Series(np.random.randint(0, 10000, 1000000))
>>> r = {np.random.randint(0, 10000): np.random.randint(10000) for _ in range(1000)}
>>> assert (s.map(r).fillna(s) == s.replace(r)).all()
>>> %timeit s.replace(r)
1 loop, best of 3: 1.63 s per loop
>>> %timeit s.map(r).fillna(s)
10 loops, best of 3: 26.6 ms per loop
```
Memory stats not provided here but I've seen it explode (e.g. use 60+ GB RAM).
An old issue, not sure if related: #6697.
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} | 7 | 2016-03-17T13:30:36Z | 2016-04-22T15:14:34Z | 2016-04-22T15:14:34Z | CONTRIBUTOR | null | this fixes a crasher I had in one of my files using SAS7BDATReader.
cc: @kshedden
self._path_or_buf.read is an obvious mistake
also added + total_align to _os_name_offset and _os_make_offset as they are
present in the original code from Jared Hobbs:
https://bitbucket.org/jaredhobbs/sas7bdat/src/da1faa90d0b15c2c97a2a8eb86c91c58081bdd86/sas7bdat.py?fileviewer=file-view-default#sas7bdat.py-1450
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} | 2 | 2016-03-17T13:50:50Z | 2016-04-22T15:09:57Z | 2016-04-22T15:09:57Z | CONTRIBUTOR | null | cc: @kshedden
#### Code Sample, a copy-pastable example if possible
https://github.com/pydata/pandas/blob/8857008178eb90b59ca67874507ef860b3faf88f/pandas/io/tests/sas/data/test17.sas7bdat
Do you want a PR with this test case?
#### Expected Output
pandas reads the file without crashing :)
I tracked the problem to _process_columntext_subheader. It seems like there is an encoding issue...
You might need to apply PR #12658 first to see this issue.
FWIW, here is an AWFUL HACK to make pandas read the file "almost" correctly (I get column names as bytes instead of str in this case).
``` diff
@@ -524,17 +524,17 @@ class SAS7BDATReader(BaseIterator):
offset += self._int_length
text_block_size = self._read_int(offset, _text_block_size_length)
buf = self._read_bytes(offset, text_block_size)
- self.column_names_strings.append(
- buf[0:text_block_size].rstrip(b"\x00 ").decode())
+ self.column_names_strings.append(buf)
if len(self.column_names_strings) == 1:
column_name = self.column_names_strings[0]
compression_literal = ""
for cl in _compression_literals:
+ cl = bytes(cl, 'ascii')
if cl in column_name:
compression_literal = cl
self.compression = compression_literal
offset -= self._int_length
```
#### output of `pd.show_versions()`
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.0
setuptools: 20.2.2
Cython: None
numpy: 1.10.4
scipy: None
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: 1.3.1
patsy: None
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: None
xlsxwriter: 0.8.4
lxml: 3.5.0
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
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} | 7 | 2016-03-17T14:17:07Z | 2019-07-12T16:01:02Z | 2019-07-12T16:01:02Z | NONE | null | Hello,
I know it is [well documented](http://pandas.pydata.org/pandas-docs/stable/advanced.html#the-need-for-sortedness-with-multiindex) that MultiIndex DataFrames need to be sorted to use slicing, and that is fine. Even if you forget this, in most cases (for example when using .loc with a slicer) Pandas gives a helpful error message when you try to call it on an unsorted DataFrame, which makes it easy to spot the mistake and add the necessary sorting. However when simply using .loc without a slicer, the same KeyError exception is raised without an error message, which looks like as if it was a legit key error.
#### Code Sample, a copy-pastable example if possible
Create a test DataFrame
``` python
iterables = [['a', 'b'], [2, 1]]
columns = pd.MultiIndex.from_product(iterables, names=['col1', 'col2'])
rows = pd.MultiIndex.from_product(iterables, names=['row1', 'row2'])
df = pd.DataFrame(np.random.randn(4, 4), index=rows, columns=columns)
print(df)
```
```
col1 a b
col2 2 1 2 1
row1 row2
a 2 -1.285010 0.183851 -1.180964 0.885343
1 0.213501 0.479927 0.142614 0.064209
b 2 0.250557 -0.612791 -0.275680 -0.134086
1 -0.853687 -2.397638 0.940984 1.133747
```
Try to call .loc without a slicer
``` python
df.loc['a', 'b']
```
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-28-b77cac191687> in <module>()
----> 1 df.loc['a', 'b']
/opt/conda/lib/python3.4/site-packages/pandas/core/indexing.py in __getitem__(self, key)
1223 def __getitem__(self, key):
1224 if type(key) is tuple:
-> 1225 return self._getitem_tuple(key)
1226 else:
1227 return self._getitem_axis(key, axis=0)
/opt/conda/lib/python3.4/site-packages/pandas/core/indexing.py in _getitem_tuple(self, tup)
736 def _getitem_tuple(self, tup):
737 try:
--> 738 return self._getitem_lowerdim(tup)
739 except IndexingError:
740 pass
/opt/conda/lib/python3.4/site-packages/pandas/core/indexing.py in _getitem_lowerdim(self, tup)
849 ax0 = self.obj._get_axis(0)
850 if isinstance(ax0, MultiIndex):
--> 851 result = self._handle_lowerdim_multi_index_axis0(tup)
852 if result is not None:
853 return result
/opt/conda/lib/python3.4/site-packages/pandas/core/indexing.py in _handle_lowerdim_multi_index_axis0(self, tup)
831 ax0 = self.obj._get_axis(0)
832 if not ax0.is_lexsorted_for_tuple(tup):
--> 833 raise e1
834
835 return None
/opt/conda/lib/python3.4/site-packages/pandas/core/indexing.py in _handle_lowerdim_multi_index_axis0(self, tup)
820 try:
821 # fast path for series or for tup devoid of slices
--> 822 return self._get_label(tup, axis=0)
823 except TypeError:
824 # slices are unhashable
/opt/conda/lib/python3.4/site-packages/pandas/core/indexing.py in _get_label(self, label, axis)
84 raise IndexingError('no slices here, handle elsewhere')
85
---> 86 return self.obj._xs(label, axis=axis)
87
88 def _get_loc(self, key, axis=0):
/opt/conda/lib/python3.4/site-packages/pandas/core/generic.py in xs(self, key, axis, level, copy, drop_level)
1482 if isinstance(index, MultiIndex):
1483 loc, new_index = self.index.get_loc_level(key,
-> 1484 drop_level=drop_level)
1485 else:
1486 loc = self.index.get_loc(key)
/opt/conda/lib/python3.4/site-packages/pandas/core/index.py in get_loc_level(self, key, level, drop_level)
5553 key = tuple(self[indexer].tolist()[0])
5554
-> 5555 return (self._engine.get_loc(_values_from_object(key)),
5556 None)
5557 else:
pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3979)()
pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3843)()
pandas/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12265)()
pandas/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12216)()
KeyError: ('a', 'b')
```
Make the same call after setting the sortlevel
``` python
df2 = df.sortlevel(0)
print(df2.loc['a', 'b'])
```
```
col2 2 1
row2
1 0.142614 0.064209
2 -1.180964 0.885343
```
#### Expected Output
The same helpful error message, regardless of using or not using an explicit slicer in the .loc query.
```
KeyError: 'MultiIndex Slicing requires the index to be fully lexsorted tuple len (2), lexsort depth (1)'
```
#### output of `pd.show_versions()`
``` python
pd.show_versions()
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.4.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-27-generic
machine: x86_64
processor:
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.17.1
nose: None
pip: 8.0.2
setuptools: 20.1.1
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
IPython: 4.1.1
sphinx: None
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: None
```
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} | 1 | 2016-03-17T14:52:27Z | 2016-11-23T08:31:50Z | 2016-11-23T08:31:49Z | NONE | null | [in pandas 0.18.0: doc](http://pandas.pydata.org/pandas-docs/stable/10min.html#boolean-indexing)
"where" is mentioned but not used, should it be df.where(df > 0)? It's the same in this case but anyway..
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} | 1 | 2016-03-17T18:09:25Z | 2016-11-26T09:18:22Z | 2016-11-26T09:18:22Z | MEMBER | null | Currently, the parameter for values that one needs to insert into an array-like object such as `DataFrame` or `Series` is inconsistent across implementations. For example, `base.py` surfaces it as `key`, whereas `series.py` surfaces it as `v`.
This issue was identified while fixing an issue for `v0.18.1` (xref #12413), but in the interests of maintaining backwards compatibility for a minor release, this change should be made as early as `v0.19.0` with the following API comment:
"The signature for `searchsorted` has changed. It is now `searchsorted(self, value, side='left', sorter=None)`."
List of functions with signature inconsistencies:
`searchsorted`
`repeat`
@jreback
@jorisvandenbossche
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} | 5 | 2016-03-17T20:16:28Z | 2019-12-03T15:03:32Z | 2016-03-23T17:36:38Z | CONTRIBUTOR | null | The `copy` method of `pd.Series` and `pd.DataFrame` has a parameter `deep` which claims to [Make a deep copy, i.e. also copy data](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.copy.html). The example below seems to show that this isn't a truly deep copy (as in `from copy import deepcopy`) I can't seem to find the implementation in the source. I am wondering if this behavior below is expected, if something different should be done for `dtype=object` to make it a truly deep copy, or if we could at least add a note to the documentation that notes this behavior?
Thanks!
```
import pandas as pd
import copy
import numpy as np
"""Create a Series based on np array of objects"""
a = pd.Series(np.array([1, 'series of objects', {'first':3,'second':5}], dtype=np.object))
b = a.copy(deep=True)
c = copy.deepcopy(a)
"""The dict has length 2"""
print len(a.loc[2]), len(b.loc[2]), len(c.loc[2])
"""Remove one key from c (the deepcopy)"""
c.loc[2].pop('first')
"""Only changes c"""
print len(a.loc[2]), len(b.loc[2]), len(c.loc[2])
"""Remove one key from c (the pandas deepcopy)"""
b.loc[2].pop('first')
"""Changes a and b?"""
print len(a.loc[2]), len(b.loc[2]), len(c.loc[2])
```
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} | 3 | 2016-03-18T04:48:50Z | 2016-03-23T17:36:49Z | 2016-03-23T17:36:38Z | CONTRIBUTOR | null | - [x] closes #12663
Added note that a copy with `deep=True` does not copy the data of objects contained in underlying arrays with `dtype=object`.
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} | 10 | 2016-03-18T15:25:45Z | 2016-11-18T14:33:25Z | 2016-11-18T13:38:11Z | CONTRIBUTOR | null | xref https://github.com/pydata/pandas/pull/12551
a) change `CParserError` to an actual `ValueError`. this is used in a very small number of cases in the `engine='c'` parser. (in #12551 it will inherit from `ValueError`)
b) So we either change ALL `ValueError` to `ParserError(ValueError)` (e.g. a custom exception) and make the changes in both the `engine='c'` and `engine='python'`.
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} | 12 | 2016-03-18T17:56:48Z | 2016-08-31T16:17:14Z | 2016-08-31T16:17:14Z | NONE | null | With attached CSV-file [test_csv.txt](https://github.com/pydata/pandas/files/180140/test_csv.txt), the following code will segfault if the input dataframe has more than 17 entries:
``` python
import pandas as PD
df = PD.read_csv('test_csv.txt',
index_col=0,
converters={'xyinrad': complex,
'xyoutm': complex})
# Pivot works fine with 17 entries
df[:-1].info()
pivot = df[:-1].pivot('wave', 'xyinrad', 'xyoutm')
print("Still alive".center(50, '!'))
# Pivot segfaults with 18 entries
df.info()
print("Dave, my mind is going.".center(50, '.'))
pivot = df.pivot('wave', 'xyinrad', 'xyoutm')
```
#### Result
```
<class 'pandas.core.frame.DataFrame'>
Int64Index: 16 entries, 0 to 15
Data columns (total 3 columns):
wave 16 non-null float64
xyinrad 16 non-null complex128
xyoutm 16 non-null complex128
dtypes: complex128(2), float64(1)
memory usage: 768.0 bytes
!!!!!!!!!!!!!!!!!!!Still alive!!!!!!!!!!!!!!!!!!!!
<class 'pandas.core.frame.DataFrame'>
Int64Index: 17 entries, 0 to 16
Data columns (total 3 columns):
wave 17 non-null float64
xyinrad 17 non-null complex128
xyoutm 17 non-null complex128
dtypes: complex128(2), float64(1)
memory usage: 816.0 bytes
.............Dave, my mind is going...............
Segmentation fault (core dumped)
```
#### Expected Output
- No segfault!
- Return the pivot table
#### Output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 32
OS: Linux
OS-release: 3.16.0-38-generic
machine: i686
processor: i686
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.4
pip: 8.1.1
setuptools: 20.3
Cython: 0.21.1
numpy: 1.10.4
scipy: 0.16.1
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: None
dateutil: 2.5.0
pytz: 2016.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.8
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.4.5 (dt dec mx pq3 ext)
jinja2: 2.8
boto: None
```
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} | 9 | 2016-03-18T18:49:39Z | 2019-10-21T19:34:17Z | null | NONE | null | Date parsing is slow on dates of the form "%m.%d.%Y" compared to "%Y-%m-%d". I am using 0.17.1 so maybe it's improved in 0.18 but I didn't see anything about it. I provide a benchmark below to compare the two datetime formats.
Some observations:
- Letting pandas figure it out is unacceptably slow. In small files it won't make much of a difference but it became a massive bottleneck in an application.
- In my particular use case, I know each timestamp in my file will have the same date so it is very easy to do a string replace. And this turns out to be by far the fastest option here. Even with the actual string replacement time.
- Regex is slow. I'm sure that would have been obvious but I tried it out for comparison.
- `infer_datetime_format` is the next best option but it appears to be a massive slowdown. Compare the relative times on the large file to the relative times of the pure read on the small file and date parsing with `infer_datetime_format`.
- A custom date parser doesn't help. Not sure why it doesn't help. Maybe it's not compiled?
#### Code Sample
`test.csv` (1 million lines)
```
2016-03-12, 1
2016-03-12, 1
2016-03-12, 1
2016-03-12, 1
...
```
`test2.csv` (5000 lines)
```
03.12.2016, 1
03.12.2016, 1
03.12.2016, 1
03.12.2016, 1
...
```
```
import pandas as pd
from cStringIO import StringIO
import datetime
import re
print "Large file: pure read"
%timeit pd.read_csv('test.csv')
print "Large file: parse dates"
%timeit pd.read_csv('test.csv', parse_dates=[0])
print "Small file: pure read"
%timeit pd.read_csv('test2.csv')
print "Small file: parse dates"
%timeit pd.read_csv('test2.csv', parse_dates=[0])
print "Small file: parse dates with infer datetime format"
%timeit pd.read_csv('test2.csv', parse_dates=[0], infer_datetime_format=True)
print "Small file: string replace then parse dates "
def test_replace():
s = file('test2.csv', 'r').read()
s = s.replace('03.12.2016', '2016-03-12')
pd.read_csv(StringIO(s), parse_dates=[0])
%timeit test_replace()
print "Small file: parse dates with custom date parser"
date_parser=lambda t: datetime.datetime.strptime(t, "%m.%d.%Y")
%timeit pd.read_csv('test2.csv', parse_dates=[0], date_parser=date_parser)
print "Small file: regex sub then parse dates"
def test_re_sub():
s = file('test2.csv', 'r').read()
p = re.compile('(?P<month> \d{2}).(?P<day> \d{2}).(?P<year> \d{4})')
s = p.sub('\g<year>-\g<month>-\g<day>', s)
pd.read_csv(StringIO(s), parse_dates=[0])
%timeit test_re_sub()
```
#### Output (2012 MacBook Air)
```
Large file: pure read
1 loop, best of 3: 253 ms per loop
Large file: parse dates
1 loop, best of 3: 790 ms per loop
Small file: pure read
100 loops, best of 3: 2.9 ms per loop
Small file: parse dates
1 loop, best of 3: 1.22 s per loop
Small file: parse dates with infer datetime format
10 loops, best of 3: 32.7 ms per loop
Small file: string replace then parse dates
100 loops, best of 3: 4.97 ms per loop
Small file: parse dates with custom date parser
10 loops, best of 3: 90.6 ms per loop
Small file: regex sub then parse dates
1 loop, best of 3: 1.08 s per loop
```
#### output of `pd.show_versions()`
```
>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.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: 0.17.1
nose: 1.3.7
pip: 8.1.0
setuptools: 20.2.2
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
IPython: 4.1.2
sphinx: 1.3.6
patsy: None
dateutil: 2.5.1
pytz: 2016.1
blosc: None
bottleneck: None
tables: None
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
Jinja2: None
```
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} | 13 | 2016-03-18T20:26:34Z | 2016-03-23T20:38:52Z | 2016-03-23T20:38:17Z | CONTRIBUTOR | null | - [x] closes #11852
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 9 | 2016-03-19T04:54:25Z | 2016-04-26T23:40:37Z | 2016-04-26T23:40:37Z | CONTRIBUTOR | null | xref #12765. Let's pick up this as a test case and give a more meaningful error message. e.g. for `Window` the `win_type` is missing, so this should be the error.
```
In [15]: pd.DataFrame(np.arange(10)).rolling(2., center=True).median()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-15-73c4e557093d> in <module>()
----> 1 pd.DataFrame(np.arange(10)).rolling(2., center=True).median()
/usr/lib/python3.5/site-packages/pandas/core/window.py in median(self, **kwargs)
811 @Appender(_shared_docs['median'])
812 def median(self, **kwargs):
--> 813 return super(Rolling, self).median(**kwargs)
814
815 @Substitution(name='rolling')
/usr/lib/python3.5/site-packages/pandas/core/window.py in median(self, how, **kwargs)
592 if self.freq is not None and how is None:
593 how = 'median'
--> 594 return self._apply('roll_median_c', how=how, **kwargs)
595
596 _shared_docs['std'] = dedent("""
/usr/lib/python3.5/site-packages/pandas/core/window.py in _apply(self, func, window, center, check_minp, how, **kwargs)
473 # calculation function
474 if center:
--> 475 offset = _offset(window, center)
476 additional_nans = np.array([np.NaN] * offset)
477
/usr/lib/python3.5/site-packages/pandas/core/window.py in _offset(window, center)
1356 def _offset(window, center):
1357 if not com.is_integer(window):
-> 1358 window = len(window)
1359 offset = (window - 1) / 2. if center else 0
1360 try:
TypeError: object of type 'float' has no len()
```
Note that `pd.DataFrame(np.arange(10)).rolling(2.).median()` works fine.
pandas 0.18.0.
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| null | 7 | 2016-03-19T08:45:12Z | 2016-05-13T20:38:23Z | 2016-03-20T14:44:44Z | NONE | null | I'm attempting to install pandas via pip on Ubuntu 14.04 (inside Docker, for what it's worth, so doing it as root with no virtualenv - the Docker container serves that purpose.). I get the following exception:
```
Exception: Cython-generated file 'pandas/index.c' not found.
Cython is required to compile pandas from a development branch.
Please install Cython or download a release package of pandas.
```
This is strange, because earlier this week the exact same configuration built correctly. What's changed? Is there something else I ned to do?
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| null | 6 | 2016-03-20T06:55:34Z | 2016-05-13T23:36:43Z | 2016-05-13T23:36:43Z | CONTRIBUTOR | null | - [x] closes #12661
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Improvement in the documentation w.r.t to issue #12661
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| null | 3 | 2016-03-20T09:10:34Z | 2016-05-07T18:51:30Z | 2016-05-07T18:51:30Z | CONTRIBUTOR | null | - [x] closes #12651
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#12651
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| null | 3 | 2016-03-20T09:22:49Z | 2016-03-20T15:28:55Z | 2016-03-20T15:28:55Z | CONTRIBUTOR | null | - [ x] closes #12561
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#12561 : wrote the `.expanding` function int the `basics.rst`
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#11558 is partially fixed by #12531, this PR let `.describe()` to preserve `CategoricalIndex` info. And added explicit test cases derived from #11558.
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} | 13 | 2016-03-20T15:31:10Z | 2016-03-31T13:38:42Z | 2016-03-31T13:38:15Z | MEMBER | null | - [x] closes #12631
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 6 | 2016-03-20T17:20:34Z | 2018-02-11T00:04:50Z | 2018-02-11T00:04:50Z | NONE | null | Timestamp comparisons with pd.Timestamp.max don't seem to work correctly when there are timezones, as in the code sample below. This is with Pandas 0.16.2.
#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
import datetime
# should print True and does
print pd.Timestamp(datetime.date(2010,1,1)).tz_localize('UTC')<pd.Timestamp.max.tz_localize('UTC')
# should print True but prints False
print pd.Timestamp(datetime.date(2010,1,1)).tz_localize('US/Eastern')<pd.Timestamp.max.tz_localize('US/Eastern')
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 15.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.2
nose: None
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.1
statsmodels: None
IPython: 4.0.1
sphinx: None
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2015.7
bottleneck: 1.0.0
numexpr: 2.4.6
matplotlib: 1.5.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 14 | 2016-03-20T20:46:05Z | 2016-04-06T19:17:26Z | 2016-04-06T19:17:26Z | CONTRIBUTOR | null | https://github.com/pydata/pandas/pull/12551 and comment: https://github.com/pydata/pandas/pull/12512#discussion_r56771668
`usecols=['a',1]` should raise
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} | 11 | 2016-03-21T02:38:57Z | 2021-01-22T21:42:34Z | 2021-01-22T21:42:34Z | NONE | null | Hello,
I'm trying to make a new DataFrame that contains the value counts of a column of an existing DataFrame ([spreadsheet.xlsx](https://github.com/pydata/pandas/files/181629/spreadsheet.xlsx)), but I want the rows in the new DataFrame to be in the same order as the old one.
When I do:
``` python
import pandas as pd
df = pd.read_excel('./spreadsheet.xlsx')
print(df[0].value_counts(sort=False))
```
I get the DataFrame:
``` python
h4 8
ct1 6
f2 2
s1 2
EST2 2
f5 2
E4 8
h2 8
hd2 7
f3 2
ART1 2
s2 2
f1 2
h3 8
EST1 2
s3 2
E6 8
ART2 2
DGT2 2
ct2 6
s4 2
ct3 6
f4 2
DGT1 2
s5 2
```
When what I really want is:
``` python
h2 8
h3 8
h4 8
hd2 7
E4 8
E6 8
ct1 6
.
.
.
```
...because that's the order in which the values occur in the original DataFrame.
I can't tell how it's sorting them, but it is somehow. Is this the expected behavior?
Thanks
``` python
Installed versions:
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.3.0
machine: x86_64
processor: i386
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: None
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.0
pytz: 2016.1
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: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.0
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
```
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| null | 2 | 2016-03-21T04:55:06Z | 2016-03-21T15:55:28Z | 2016-03-21T15:55:04Z | NONE | null | Hi,
I have an issue with passing pandas.Timestamp objects as arguments to pandas.Series.between_time(start, end).
It appears that pandas.tseries.index.DatetimeIndex.indexer_between_time() tries to convert start_time and end_time arguments as strings.
The following lines give a temporary fix:
```
if not isinstance(start_time, Timestamp):
start_time = to_time(start_time)
if not isinstance(end_time, Timestamp):
end_time = to_time(end_time)
```
Thanks,
Simon
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| null | 2 | 2016-03-21T12:41:39Z | 2016-03-21T12:44:03Z | 2016-03-21T12:43:52Z | NONE | null | #### Code Sample, a copy-pastable example if possible
``` python
>>> panel.tz_localize(tz = pytz.UTC, axis=1)
>>> pp.pprint(panel.axes[1].tzinfo)
```
``` python
[Index([u'ddhm', u'ffhm', u'nnhv', u'pshv', u'tahv', u'uuhv', u'rrhs'], dtype='object'),
DatetimeIndex(['2016-03-09 12:05:05'], dtype='datetime64[ns]', name=u'date', freq=None),
DatetimeIndex(['2016-03-09 13:00:00', '2016-03-09 14:00:00',
'2016-03-09 15:00:00', '2016-03-09 16:00:00',
'2016-03-09 17:00:00', '2016-03-09 18:00:00',
'2016-03-09 19:00:00', '2016-03-09 20:00:00',
'2016-03-09 21:00:00', '2016-03-09 22:00:00',
...
'2016-03-19 03:00:00', '2016-03-19 04:00:00',
'2016-03-19 05:00:00', '2016-03-19 06:00:00',
'2016-03-19 07:00:00', '2016-03-19 08:00:00',
'2016-03-19 09:00:00', '2016-03-19 10:00:00',
'2016-03-19 11:00:00', '2016-03-19 12:00:00'],
dtype='datetime64[ns]', name=u'forecast', length=240, freq=None)]
None
```
#### Expected Output
``` python
[Index([u'ddhm', u'ffhm', u'nnhv', u'pshv', u'tahv', u'uuhv', u'rrhs'], dtype='object'),
DatetimeIndex(['2016-03-09 12:05:05+00:00'], dtype='datetime64[ns, UTC]', name=u'date', freq=None),
DatetimeIndex(['2016-03-09 13:00:00', '2016-03-09 14:00:00',
'2016-03-09 15:00:00', '2016-03-09 16:00:00',
'2016-03-09 17:00:00', '2016-03-09 18:00:00',
'2016-03-09 19:00:00', '2016-03-09 20:00:00',
'2016-03-09 21:00:00', '2016-03-09 22:00:00',
...
'2016-03-19 03:00:00', '2016-03-19 04:00:00',
'2016-03-19 05:00:00', '2016-03-19 06:00:00',
'2016-03-19 07:00:00', '2016-03-19 08:00:00',
'2016-03-19 09:00:00', '2016-03-19 10:00:00',
'2016-03-19 11:00:00', '2016-03-19 12:00:00'],
dtype='datetime64[ns]', name=u'forecast', length=240, freq=None)]
<UTC>
```
#### output of `pd.show_versions()`
http://paste.pound-python.org/show/zeZJO5S6OCJJ3Ohk3UWf/
using `panel = panel.tz_locaize()`fixes it, but there is no information about inplace operation or a return value @ http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Panel.tz_localize.html
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} | 4 | 2016-03-21T12:45:47Z | 2016-04-04T16:52:56Z | 2016-04-04T16:52:39Z | CONTRIBUTOR | null | - [x] closes #9784
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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| null | 11 | 2016-03-21T14:11:44Z | 2017-05-23T03:37:02Z | 2016-03-21T14:19:04Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
conda create -n pandas18 python=3.5 pandas xlrd ipython
```
``` python
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.18.0'
In [3]: url='https://github.com/chris1610/pbpython/raw/master/data/sales-feb-2014.xlsx'
In [4]: pd.read_excel(url, parse_dates=True, index_col='date').info()
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-4-636dbf8d885a> in <module>()
----> 1 pd.read_excel(url, parse_dates=True, index_col='date').info()
/Users/adefusco/Applications/anaconda3/envs/p18/lib/python3.5/site-packages/pandas/io/excel.py in read_excel(io, sheetname, header, skiprows, skip_footer, index_col, names, parse_cols, parse_dates, date_parser, na_values, thousands, convert_float, has_index_names, converters, engine, squeeze, **kwds)
175 convert_float=convert_float, has_index_names=has_index_names,
176 skip_footer=skip_footer, converters=converters,
--> 177 squeeze=squeeze, **kwds)
178
179
/Users/adefusco/Applications/anaconda3/envs/p18/lib/python3.5/site-packages/pandas/io/excel.py in _parse_excel(self, sheetname, header, skiprows, skip_footer, index_col, has_index_names, parse_cols, parse_dates, date_parser, na_values, thousands, convert_float, verbose, squeeze, **kwds)
308 "is not implemented")
309 if parse_dates:
--> 310 raise NotImplementedError("parse_dates keyword of read_excel "
311 "is not implemented")
312
NotImplementedError: parse_dates keyword of read_excel is not implemented
```
#### Expected Output
```
conda install pandas=0.17
```
``` python
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.17.1'
In [3]: url='https://github.com/chris1610/pbpython/raw/master/data/sales-feb-2014.xlsx'
In [4]: pd.read_excel(url, parse_dates=True, index_col='date').info()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 108 entries, 2014-02-01 09:04:59 to 2014-02-28 23:47:32
Data columns (total 6 columns):
account number 108 non-null int64
name 108 non-null object
sku 108 non-null object
quantity 108 non-null int64
unit price 108 non-null float64
ext price 108 non-null float64
dtypes: float64(2), int64(2), object(2)
memory usage: 5.9+ KB
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.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: 0.18.0
nose: None
pip: 8.1.1
setuptools: 20.3
Cython: None
numpy: 1.10.4
scipy: None
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: None
patsy: None
dateutil: 2.5.0
pytz: 2016.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
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} | 6 | 2016-03-21T15:54:42Z | 2016-06-29T12:16:22Z | 2016-06-29T12:16:22Z | NONE | null | The behaviour of the method [date_range](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.date_range.html) appears to have changed between version 17 and 19. the below example changes the starting bound of the index returned?
`print pd.date_range(start=datetime.datetime(2016,3,18,0,0), end=datetime.datetime(2016,3,22,0,0), freq='D', tz='UTC', closed='right')`
**pandas 0.17.0 Output**
DatetimeIndex(['2016-03-19', '2016-03-20', '2016-03-21', '2016-03-22'], dtype='datetime64[ns, UTC]', freq='D')
**pandas 0.18.0 Output**
DatetimeIndex(['2016-03-18', '2016-03-19', '2016-03-20', '2016-03-21', '2016-03-22'],
dtype='datetime64[ns, UTC]', freq='D')
python: 2.7.11.final.0
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} | 2 | 2016-03-22T02:01:09Z | 2016-07-05T08:37:17Z | 2016-07-05T08:37:17Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
``` python
>>> dft = pd.DataFrame(np.random.randn(100000,1),columns=['A'],index=pd.date_range('20130101',periods=100000,freq='T'))
>>> dft2 = pd.DataFrame(np.random.randn(200000,1),columns=['A'],index=pd.MultiIndex.from_product([dft.index, ['a', 'b']]))
>>> dft2.loc[pd.IndexSlice['2013-03':'2013-03',:],:]
A
2013-01-01 00:00:00 a 0.563968
2013-01-01 00:01:00 a -0.439376
2013-01-01 00:02:00 a 1.785202
2013-01-01 00:03:00 a 0.376901
2013-01-01 00:04:00 a -0.977926
2013-01-01 00:05:00 a -0.738415
2013-01-01 00:06:00 a -2.156905
2013-01-01 00:07:00 a -0.016085
2013-01-01 00:08:00 a 1.035935
2013-01-01 00:09:00 a -1.198991
2013-01-01 00:10:00 a -0.867717
2013-01-01 00:11:00 a -0.596923
2013-01-01 00:12:00 a -1.256923
2013-01-01 00:13:00 a 1.042369
2013-01-01 00:14:00 a 1.161365
2013-01-01 00:15:00 a 0.853495
2013-01-01 00:16:00 a 1.398594
2013-01-01 00:17:00 a -0.431314
2013-01-01 00:18:00 a 2.630920
2013-01-01 00:19:00 a -1.031731
2013-01-01 00:20:00 a -0.891799
2013-01-01 00:21:00 a 0.075546
2013-01-01 00:22:00 a -0.163999
2013-01-01 00:23:00 a 0.678027
2013-01-01 00:24:00 a 1.427323
2013-01-01 00:25:00 a 1.426418
2013-01-01 00:26:00 a 0.492158
2013-01-01 00:27:00 a 0.477629
2013-01-01 00:28:00 a -0.703225
2013-01-01 00:29:00 a 0.864293
... ...
2013-03-11 10:25:00 a 1.585880
b -0.595927
2013-03-11 10:26:00 a 0.259137
b -0.718385
2013-03-11 10:27:00 a -0.143240
b -0.898806
2013-03-11 10:28:00 a -0.293221
b -1.645180
2013-03-11 10:29:00 a -0.790069
b -1.075649
2013-03-11 10:30:00 a 0.368399
b 2.206858
2013-03-11 10:31:00 a 1.125287
b -0.834588
2013-03-11 10:32:00 a 0.740703
b -0.587527
2013-03-11 10:33:00 a 0.765259
b -0.662232
2013-03-11 10:34:00 a 2.138155
b -0.030379
2013-03-11 10:35:00 a -1.510801
b 1.200521
2013-03-11 10:36:00 a 0.934974
b 1.340875
2013-03-11 10:37:00 a -0.251199
b 1.728432
2013-03-11 10:38:00 a 1.651664
b -1.032225
2013-03-11 10:39:00 a -0.352417
b 0.378273
[115040 rows x 1 columns]
```
#### Expected Output
Data from 2013-01-01 through 2013-03-03 should not be present. Data from 2013-03-04 onward should not be present, either.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: 25cec3adf044982accb78384acab5df94c1435c7
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-34-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0+31.g25cec3a
nose: 1.3.7
pip: 8.0.3
setuptools: 20.1.1
Cython: 0.23.4
numpy: 1.10.4
scipy: None
statsmodels: None
xarray: None
IPython: 4.1.1
sphinx: 1.3.5
patsy: None
dateutil: 2.4.2
pytz: 2015.7
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
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| null | 22 | 2016-03-22T14:11:51Z | 2021-04-23T01:23:48Z | null | CONTRIBUTOR | null | ## Known differences between Python & C engines
Update [here](http://pandas.pydata.org/pandas-docs/stable/io.html#specifying-the-parser-engine)
### Features supported in the Python engine only
- [ ] `skipfooter` / `skip_footer` (#13349) - num of lines at the bottom of the file to skip
- [ ] sniffing (`sep=None`) - deduce the `sep` #9645
- [ ] regex `sep` - regular expression/multicharacter seperator
### Features supported in the C engine only
- [x] `dtype` - specify dtype for providing dtype or `{column_name: dtype}` (related as this is a conflicting option: https://github.com/pydata/pandas/issues/5232) (done in #14295)
- [x] `warn_bad_lines` - issue a warnings for each bad line (#15925)
- [x] `error_bad_lines` - if `False`, drop bad lines instead of raising (#15925)
- [ ] `lineterminator` - specify the line terminating character
- [x] C engine accepts `float` for `nrows` but Python engine raises #10476 (closed by #13275)
- [x] `decimal` option, #12933 (closed by #13189)
- [x] `delim_whitespace` #12958
- [x] `na_filter` #13321
- [x] `float_precision`, documented [here](https://github.com/pydata/pandas/blob/master/pandas/io/parsers.py#L1526) and [here](https://github.com/pydata/pandas/blob/master/doc/source/io.rst#specifying-method-for-floating-point-conversion), #13377
#### In C engine only (but undocumented)
- [x] `low_memory` (PR #13293)
### marked as internal on C engine only (maybe be a bit louder about this in the internal code)
- [x] `buffer_lines` #13360
### Undocumented arguments to `read_csv`
- [x] `doublequote` #13368
- [x] `compact_ints` #13323
- [x] `use_unsigned` #13323
- [x] `as_recarray` #(#13373)
- [x] `memory_map`, #7477, #13381
### Differences
- [ ] validity of `names` and its length with respect to `usecols` #16469
- [ ] different handling of `na_values` when `converters` is also present. #13302
- [ ] different handling of columns aggregated to create date columns #23845
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} | 11 | 2016-03-22T14:28:20Z | 2016-05-01T15:11:59Z | 2016-05-01T15:11:59Z | CONTRIBUTOR | null | see [here](https://travis-ci.org/pydata/pandas/jobs/117710492)
xref #12603
#12644
[numpy issue 7325](https://github.com/numpy/numpy/pull/7325)
cc @gfyoung
cc @njsmith
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} | 5 | 2016-03-22T14:35:26Z | 2016-12-13T23:12:17Z | 2016-12-13T23:11:38Z | CONTRIBUTOR | null | we have 3 copies of the compression inference routines in `parser.py, parser.py, and io/common.py`. See how much we can reduce this (_some_ is necessary as sometimes we need to actually return a file handle or need to read in and re-wrap).
xref #12175
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} | 3 | 2016-03-22T14:41:03Z | 2020-04-16T21:46:21Z | 2019-12-24T14:24:25Z | NONE | null | The "in" function does not work as expected in regard to a data frame. Let's say we have the following data frame :
`df = pd.DataFrame(np.random.randn(6,4), columns=list('ABCD'))`
We can test this data frame :
```
df is None
False
```
However we cannot test its presence in a list :
```
df in [None]
TypeError: Could not compare [None] with block values
```
We would expect to get just False in this case.
(Pandas version 0.17.1)
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} | 2 | 2016-03-22T14:55:15Z | 2018-03-26T13:22:40Z | 2018-03-26T13:22:40Z | NONE | null | Found this while working on #12136.
Rounding errors occur for timedeltas initialized using fractional microsecond values:
```
>>> pd.Timedelta('12.312us') == pd.Timedelta(12312)
False
>>> pd.Timedelta(12.312, unit='us') == pd.Timedelta(12312)
False
>>> pd.Timedelta('12.312us') == pd.Timedelta(12311)
True
```
Additionally, precision is lost when initializing timedeltas with fractional millisecond (and probably second) values:
```
>>> pd.Timedelta(0.012312, unit='ms') == pd.Timedelta(12311)
False
>>> pd.Timedelta(0.012311, unit='ms') == pd.Timedelta(12311)
False
>>> pd.Timedelta(0.012311, unit='ms') == pd.Timedelta(12000)
True
```
I believe the problem is here: https://github.com/pydata/pandas/blob/288059adfb751d52be57e444198198518d256c05/pandas/tslib.pyx#L3399
bumping up the values of `p` should probably solve this.
#### output of `pd.show_versions()`
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0+unknown
nose: 1.3.7
pip: 8.1.0
setuptools: 20.2.2
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.1.2
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: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: 0.6.7.None
psycopg2: None
jinja2: 2.8
boto: None
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} | 3 | 2016-03-22T15:26:26Z | 2021-04-23T01:24:54Z | null | NONE | null | I initially posted this on #12136, as I intended to fix it with the same patch, but I think it deserves its own issue as my initial idea for a fix may in fact change the behaviour for strings suffixed by capital `M`. Previously, those would be interpreted as minutes because of the `unit.lower()` in https://github.com/pydata/pandas/blob/master/pandas/tslib.pyx#L2820
Apparently, `timedelta_from_spec()` doesn't recognize all the abbreviations that are allowed in the `unit` parameter. Consider for example:
```
>>> pd.Timedelta('1Y')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas\tslib.pyx", line 2373, in pandas.tslib.Timedelta.__new__ (pandas\tslib.c:43241)
value = np.timedelta64(parse_timedelta_string(value, unit, False))
File "pandas\tslib.pyx", line 3010, in pandas.tslib.parse_timedelta_string (pandas\tslib.c:52603)
raise
File "pandas\tslib.pyx", line 3005, in pandas.tslib.parse_timedelta_string (pandas\tslib.c:52506)
r = timedelta_from_spec(number, frac, unit)
File "pandas\tslib.pyx", line 2822, in pandas.tslib.timedelta_from_spec (pandas\tslib.c:50601)
raise ValueError("invalid abbreviation: {0}".format(unit))
ValueError: invalid abbreviation: Y
```
while
```
>>> pd.Timedelta(1, unit='Y')
Timedelta('365 days 05:49:12')
```
Same goes for Week.
Month is slightly trickier. As noted above, we have this inconsistency that we might want to do something about:
```
>>> pd.Timedelta('1M')
Timedelta('0 days 00:01:00')
>>> pd.Timedelta(1, unit='M')
Timedelta('30 days 10:29:06')
```
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| null | 1 | 2016-03-22T15:58:55Z | 2016-03-22T16:14:03Z | 2016-03-22T16:13:59Z | CONTRIBUTOR | null | {
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} | 3 | 2016-03-22T17:32:44Z | 2021-04-23T01:25:56Z | null | NONE | null | IN:
```
import pandas as pd
df = pd.DataFrame([[{'a', 'b'}], [{'b','c'}], [{'b', 'a'}]])
df
```
OUT:
```
0
0 {a, b}
1 {c, b}
2 {a, b}
```
IN:
```
df.duplicated()
```
OUT:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-77-7cc63ba1ed41> in <module>()
----> 1 df.duplicated()
venv/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
venv/lib/python3.5/site-packages/pandas/core/frame.py in duplicated(self, subset, keep)
3100
3101 vals = (self[col].values for col in subset)
-> 3102 labels, shape = map(list, zip(*map(f, vals)))
3103
3104 ids = get_group_index(labels, shape, sort=False, xnull=False)
TypeError: type object argument after * must be a sequence, not map
```
I expect:
```
0 False
1 False
2 True
dtype: bool
```
`pd.show_versions()` output:
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.3.0-1-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: ru_RU.UTF-8
pandas: 0.18.0
nose: None
pip: 1.5.6
setuptools: 18.8
Cython: None
numpy: 1.10.4
scipy: None
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: None
patsy: None
dateutil: 2.5.1
pytz: 2016.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
```
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} | 12 | 2016-03-22T18:11:20Z | 2021-04-23T02:45:41Z | null | CONTRIBUTOR | null | It appears that `Series.argsort()` is implemented as `s.dropna().argsort().reindex(s.index, fill_value=-1) = np.argsort(s.dropna()).reindex(s.index, fill_value=-1)`.
There are two problems with this:
(a) Since the result represents integer indices into the original series `s`, the result should not have the same `index` as `s.index` -- it should either be a `Series` with index `[0, 1, ...]`, or more likely simply be a NumPy`array`;
(b) The way `NaN`s are effectively removed before calling `np.argsort()` leads to indexes that are no longer appropriate into the original `Series`, resulting in the nonsensical results shown in `[9]` and `[10]` below.
Note that: "As of NumPy 1.4.0 `argsort` works with real/complex arrays containing nan values." So it's not obvious to me that there's much to be gained from mucking around with `np.argsort(s.values)`.
```
Python 3.4.4 (v3.4.4:737efcadf5a6, Dec 20 2015, 20:20:57) [MSC v.1600 64 bit (AMD64)]
Type "copyright", "credits" or "license" for more information.
IPython 4.1.2 -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: s = pd.Series([200, 100, 400, 500, np.nan, 300], index=list('abcdef'))
In [4]: s
Out[4]:
a 200.0
b 100.0
c 400.0
d 500.0
e NaN
f 300.0
dtype: float64
In [5]: s.argsort()
Out[5]:
a 1
b 0
c 4
d 2
e -1
f 3
dtype: int64
In [6]: s.dropna().argsort().reindex(s.index, fill_value=-1)
Out[6]:
a 1
b 0
c 4
d 2
e -1
f 3
dtype: int64
In [33]: np.argsort(s.dropna()).reindex(s.index, fill_value=-1)
Out[33]:
a 1
b 0
c 4
d 2
e -1
f 3
dtype: int64
In [34]: np.argsort(s.dropna().values)
Out[34]: array([1, 0, 4, 2, 3], dtype=int64)
In [7]: np.argsort(s.values)
Out[7]: array([1, 0, 5, 2, 3, 4], dtype=int64)
In [8]: s[np.argsort(s.values)] # desired result
Out[8]:
b 100.0
a 200.0
f 300.0
c 400.0
d 500.0
e NaN
dtype: float64
In [9]: s[s.argsort()] # nonsensical result
Out[9]:
b 100.0
a 200.0
e NaN
c 400.0
f 300.0
d 500.0
dtype: float64
In [10]: s[s.argsort().values] # nonsensical result
Out[10]:
b 100.0
a 200.0
e NaN
c 400.0
f 300.0
d 500.0
dtype: float64
In [11]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.4.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 62 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3.1
Cython: None
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: None
patsy: 0.4.1
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.4
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xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 1.0b8
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
```
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} | 2 | 2016-03-22T18:41:01Z | 2016-04-10T14:22:42Z | 2016-04-10T14:22:42Z | NONE | null | With version 0.18.0 introducing RangeIndex, should RangeIndex be applied for concat when ignore_index is set to True?
`df = pd.concat([df_a, df_b], ignore_index=True)`
df.index is an instance of Int64Index, not RangeIndex currently in 0.18.0
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| null | 3 | 2016-03-22T20:52:36Z | 2016-08-31T00:52:25Z | 2016-04-10T14:22:42Z | NONE | null | - [x] closes #12695
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
Replaced generated indexes with RangeIndex, test still needed
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} | 3 | 2016-03-22T23:55:33Z | 2017-01-29T21:52:56Z | 2017-01-29T21:52:56Z | NONE | null | When I try to slice a multi indexed data frame, the slice interval is ignored for all the entries of the first level index except the last one. This only happens if the length of the slice is beyond a particular value (around 30 in the cases that I observed, but it is not the same across data frames).
This bug seems to have been introduced in version 0.17.1 as this works fine in version 0.16.4
#### Code Sample
``` python
import pandas as pd
import numpy as np
freq = ['a', 'b', 'c', 'd']
idx = pd.MultiIndex.from_product([freq, np.arange(500)])
dfmi = pd.DataFrame(np.random.randn(2000), index=idx, columns=['Test'])
sliced_df = dfmi.loc[pd.IndexSlice[:, 30:70], :]
print sliced_df.loc['a']
print sliced_df.loc['d']
```
#### Current Output
```
Test
0 -2.288252
1 0.501113
2 -0.581190
3 0.366600
.. ...
496 -1.124694
497 -0.106180
498 -0.348668
499 0.659645
[500 rows x 1 columns]
Test
30 0.079055
31 2.455371
32 0.014673
33 0.966548
.. ...
67 0.997713
68 1.235465
69 -0.320166
70 -0.968143
```
#### Expected Output
```
Test
30 -1.025443
31 -1.305710
32 0.614858
33 -0.606788
34 -0.673230
.. ...
68 -1.129218
69 -1.747830
70 -0.611186
Test
30 -0.679267
31 -0.590352
32 1.000755
33 -0.106813
34 -1.214385
.. ...
68 -1.467416
69 -0.008881
70 0.040510
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-63-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.0
setuptools: 20.2.2
Cython: 0.22.1
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.0.0-dev
sphinx: 1.4a1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.0
numexpr: 2.4.4
matplotlib: 1.5.1
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.5
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
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} | 7 | 2016-03-23T01:44:16Z | 2016-04-03T14:06:15Z | 2016-04-03T14:06:01Z | CONTRIBUTOR | null | - [x] closes #12564
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 9 | 2016-03-23T06:17:32Z | 2016-06-11T12:19:42Z | 2016-06-11T12:19:42Z | NONE | null | I ran into this issue today, and it seems like it should be a fairly common situation. I have imported two dataframes (using `pandas.read_stata`) of categorical data that I want to concatenate. One of them might not have an instance of every category that the other one has, so pandas won't concatenate. It seems like it would be more flexible if it could add all missing categories.
I know that this inflexibility is in the [documentation](http://pandas.pydata.org/pandas-docs/stable/categorical.html#merging), but I wonder why it exists. Is there a good reason why pandas shouldn't automatically append new categories as they are encountered?
#### In
```
s = pd.Series(['a', 'b'], dtype="category")
s2 = pd.Series(['a', 'c'], dtype="category")
s.append(s2)
```
#### Expected Output
```
0 a
1 b
0 a
1 c
dtype: category
Categories (3, object): [a, b, c]
```
#### Actual Output
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-40-e1bb85501be1> in <module>()
4 s = pd.Series(['a', 'b'], dtype="category")
5 s2 = pd.Series(['a', 'c'], dtype="category")
----> 6 s.append(s2)
/Users/will/anaconda/lib/python2.7/site-packages/pandas/core/series.pyc in append(self, to_append, verify_integrity)
1575 to_concat = [self, to_append]
1576 return concat(to_concat, ignore_index=False,
-> 1577 verify_integrity=verify_integrity)
1578
1579 def _binop(self, other, func, level=None, fill_value=None):
/Users/will/anaconda/lib/python2.7/site-packages/pandas/tools/merge.pyc in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)
833 verify_integrity=verify_integrity,
834 copy=copy)
--> 835 return op.get_result()
836
837
/Users/will/anaconda/lib/python2.7/site-packages/pandas/tools/merge.pyc in get_result(self)
979 # stack blocks
980 if self.axis == 0:
--> 981 new_data = com._concat_compat([x._values for x in self.objs])
982 name = com._consensus_name_attr(self.objs)
983 return (Series(new_data, index=self.new_axes[0], name=name)
/Users/will/anaconda/lib/python2.7/site-packages/pandas/core/common.pyc in _concat_compat(to_concat, axis)
2722 elif 'category' in typs:
2723 from pandas.core.categorical import _concat_compat
-> 2724 return _concat_compat(to_concat, axis=axis)
2725
2726 if not nonempty:
/Users/will/anaconda/lib/python2.7/site-packages/pandas/core/categorical.pyc in _concat_compat(to_concat, axis)
1948 for x in categoricals[1:]:
1949 if not categories.is_dtype_equal(x):
-> 1950 raise ValueError("incompatible categories in categorical concat")
1951
1952 # we've already checked that all categoricals are the same, so if their
ValueError: incompatible categories in categorical concat
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.4
pip: 8.1.0
setuptools: 20.2.2
Cython: 0.21
numpy: 1.10.2
scipy: 0.16.1
statsmodels: 0.5.0
xarray: None
IPython: 4.0.1
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.5.0
pytz: 2016.1
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.5.0
openpyxl: 1.8.5
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
```
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} | 1 | 2016-03-23T13:43:00Z | 2017-05-22T17:49:50Z | 2017-05-22T17:49:50Z | CONTRIBUTOR | null | http://pandas.pydata.org/pandas-docs/stable/comparison_with_sas.html (at the end)
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} | 1 | 2016-03-23T14:33:44Z | 2016-04-03T17:52:38Z | 2016-04-03T17:52:22Z | NONE | null | Hi,
there seems to be a bug in DataFrame.drop() when the DataFrame has a non-unique MultiIndex and one of the levels is a DatetimeIndex (labels of which I would like to pass to the drop-method)
#### Code Sample
```
import pandas as pd
from pandas import DataFrame
import numpy as np
# Prepare DataFrame
idx = pd.Index([0, 0, 1, 1, 1, 2, 3, 4, 4, 5], name='id')
idxdt = pd.to_datetime(['201603231200',
'201603231200',
'201603231300',
'201603231300',
'201603231400',
'201603231400',
'201603231500',
'201603231600',
'201603231600',
'201603231700'])
df = DataFrame(np.arange(30).reshape(10, 3), columns=list('abc'), index=idx)
df['tstamp'] = idxdt
df = df.set_index('tstamp', append=True)
print df
# Drop the following timestamp
ts = pd.Timestamp('201603231600')
df = df.drop(ts, level='tstamp')
print df
```
#### Expected Output
```
a b c
id tstamp
0 2016-03-23 12:00:00 0 1 2
2016-03-23 12:00:00 3 4 5
1 2016-03-23 13:00:00 6 7 8
2016-03-23 13:00:00 9 10 11
2016-03-23 14:00:00 12 13 14
2 2016-03-23 14:00:00 15 16 17
3 2016-03-23 15:00:00 18 19 20
4 2016-03-23 16:00:00 21 22 23
2016-03-23 16:00:00 24 25 26
5 2016-03-23 17:00:00 27 28 29
a b c
id tstamp
0 2016-03-23 12:00:00 0 1 2
2016-03-23 12:00:00 3 4 5
1 2016-03-23 13:00:00 6 7 8
2016-03-23 13:00:00 9 10 11
2016-03-23 14:00:00 12 13 14
2 2016-03-23 14:00:00 15 16 17
3 2016-03-23 15:00:00 18 19 20
5 2016-03-23 17:00:00 27 28 29
```
#### Current Output
```
a b c
id tstamp
0 2016-03-23 12:00:00 0 1 2
2016-03-23 12:00:00 3 4 5
1 2016-03-23 13:00:00 6 7 8
2016-03-23 13:00:00 9 10 11
2016-03-23 14:00:00 12 13 14
2 2016-03-23 14:00:00 15 16 17
3 2016-03-23 15:00:00 18 19 20
4 2016-03-23 16:00:00 21 22 23
2016-03-23 16:00:00 24 25 26
5 2016-03-23 17:00:00 27 28 29
a b c
id tstamp
0 2016-03-23 12:00:00 0 1 2
2016-03-23 12:00:00 3 4 5
1 2016-03-23 13:00:00 6 7 8
2016-03-23 13:00:00 9 10 11
2016-03-23 14:00:00 12 13 14
2 2016-03-23 14:00:00 15 16 17
3 2016-03-23 15:00:00 18 19 20
4 2016-03-23 16:00:00 21 22 23
2016-03-23 16:00:00 24 25 26
5 2016-03-23 17:00:00 27 28 29
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 0.6
Cython: 0.23.3
numpy: 1.10.4
scipy: 0.16.0
statsmodels: 0.6.1
xarray: None
IPython: 4.0.3
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: 2.4.4
matplotlib: 1.5.1
openpyxl: 2.0.2
xlrd: None
xlwt: 1.0.0
xlsxwriter: None
lxml: 3.5.0
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.8
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.7.3
boto: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12702 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12702/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12702/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12702/events | https://github.com/pandas-dev/pandas/pull/12702 | 143,019,857 | MDExOlB1bGxSZXF1ZXN0NjM5MTY3MDc= | 12,702 | BUG: concatenation with a coercable dtype was too aggressive | {
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} | 0 | 2016-03-23T16:59:58Z | 2016-03-23T17:56:45Z | 2016-03-23T17:56:45Z | CONTRIBUTOR | null | closes #12411
closes #12045
closes #11594
closes #10571
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https://api.github.com/repos/pandas-dev/pandas/issues/12703 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12703/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12703/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12703/events | https://github.com/pandas-dev/pandas/issues/12703 | 143,027,049 | MDU6SXNzdWUxNDMwMjcwNDk= | 12,703 | df.sample() much slower than df.iloc[randint] | {
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| null | 1 | 2016-03-23T17:28:20Z | 2016-03-23T17:50:50Z | 2016-03-23T17:50:38Z | NONE | null | It seems like the Dataframe sample function is much slower than directly selecting a randomly indexed row:
import pandas as pd
import numpy as np
from time import time
> > > test_df = pd.DataFrame(np.random.randint(0, 100, size = (400, 600)))
> > > def f():
> > > ... start = time()
> > > ... for _ in range(100):
> > > ... test_df.sample()
> > > ... print(time() - start)
> > > ...
> > > def g():
> > > ... start = time()
> > > ... for _ in range(100):
> > > ... test_df.iloc[np.random.randint(0, test_df.shape[0])]
> > > ... print(time() - start)
> > > ...
> > > f()
> > > 0.571173906326
> > > g()
> > > 0.0156011581421
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