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| null | 12 | 2016-02-21T07:16:43Z | 2016-05-07T20:42:54Z | 2016-05-07T17:45:31Z | NONE | null | - [x] closes #8162 and #10816
- [x] tests added
- [x] tests passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Still missing are groupby support (#5677) and `.loc` support.
Also, I wasn't sure if this deserves more than just a line in whatsnew, so I kept it small for now.
With a standard index:
```
idx = pd.Index(list('abc'), name='idx')
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, index=idx)
df['idx']
Out[4]:
idx
a a
b b
c c
Name: idx, dtype: object
df.idx
Out[5]:
idx
a a
b b
c c
Name: idx, dtype: object
df[['idx', 'B']]
Out[6]:
idx B
idx
a a 4
b b 5
c c 6
```
and with a MultiIndex:
```
idx = pd.MultiIndex.from_product([list('abc'), list('fg')], names=['lev0', 'lev1'])
df = pd.DataFrame({'A': range(6), 'B': range(10, 16)}, index=idx)
df['lev0']
Out[9]:
lev0 lev1
a f a
g a
b f b
g b
c f c
g c
Name: lev0, dtype: object
df.lev0
Out[10]:
lev0 lev1
a f a
g a
b f b
g b
c f c
g c
Name: lev0, dtype: object
df[['A', 'lev1']]
Out[11]:
A lev1
lev0 lev1
a f 0 f
g 1 g
b f 2 f
g 3 g
c f 4 f
g 5 g
```
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} | 0 | 2016-02-21T10:53:14Z | 2017-05-20T21:59:19Z | 2017-05-20T21:59:19Z | MEMBER | null | xref #8914
```
In [1]: %matplotlib
Using matplotlib backend: Qt4Agg
In [2]: df = pd.DataFrame({'date': pd.date_range('2016-01-01', periods=5), 'vals
': range(5)})
In [3]: df.loc[2, 'date'] = np.nan
In [4]: s = df.set_index('date')['vals']
In [5]: s
Out[5]:
date
2016-01-01 0
2016-01-02 1
NaT 2
2016-01-04 3
2016-01-05 4
Name: vals, dtype: int64
In [6]: ax = s.plot()
In [11]: ax.get_lines()[0].get_data()
Out[11]:
(array([datetime.datetime(2016, 1, 1, 0, 0),
datetime.datetime(2016, 1, 2, 0, 0),
datetime.datetime(2016, 1, 4, 0, 0),
datetime.datetime(2016, 1, 5, 0, 0), NaT], dtype=object),
array([0, 1, 3, 4, 2], dtype=int64))
In [44]: ax.get_lines()[0].get_xydata()
Out[44]:
array([[ 7.35964000e+05, 0.00000000e+00],
[ 7.35965000e+05, 1.00000000e+00],
[ 7.35967000e+05, 3.00000000e+00],
[ 7.35968000e+05, 4.00000000e+00],
[ 6.12411009e+05, 2.00000000e+00]])
```
So this gives you a plot with one of the values in the year 1677, September 22, so the minimum possible Timestamp.
This is of course not correct, but there are two things:
- NaT gets converted to `Timestamp.min`
- the order of the values is changed, as the NaT is put at the end
There is a related issue about `plt.plot(pd.NaT)` erroring (https://github.com/pydata/pandas/issues/9253), but in this case it is pandas code that gives wrong results, so we have more control over this. I find it also strange that this does not error as in the pd.NaT case, but converts the NaT to Timestamp.min (but didn't look into detail)
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} | 0 | 2016-02-21T15:17:47Z | 2016-02-22T16:44:03Z | 2016-02-22T16:44:03Z | CONTRIBUTOR | null | ERROR: type should be string, got "https://travis-ci.org/pydata/pandas/jobs/110280302\n\nThese indicate an indexing change with `nan` that now returns `TypeError` rather than `IndexError`, ok makes sense\n\n```\n======================================================================\nERROR: test_range_slice_day (pandas.tseries.tests.test_period.TestPeriodIndex)\n----------------------------------------------------------------------\nTraceback (most recent call last):\n File \"/Users/jreback/miniconda/envs/numpy_dev/pandas/pandas/tseries/tests/test_period.py\", line 2589, in test_range_slice_day\n idx[v:]\n File \"/Users/jreback/miniconda/envs/numpy_dev/pandas/pandas/tseries/base.py\", line 175, in __getitem__\n result = getitem(key)\nTypeError: slice indices must be integers or None or have an __index__ method\n\n======================================================================\nERROR: test_range_slice_seconds (pandas.tseries.tests.test_period.TestPeriodIndex)\n----------------------------------------------------------------------\nTraceback (most recent call last):\n File \"/Users/jreback/miniconda/envs/numpy_dev/pandas/pandas/tseries/tests/test_period.py\", line 2643, in test_range_slice_seconds\n idx[v:]\n File \"/Users/jreback/miniconda/envs/numpy_dev/pandas/pandas/tseries/base.py\", line 175, in __getitem__\n result = getitem(key)\nTypeError: slice indices must be integers or None or have an __index__ method\n```\n\nSo our comparison in this test changed. So we need to make this change for 0.18.0 as well [here](https://github.com/pydata/pandas/blob/master/pandas/src/sparse.pyx#L1002), where these should be `NaN` and not `inf`.\n\n```\nSo numpy now returns:\nIn [1]: np.__version__\nOut[1]: '1.12.0.dev0+7d4d26a'\n\nIn [3]: np.array([3])//np.array([0],dtype='float64')\nOut[3]: array([ nan])\n\nIn [2]: np.float64(3)//np.float64(0)\nOut[2]: nan\n\nIn [3]: np.__version__\nOut[3]: '1.10.4'\n\nIn [4]: np.float64(3)//np.float64(0)\nOut[4]: inf\n\nIn [7]: np.array([3])//np.array([0],dtype='float64')\nOut[7]: array([ inf])\n```\n\n```\n======================================================================\nFAIL: test_sparse_series_ops (pandas.sparse.tests.test_sparse.TestSparseDataFrame)\n----------------------------------------------------------------------\nTraceback (most recent call last):\n File \"/Users/jreback/miniconda/envs/numpy_dev/pandas/pandas/sparse/tests/test_sparse.py\", line 1151, in test_sparse_series_ops\n self._check_frame_ops(self.frame)\n File \"/Users/jreback/miniconda/envs/numpy_dev/pandas/pandas/sparse/tests/test_sparse.py\", line 1221, in _check_frame_ops\n _compare_to_dense(frame, s, frame.to_dense(), s.to_dense(), f)\n File \"/Users/jreback/miniconda/envs/numpy_dev/pandas/pandas/sparse/tests/test_sparse.py\", line 1194, in _compare_to_dense\n exact_indices=False)\n File \"/Users/jreback/miniconda/envs/numpy_dev/pandas/pandas/sparse/tests/test_sparse.py\", line 95, in assert_sp_frame_equal\n assert_series_equal(series.to_dense(), right[col].to_dense())\n File \"/Users/jreback/miniconda/envs/numpy_dev/pandas/pandas/util/testing.py\", line 999, in assert_series_equal\n check_less_precise, obj='{0}'.format(obj))\n File \"testing.pyx\", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:3254)\n File \"testing.pyx\", line 147, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2395)\n File \"/Users/jreback/miniconda/envs/numpy_dev/pandas/pandas/util/testing.py\", line 867, in raise_assert_detail\n raise AssertionError(msg)\nAssertionError: Series are different\n\nSeries values are different (10.0 %)\n[left]: [nan, nan, nan, inf, 4.0, 2.0, 2.0, 1.0, 1.0, 1.0]\n[right]: [nan, nan, nan, nan, 4.0, 2.0, 2.0, 1.0, 1.0, 1.0]\n```\n" | {
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} | 1 | 2016-02-21T16:20:54Z | 2016-02-22T11:18:41Z | 2016-02-22T11:18:32Z | CONTRIBUTOR | null | Minor doc fixes following merge of PR #12015.
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| null | 1 | 2016-02-21T17:46:54Z | 2016-02-22T14:13:15Z | 2016-02-22T14:13:15Z | NONE | null | #### Code Sample, a copy-pastable example if possible
``` python
"""Example of Pandas bug."""
from pandas import read_csv
try:
from StringIO import StringIO
except ImportError:
from io import StringIO
csv = """A, B, C
1, 2, 3"""
def message(msg):
"""Print message."""
msg_len = len(msg) + 2
print('-' * msg_len + '\n {}\n'.format(msg) + '-' * msg_len)
message('raw file')
print(csv)
df = read_csv(StringIO(csv), names=['A', 'B', 'C'], skiprows=1, header=None,
usecols=['B', 'C'])
message('expected result after skiping column A')
print(df)
df = read_csv(StringIO(csv), names=['A', 'B', 'C'], skiprows=1, header=None,
usecols=['B', 'C'], index_col='B')
message('not expected result, if use index with column B')
print(df)
```
#### Expected Output
Current output:
```
----------
raw file
----------
A, B, C
1, 2, 3
----------------------------------------
expected result after skiping column A
----------------------------------------
B C
0 2 3
-------------------------------------------------
not expected result, if use index with column B
-------------------------------------------------
B
B
3 2
```
Expected output:
```
----------
raw file
----------
A, B, C
1, 2, 3
----------------------------------------
expected result after skiping column A
----------------------------------------
B C
0 2 3
-------------------------------------------------
not expected result, if use index with column B
-------------------------------------------------
C
B
2 3
```
#### output of `pd.show_versions()`
## 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: 0.17.1
nose: None
pip: 7.1.2
setuptools: 18.3.2
Cython: None
numpy: 1.10.1
scipy: 0.16.1
statsmodels: None
IPython: 4.0.1
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.0
openpyxl: 2.3.2
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: 2.8
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} | 2 | 2016-02-21T18:51:05Z | 2016-02-23T14:54:21Z | 2016-02-23T14:54:21Z | CONTRIBUTOR | null | The following works on 0.17.1 but breaks on the release candidate 0.18.0.rc1
```
>>> import pandas as pd
>>> pd.date_range(start=pd.Timestamp('20150101', tz='UTC'), end=pd.Timestamp('20150105', tz='UTC'), closed='left')
DatetimeIndex(['2015-01-01', '2015-01-02', '2015-01-03', '2015-01-04'], dtype='datetime64[ns, UTC]', freq='D')
```
On 0.18.0.rc1:
```
In [2]: pd.date_range(start=pd.Timestamp('20150101', tz='UTC'), end=pd.Timestamp('20150105', tz='UTC'), closed='left')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-2-b04844565a59> in <module>()
----> 1 pd.date_range(start=pd.Timestamp('20150101', tz='UTC'), end=pd.Timestamp('20150105', tz='UTC'), closed='left')
/.../pandas/tseries/index.py in date_range(start, end, periods, freq, tz, normalize, name, closed, **kwargs)
2033 return DatetimeIndex(start=start, end=end, periods=periods,
2034 freq=freq, tz=tz, normalize=normalize, name=name,
-> 2035 closed=closed, **kwargs)
2036
2037
/.../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
/.../pandas/tseries/index.py in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, closed, ambiguous, dtype, **kwargs)
259 return cls._generate(start, end, periods, name, freq,
260 tz=tz, normalize=normalize, closed=closed,
--> 261 ambiguous=ambiguous)
262
263 if not isinstance(data, (np.ndarray, Index, ABCSeries)):
/.../pandas/tseries/index.py in _generate(cls, start, end, periods, name, offset, tz, normalize, ambiguous, closed)
532 if not left_closed and len(index) and index[0] == start:
533 index = index[1:]
--> 534 if not right_closed and len(index) and index[-1] == end:
535 index = index[:-1]
536
/.../pandas/tslib.pyx in pandas.tslib._Timestamp.__richcmp__ (pandas/tslib.c:18791)()
975 (type(self).__name__, type(other).__name__))
976
--> 977 self._assert_tzawareness_compat(other)
978 return _cmp_scalar(self.value, ots.value, op)
979
/.../pandas/tslib.pyx in pandas.tslib._Timestamp._assert_tzawareness_compat (pandas/tslib.c:19145)()
1007 'timestamps')
1008 elif other.tzinfo is None:
-> 1009 raise TypeError('Cannot compare tz-naive and tz-aware timestamps')
1010
1011 cpdef datetime to_datetime(_Timestamp self):
TypeError: Cannot compare tz-naive and tz-aware timestamps
```
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} | 9 | 2016-02-21T19:51:40Z | 2016-02-23T14:54:21Z | 2016-02-23T14:54:21Z | CONTRIBUTOR | null | - [X] closes #12409
- [X] tests added / passed
- [X] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry - not added since this fixes a pre-release bug
do comparison for closed intervals on UTC basis prior to tz conversion
added tests for date_range covering case of tz-aware start/end dates with closed ranges
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} | 16 | 2016-02-22T02:13:44Z | 2016-03-23T17:56:45Z | 2016-03-23T17:56:45Z | NONE | null | I have a dataframe, `a_dataframe`. I want to access the value at index=a, column=0. I can do this successfully with `a_dataframe.loc["a", "0"]`. However, `a_dataframe.loc("a", "0")` returns an object! Note: parens, no brackets.
I am new to Pandas, so this was very unexpected behavior in the API. I would think the API should throw an exception or issue a warning. This is a very, very subtle difference in syntax and I think it makes it very easy for new users to get confused.
#### Code Sample, a copy-pastable example if possible
```
`self.assertEqual(a_dataframe.loc["a", "0"], a_dataframe.loc("a", "0"))`
```
#### Expected Output
true
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.2
nose: 1.3.7
Cython: 0.23.2
numpy: 1.8.0rc1
scipy: 0.13.0b1
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.2
pytz: 2013.7
bottleneck: None
tables: None
numexpr: 2.4.6
matplotlib: 1.3.1
openpyxl: 2.2.0-b1
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: 3.5.0
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.8
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
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| null | 21 | 2016-02-22T02:41:47Z | 2020-05-18T17:09:57Z | 2020-05-18T17:09:56Z | CONTRIBUTOR | null | Same issue as xarray: https://github.com/pydata/xarray/issues/771, resolved with https://github.com/pydata/xarray/pull/773.
OK to do the same to `NDFrame`?
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} | 55 | 2016-02-22T06:50:35Z | 2016-03-17T23:54:43Z | 2016-03-17T23:46:50Z | MEMBER | null | Title is self-explanatory. Closes #12238.
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| null | 3 | 2016-02-22T08:25:30Z | 2016-02-22T21:04:31Z | 2016-02-22T14:10:17Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd
df = pd.DataFrame({'a':[1,2,3], 'b':[4,5,6]})
assert type(df.apply(lambda column: [1,2])) == pd.Series
assert type(df.apply(lambda column: [1,2,3])) == pd.DataFrame
# when list length matches the relevant df size, the result is DF; otherwise, a Series of lists
```
#### Expected Output
This behavior is not documented, but more importantly it's really dangerous. A slight change in the inputs may cause a really hard-to-track bug. I think only two alternatives are safe:
1) When the function in `apply` returns a list, the result should always be a Series of lists.
2) When the function in `apply` returns a list with the correct size, the result should be a DataFrame; otherwise, an error should be raised.
Approach 2) is obviously too restrictive, so I'd recommend approach 1).
On a related note, it would be nice to be able to explicitly tell `apply` whether I want a Series of lists or a DF as a result.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.0.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 70 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.0
nose: None
pip: 8.0.2
setuptools: 18.2
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: None
IPython: 4.0.0
sphinx: None
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2015.6
blosc: None
bottleneck: None
tables: None
numexpr: 2.5
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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} | 12 | 2016-02-22T11:04:17Z | 2017-04-17T20:44:25Z | 2017-04-17T20:44:25Z | MEMBER | null | ```
In [2]: df = pd.DataFrame(index=range(2), columns=pd.MultiIndex.from_product([[10,20], ['a', 'b']]))
In [3]: df[[20]]
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-3-8422ebb3f356> in <module>()
----> 1 df[[20]]
/home/pietro/nobackup/repo/pandas/pandas/core/frame.pyc in __getitem__(self, key)
1975 if isinstance(key, (Series, np.ndarray, Index, list)):
1976 # either boolean or fancy integer index
-> 1977 return self._getitem_array(key)
1978 elif isinstance(key, DataFrame):
1979 return self._getitem_frame(key)
/home/pietro/nobackup/repo/pandas/pandas/core/frame.pyc in _getitem_array(self, key)
2020 else:
2021 indexer = self.ix._convert_to_indexer(key, axis=1)
-> 2022 return self.take(indexer, axis=1, convert=True)
2023
2024 def _getitem_multilevel(self, key):
/home/pietro/nobackup/repo/pandas/pandas/core/generic.pyc in take(self, indices, axis, convert, is_copy)
1590 new_data = self._data.take(indices,
1591 axis=self._get_block_manager_axis(axis),
-> 1592 convert=True, verify=True)
1593 result = self._constructor(new_data).__finalize__(self)
1594
/home/pietro/nobackup/repo/pandas/pandas/core/internals.pyc in take(self, indexer, axis, verify, convert)
3617 n = self.shape[axis]
3618 if convert:
-> 3619 indexer = maybe_convert_indices(indexer, n)
3620
3621 if verify:
/home/pietro/nobackup/repo/pandas/pandas/core/indexing.pyc in maybe_convert_indices(indices, n)
1803 mask = (indices >= n) | (indices < 0)
1804 if mask.any():
-> 1805 raise IndexError("indices are out-of-bounds")
1806 return indices
1807
IndexError: indices are out-of-bounds
```
and
```
In [4]: df[[1]]
Out[4]:
10
b
0 NaN
1 NaN
```
should both raise `KeyError` (see https://github.com/pydata/pandas/issues/12369#issuecomment-187065056 )
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} | 2 | 2016-02-22T11:12:07Z | 2016-02-23T19:29:43Z | 2016-02-23T18:50:26Z | CONTRIBUTOR | null | - [x] closes #12031
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
I just pass on the `decimal` parameter. All the machinery was already in place.
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} | 0 | 2016-02-22T13:47:14Z | 2016-02-22T16:44:03Z | 2016-02-22T16:44:03Z | CONTRIBUTOR | null | test_period and slicing exception with nan
compat in sparse for floordiv with 0, now returns NaN rather than inf
closes #12406
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} | 2 | 2016-02-22T14:45:12Z | 2016-02-22T17:04:02Z | 2016-02-22T16:44:53Z | MEMBER | null | @shoyer just came across this in the docs. These should be the only (remaining) mentions of xray
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| null | 3 | 2016-02-22T18:34:20Z | 2016-02-23T10:25:36Z | 2016-02-22T18:54:18Z | NONE | null | I need to perform computations in Pandas in multiple threads. None of the dataframes are shared across running threads. Although there is some race condition witch causes the interpreter to crash.
I Googled for help and [found this](http://stackoverflow.com/questions/25782912/pandas-and-numpy-thread-safety).
The snippet included in that stackoverflow discussion is definitely crashing my interpreter, so I insert it below as a bug reproduction.
I am aware of Pandas not being thread-safe, but thread-safety feature mostly considers the case of accessing the same object instance by more threads. Threads in the snippet below, however, have all their own DataFrame instance, so the thread safety at least theoretically should not be needed.
#### Code Sample, a copy-pastable example if possible
```
import threading
import pandas as pd
import numpy as np
def let_crash(crash=True):
t = 0.02 * np.arange(100000) # ok con 10000
data = pd.DataFrame({'t': t})
if crash:
data['t'] * 1.5 # CRASH
else:
data['t'].values * 1.5 # THIS IS OK!
if __name__ == '__main__':
threads = []
for i in range(100):
if True: # asynchronous
t = threading.Thread(target=let_crash, args = ())
t.daemon = True
t.start()
threads.append(t)
else: # synchronous
let_crash()
for t in threads:
t.join()
```
#### Expected Output
The code should just finish without the interpreter dying by access violation error.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 32
OS: Windows
OS-release: 8
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.16.2
nose: 1.3.7
Cython: 0.22.1
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 2.4.1
sphinx: 1.3.2
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.4
bottleneck: 1.0.0
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: 2.2.4
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: None
lxml: 3.4.4
bs4: 4.3.2
html5lib: 0.99999
httplib2: None
apiclient: None
sqlalchemy: 1.0.6
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext)
#### Windows error message
The error message changes occassionally, I included two versions below. Also I am translating from Czech Windows version so the original English field names probably vary.
```
Problem description:
Problem event name: APPCRASH
Application name: python.exe
Application version: 0.0.0.0
Application timestamp: 5560ad83
Name of errorneous module: python27.dll
Version of errorneous module: 2.7.10150.1013
Timestamp of errorneous module: 5560ad81
Exception code: c0000005
Exception shift: 000ae4a9
Operating system version: 6.3.9600.2.0.0.768.101
National environment ID: 1029
Further info 1: 5861
Further info 2: 5861822e1919d7c014bbb064c64908b2
Further info 3: 1318
Further into 4: 13181ae637ac2592fac0ca2dd662cdcc
```
```
Problem description:
Problem event name: APPCRASH
Application name: python.exe
Application version: 0.0.0.0
Application timestamp: 5560ad83
Name of errorneous module: interpreter.pyd
Version of errorneous module: 0.0.0.0
Timestamp of errorneous module: 5543308a
Exception code: c0000005
Exception shift: 00007e6c
Operating system version: 6.3.9600.2.0.0.768.101
National environment ID: 1029
Further info 1: 5861
Further info 2: 5861822e1919d7c014bbb064c64908b2
Further info 3: 1318
Further info 4: 13181ae637ac2592fac0ca2dd662cdcc
```
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} | 1 | 2016-02-22T18:48:30Z | 2016-02-23T14:57:23Z | 2016-02-23T14:57:16Z | CONTRIBUTOR | null | - [x] closes #12278
- [ ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] adding notes about .unique() and .cat.categories (as suggested in [#12278 (comment)](https://github.com/pydata/pandas/issues/12278#issuecomment-182445522))
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} | 1 | 2016-02-23T02:36:59Z | 2016-11-11T11:38:14Z | 2016-11-11T11:38:02Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
dat_train, dat_test = train_test_split(dat, test_size=0.3, random_state=110)
dat_train[features] = dat_train[features].values # SettingWithCopyWarning
dat[features] = dat[features].values # right
dat_train = dat_train.ix[dat_train.index]
dat_train[features] = dat_train[features].values # right !
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.1
nose: 1.3.7
pip: 8.0.2
setuptools: 19.6.2
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
IPython: 4.0.3
sphinx: 1.3.1
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: None
Jinja2: 2.8
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} | 9 | 2016-02-23T04:24:17Z | 2019-06-19T01:05:34Z | 2019-06-19T01:05:34Z | NONE | null | We are using pandas to do analysis on a moderately large size dataset (10s of millions of events). In the process, we create a MultiIndex object in order to store the data. While I was debugging, I repeatedly ran into an issue where the MultiIndex object tried to print out the entire very long array. I've included code for a short test I did just to confirm the behavior.
Ideally, I think it would be good for the string representation of the MultiIndex object to have similar abbreviation strategy to the Series and DataFrame objects it indexes.
#### Code Sample, a copy-pastable example if possible
```
>>> import pandas as pd
>>> index1=range(100)
>>> index2=range(100)
>>> testIndex = pd.MultiIndex.from_arrays([index1, index2])
```
#### Expected Output
```
MultiIndex(levels=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]],
labels=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-77-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 8.0.2
setuptools: 18.0.1
Cython: 0.23.4
numpy: 1.10.2
scipy: 0.16.1
statsmodels: 0.6.1
IPython: 4.0.3
sphinx: 1.3.4
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.2
lxml: 3.5.0
bs4: 4.3.2
html5lib: None
httplib2: 0.9.1
apiclient: 1.4.1
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
Jinja2: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12424 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12424/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12424/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12424/events | https://github.com/pandas-dev/pandas/issues/12424 | 135,704,250 | MDU6SXNzdWUxMzU3MDQyNTA= | 12,424 | pd.to_datetime raises AttributeError with specific inputs when errors='ignore' | {
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} | 1 | 2016-02-23T10:57:08Z | 2016-08-05T19:52:18Z | 2016-08-05T19:52:05Z | NONE | null | I'm trying to import a csv file into a PostgreSQL table using [odo](https://github.com/blaze/odo). During import, `odo` tries to automatically detect date columns using `pd.to_datetime` with `errors='ignore'`. However, with one of my columns (that isn't a date column, it's some kind of zip code), `pd.to_datetime` raises an AttributeError.
I have been able to narrow down the problem to this small snippet:
```
>>> pd.to_datetime(pd.Series(['01210', np.nan]), errors='ignore')
Traceback (most recent call last):
File "pandas\tslib.pyx", line 1952, in pandas.tslib.array_to_datetime (pandas\tslib.c:35219)
File "pandas\tslib.pyx", line 1432, in pandas.tslib._check_dts_bounds (pandas\tslib.c:26809)
pandas.tslib.OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 1210-01-01 00:00:00
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "pandas\tslib.pyx", line 1981, in pandas.tslib.array_to_datetime (pandas\tslib.c:35724)
File "pandas\tslib.pyx", line 1975, in pandas.tslib.array_to_datetime (pandas\tslib.c:35602)
File "pandas\tslib.pyx", line 1228, in pandas.tslib.convert_to_tsobject (pandas\tslib.c:23563)
File "pandas\tslib.pyx", line 1432, in pandas.tslib._check_dts_bounds (pandas\tslib.c:26809)
pandas.tslib.OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 1210-01-01 00:00:00
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Miniconda3\envs\py35\lib\site-packages\pandas\util\decorators.py", line 89, in wrapper
return func(*args, **kwargs)
File "C:\Miniconda3\envs\py35\lib\site-packages\pandas\tseries\tools.py", line 276, in to_datetime
unit=unit, infer_datetime_format=infer_datetime_format)
File "C:\Miniconda3\envs\py35\lib\site-packages\pandas\tseries\tools.py", line 390, in _to_datetime
values = _convert_listlike(arg._values, False, format)
File "C:\Miniconda3\envs\py35\lib\site-packages\pandas\tseries\tools.py", line 372, in _convert_listlike
require_iso8601=require_iso8601)
File "pandas\tslib.pyx", line 1847, in pandas.tslib.array_to_datetime (pandas\tslib.c:37155)
File "pandas\tslib.pyx", line 2005, in pandas.tslib.array_to_datetime (pandas\tslib.c:36116)
AttributeError: 'float' object has no attribute 'view'
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.1
nose: None
pip: 8.0.2
setuptools: 19.6.2
Cython: None
numpy: 1.10.1
scipy: 0.16.0
statsmodels: None
IPython: 4.0.1
sphinx: 1.3.1
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: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
Jinja2: 2.8
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12425 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12425/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12425/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12425/events | https://github.com/pandas-dev/pandas/issues/12425 | 135,720,432 | MDU6SXNzdWUxMzU3MjA0MzI= | 12,425 | Stripped timedelta[s] series represented as timedelta[ns] | {
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} | 12 | 2016-02-23T12:10:46Z | 2018-01-13T18:18:43Z | 2018-01-13T18:18:43Z | MEMBER | null | ```
In [2]: s = pd.Series(range(61)).astype('timedelta64[s]')
In [3]: s.tail(1)
Out[3]:
60 00:01:00
dtype: timedelta64[s]
In [4]: str(s).splitlines()[-2]
Out[4]: '60 00:00:00.000000'
```
... because the snipped representation interprets the content of the series as nanoseconds rather than milliseconds. The same happens with `timedelta[ms]` and probably any other resolution (if there are).
```
In [5]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: 404819358e90da57c8025a259ab58cd75426069f
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.3.0-1-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
pandas: 0.18.0rc1+35.g4048193
nose: 1.3.6
pip: 1.5.6
setuptools: 20.1.1
Cython: 0.23.2
numpy: 1.10.0.post2
scipy: 0.16.0
statsmodels: 0.8.0.dev0+755fa81
xarray: None
IPython: 4.1.1
sphinx: 1.3.1
patsy: 0.3.0-dev
dateutil: 2.4.2
pytz: 2015.6
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.3
matplotlib: 1.5.dev1
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: 0.7.3
lxml: None
bs4: 4.4.0
html5lib: 0.999
httplib2: 0.9.1
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
jinja2: 2.8
```
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} | 3 | 2016-02-23T14:43:47Z | 2021-04-23T01:02:52Z | null | CONTRIBUTOR | null | It'd be nice if there was a way to easily broadcast arithmetic operations on Series/DataFrames with `DatetimeIndexes`. For example, say you have monthly data, calculate the monthly mean, and want to multiply the original series by the monthly factor.
``` python
import numpy as np
import pandas as pd
idx = index=pd.date_range('1970-01-01', end='2015-01-01', freq='MS')
s = pd.Series(np.random.randn(len(idx)), name='ts',
index=idx)
mf = s.groupby(lambda x: x.month).mean()
In [3]: s
Out[3]:
1970-01-01 -1.032080
1970-02-01 -0.706686
1970-03-01 -0.380895
1970-04-01 0.322074
1970-05-01 -1.545298
...
2014-09-01 -0.787646
2014-10-01 -2.444045
2014-11-01 -0.646474
2014-12-01 -0.291925
2015-01-01 0.399430
Freq: MS, Name: ts, dtype: float64
In [4]: mf
Out[4]:
1 0.184326
2 -0.069534
3 -0.146372
4 0.018643
5 0.050393
...
8 -0.088188
9 0.148328
10 0.105042
11 0.219308
12 0.000997
Name: ts, dtype: float64
```
Right now you need something like (this could be simplified)
``` python
tmp = s.copy()
tmp.index = s.index.month
tmp = tmp * mf
tmp.index = s.index
s
```
It'd be nice to use the fact that `s` is a datetimeindex to allow some broadcasting over the years. Something like
```
s.mul(mf, freq='M')
```
A similar case is when both `s` and `mf` have DatetimeIndexes. Then `s.mul(mf, freq='M')` upsamples the lower frequency `mf` to a monthly frequency, fills, and multiplies.
Another example from [SO](http://stackoverflow.com/questions/35579455/how-to-multiply-all-hourly-values-in-one-pandas-dataframe-with-yearly-values-in)
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| null | 3 | 2016-02-23T14:59:00Z | 2016-02-23T15:18:58Z | 2016-02-23T14:59:29Z | CONTRIBUTOR | null | ``` python
In [103]: pd.__version__
Out[103]: '0.17.1'
In [104]: store = pd.HDFStore('store.h5')
...: df = pd.DataFrame(np.random.randn(8, 3), columns=['A', 'B', 'C'])
...: store['df'] = df
...: store.get_storer('df').attrs.pandas_version
...:
Out[104]: '0.15.2' # should be 0.17.1
In [105]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: x86
processor: x86 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 18.5
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.0
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
Jinja2: 2.8
```
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} | 14 | 2016-02-23T16:39:12Z | 2016-02-24T15:56:38Z | 2016-02-24T13:33:21Z | CONTRIBUTOR | null | {
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| null | 7 | 2016-02-23T18:01:55Z | 2016-11-15T23:00:56Z | 2016-11-15T22:53:11Z | MEMBER | null | Upstream repositories like `numpy` and `scipy` have a `numpy-svn` and `scipy-svn` mailing list that is used to avert users of commits / merges to the `master` branch. You can find those lists <a href="http://www.scipy.org/scipylib/mailing-lists.html">here</a> here. I personally find it useful to get such emails because it allows me to make sure that my PR branches don't fall too far behind the `master` branch. I see no reason why the `pandas` repository wouldn't benefit from such an emailing list either.
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| null | 6 | 2016-02-23T18:04:33Z | 2020-05-13T02:11:39Z | null | NONE | null | I was generating lots of features for time-dependent data, and ended up writing a lot of expanding apply operations in Cython. Would the community want something like this? Imagine you have a dataframe with an "entity" column, a "time" column and some numeric "feature" column, and you want to calculate the expanding sum/mean/mode/etc. of the feature column for each entity.
This is currently not optimized well in Pandas especially for computing the mode for categorical variables where keeping track of state saves a lot of time.
Example of a use case:
```
import cython_opt # cython functions are defined here
# df like {"project_id": [1,1,1,1,2,2,2,2], "value": [3,4,5,6, 10,11,12,13]}
df.groupby(level='project_id')['value'].transform(lambda x: cython_opt.expanding_mean(x.values))
# output like {"project_id": [1,1,1,1,2,2,2,2], "value": [3, 3.5, 4, 4.5, 10, 10.5, 11, 11.5]}
```
I can provide lots more examples and functions (I wrote around 20 of these)
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} | 2 | 2016-02-23T21:30:23Z | 2016-02-23T21:59:59Z | 2016-02-23T21:59:56Z | CONTRIBUTOR | null | Removes a extraneous µ from the docs.
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} | 1 | 2016-02-23T22:58:12Z | 2016-11-27T05:13:56Z | 2016-11-27T05:13:56Z | MEMBER | null | `conda` has been the widely suggested method for building / developing `pandas` on Windows. However, given that upstream libraries like `numpy` and `scipy` are in the process / can be supported with many other build environments on Windows, it seems natural that `pandas` should support alternative build environments given that its build requirements are much more lenient (just need a C compiler) compared to those of `numpy` and `scipy`. Some major alternatives are:
1) `mingw-w64`
2) `Cygwin`
Initial testing with the second one are promising and suggest that `pandas` can already support it. The former has not been tested yet but should work in theory given that it works with `numpy` for example.
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} | 2 | 2016-02-24T01:16:08Z | 2018-07-06T20:21:09Z | 2017-07-11T09:52:44Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import numpy as np
import pandas as pd
dr = pd.date_range('20100101','20151231',freq='T')
p1 = pd.Panel(items = dr, data=np.random.randn(len(dr),3,3))
#this works
p2 = p1.resample('D', axis=0, how='last')
p3 = pd.Panel(items = dr, data=np.random.randn(len(dr),1,1))
#this causes an error
p4 = p3.resample('D', axis=0, how='last')
```
#### Expected Output
#### 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 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.1
nose: 1.3.7
pip: 8.0.2
setuptools: 19.6.2
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
IPython: None
sphinx: 1.3.1
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: None
Jinja2: 2.8
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} | 2 | 2016-02-24T01:43:35Z | 2018-07-06T20:21:09Z | 2017-07-11T09:53:17Z | CONTRIBUTOR | null | ```
In [32]: pd.__version__
Out[32]: '0.18.0rc1+40.gbd1eebb'
```
```
In [23]: dr = pd.date_range('20150101','20150131',freq='T')
In [24]: p1 = pd.Panel(items = dr, data=np.random.randn(len(dr),3,3))
In [25]: p1
Out[25]:
<class 'pandas.core.panel.Panel'>
Dimensions: 43201 (items) x 3 (major_axis) x 3 (minor_axis)
Items axis: 2015-01-01 00:00:00 to 2015-01-31 00:00:00
Major_axis axis: 0 to 2
Minor_axis axis: 0 to 2
In [26]: p1.swapaxes(0,1)
Out[26]:
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 43201 (major_axis) x 3 (minor_axis)
Items axis: 0 to 2
Major_axis axis: 2015-01-01 00:00:00 to 2015-01-31 00:00:00
Minor_axis axis: 0 to 2
In [27]: p1.swapaxes(0,1).iloc[0,0,0]
Out[27]: 2.1388125215371305
In [28]: p1.swapaxes(0,1).to_frame()
TypeError: data type "minor" not understood
```
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} | 2 | 2016-02-24T10:21:44Z | 2016-02-24T10:48:16Z | 2016-02-24T10:47:31Z | NONE | null | #### Code Sample, a copy-pastable example if possible
``` python
import pandas as pd
import numpy as np
pd.DataFrame({'one' : pd.Series(["NULL", "1", "0"])}).to_csv("test.txt")
print pd.read_csv("test.txt", usecols=(1, ), dtype={'one': np.bool}, false_values=['0', 'NULL'], true_values=['1'])
```
Output:
```
one
0 NaN
1 True
2 False
```
#### Expected Output
```
one
0 False
1 True
2 False
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 32
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 19.1.1
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: None
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.0
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.3.5
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
Jinja2: None
```
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} | 4 | 2016-02-24T13:37:03Z | 2016-04-25T23:21:51Z | 2016-04-25T15:20:05Z | CONTRIBUTOR | null | anyone know the reason for these? https://travis-ci.org/pydata/pandas/jobs/111469225
only seem to be on py3.
```
/home/travis/miniconda/envs/pandas/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
/home/travis/miniconda/envs/pandas/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
```
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} | 6 | 2016-02-24T14:26:37Z | 2018-10-26T02:40:27Z | 2018-10-26T02:40:27Z | MEMBER | null | ```
In [2]: base, step = 10**15, 10**14
In [3]: data = range(base, base+3*step, step)
In [4]: df = pd.DataFrame({i : data for i in range(3)}).astype('datetime64[ns]')
In [5]: s = pd.Series(data).astype('datetime64[ns]')
In [6]: df
Out[6]:
0 1 2
0 1970-01-12 13:46:40 1970-01-12 13:46:40 1970-01-12 13:46:40
1 1970-01-13 17:33:20 1970-01-13 17:33:20 1970-01-13 17:33:20
2 1970-01-14 21:20:00 1970-01-14 21:20:00 1970-01-14 21:20:00
In [7]: (df - s)[0]
Out[7]:
0 1970-01-01 00:00:00
1 1970-01-02 03:46:40
2 1970-01-03 07:33:20
Name: 0, dtype: datetime64[ns]
In [8]: (df[0] - s[0])
Out[8]:
0 0 days 00:00:00
1 1 days 03:46:40
2 2 days 07:33:20
Name: 0, dtype: timedelta64[ns]
```
I would expect the two to give the same result (`timedelta64[ns]`).
```
In [9]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 4.3.0-1-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
pandas: 0.18.0rc1+35.g4048193
nose: 1.3.6
pip: 1.5.6
setuptools: 18.4
Cython: 0.23.2
numpy: 1.10.4
scipy: 0.16.0
statsmodels: 0.8.0.dev0+755fa81
xarray: None
IPython: 2.4.1
sphinx: 1.3.1
patsy: 0.3.0-dev
dateutil: 2.2
pytz: 2012c
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.3
matplotlib: 1.5.0rc2
openpyxl: None
xlrd: 0.9.4
xlwt: 0.7.5
xlsxwriter: 0.7.3
lxml: None
bs4: 4.4.0
html5lib: 0.999
httplib2: 0.9.1
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: 2.6.1 (dt dec mx pq3 ext lo64)
jinja2: 2.8
```
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} | 10 | 2016-02-24T14:43:01Z | 2017-03-22T21:39:51Z | 2017-03-22T13:58:04Z | CONTRIBUTOR | null | Came across this just now https://github.com/grosser/travis_dedup
It sounds like it cancels old builds on a PR. It works via github webhooks.
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https://api.github.com/repos/pandas-dev/pandas/issues/12440 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12440/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12440/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12440/events | https://github.com/pandas-dev/pandas/issues/12440 | 136,140,676 | MDU6SXNzdWUxMzYxNDA2NzY= | 12,440 | calling mean on groupby of timedelta does not work | {
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| null | 1 | 2016-02-24T18:07:34Z | 2016-02-24T18:49:13Z | 2016-02-24T18:11:29Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
This works:
```
print pd.DataFrame([pd.Timedelta(1)]).groupby(level=0).sum()
0
0 00:00:00.000000
```
but this does not:
```
print pd.DataFrame([pd.Timedelta(1)]).groupby(level=0).mean()
---------------------------------------------------------------------------
DataError Traceback (most recent call last)
<ipython-input-151-ac23f5162583> in <module>()
----> 1 print pd.DataFrame([pd.Timedelta(1)]).groupby(level=0).mean()
/Users/alex/.virtualenvs/work/lib/python2.7/site-packages/pandas/core/groupby.pyc in mean(self)
758 """
759 try:
--> 760 return self._cython_agg_general('mean')
761 except GroupByError:
762 raise
/Users/alex/.virtualenvs/work/lib/python2.7/site-packages/pandas/core/groupby.pyc in _cython_agg_general(self, how, numeric_only)
2851
2852 def _cython_agg_general(self, how, numeric_only=True):
-> 2853 new_items, new_blocks = self._cython_agg_blocks(how, numeric_only=numeric_only)
2854 return self._wrap_agged_blocks(new_items, new_blocks)
2855
/Users/alex/.virtualenvs/work/lib/python2.7/site-packages/pandas/core/groupby.pyc in _cython_agg_blocks(self, how, numeric_only)
2896
2897 if len(new_blocks) == 0:
-> 2898 raise DataError('No numeric types to aggregate')
2899
2900 return data.items, new_blocks
DataError: No numeric types to aggregate
```
even though both of these work:
```
pd.DataFrame([pd.Timedelta(1)]).mean()
```
and
```
pd.DataFrame([pd.Timedelta(1)]).sum()
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.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: 1.3.6
pip: 1.5.6
setuptools: 3.6
Cython: None
numpy: 1.10.4
scipy: 0.15.1
statsmodels: None
IPython: 3.1.0
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: 0.9.3
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.0
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
Jinja2: None
```
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| null | 3 | 2016-02-24T20:11:26Z | 2016-02-25T00:06:22Z | 2016-02-25T00:05:57Z | NONE | null | #### Code Sample, a copy-pastable example if possible
after installing pandas rc18, getting import error
```
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-2-26dfcabfb474> in <module>()
----> 1 from pandas import DataFrame
/Users/vassr/.virtualenvs/gapsincare/lib/python3.5/site-packages/pandas/__init__.py in <module>()
46 from pandas.stats.api import *
47 from pandas.tseries.api import *
---> 48 from pandas.io.api import *
49 from pandas.computation.api import *
50
/Users/vassr/.virtualenvs/gapsincare/lib/python3.5/site-packages/pandas/io/api.py in <module>()
10 from pandas.io.html import read_html
11 from pandas.io.sql import read_sql, read_sql_table, read_sql_query
---> 12 from pandas.io.sas import read_sas
13 from pandas.io.stata import read_stata
14 from pandas.io.pickle import read_pickle, to_pickle
ImportError: cannot import name 'read_sas'
```
### You can see that pandas rc18 was successfully installed here.
```
~ pip install cython
Collecting cython
Downloading Cython-0.23.4-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (3.6MB)
100% |████████████████████████████████| 3.6MB 127kB/s
Installing collected packages: cython
Successfully installed cython-0.23.4
(gapsincare)➜ ~ pip install git+https://github.com/pydata/pandas.git --upgrade
Collecting git+https://github.com/pydata/pandas.git
Cloning https://github.com/pydata/pandas.git to /var/folders/4y/5mcwcwm94v18v67997q6m_vmlrpjlj/T/pip-7kx4op06-build
Requirement already up-to-date: python-dateutil>=2 in ./.virtualenvs/gapsincare/lib/python3.5/site-packages (from pandas==0.18.0rc1+43.gdb12255)
Requirement already up-to-date: pytz>=2011k in ./.virtualenvs/gapsincare/lib/python3.5/site-packages (from pandas==0.18.0rc1+43.gdb12255)
Requirement already up-to-date: numpy>=1.7.0 in ./.virtualenvs/gapsincare/lib/python3.5/site-packages (from pandas==0.18.0rc1+43.gdb12255)
Requirement already up-to-date: six>=1.5 in ./.virtualenvs/gapsincare/lib/python3.5/site-packages (from python-dateutil>=2->pandas==0.18.0rc1+43.gdb12255)
Installing collected packages: pandas
Running setup.py install for pandas ... \^[[B^[[done
Successfully installed pandas-0.18.0rc1+43.gdb12255
```
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} | 6 | 2016-02-24T20:16:00Z | 2016-02-26T02:14:46Z | 2016-02-26T02:13:43Z | CONTRIBUTOR | null | - [x] closes #12393
- [x] passes `git diff upstream/master | flake8 --diff`
Added notes and examples to provide clarity around the issue discussed in #12393. This is my first contribution to pandas, so let me know if I need to fix anything.
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| null | 3 | 2016-02-25T08:24:24Z | 2016-08-21T02:41:02Z | 2016-08-21T02:41:02Z | NONE | null | `pd.read_csv()`'s `skip_blank_lines` parameter defaults to `True`
I'm not sure if that is a good idea?
If I write away a dataframe that has 1 column and includes N null values to a CSV, without writing away an index (`index=False`), then I do not expect that when I read the dataframe back from that CSV the result is N rows shorter than the original.
For example I was puzzled when I wrote away a column with 1000 rows to CSV, then read that column back from the CSV to find it only had 37 rows.
```
In [2]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-1-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_ZA.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 1.5.6
setuptools: 20.1.1
Cython: None
numpy: 1.11.0b3
scipy: 0.17.0
statsmodels: None
IPython: 4.1.1
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 0.999
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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| null | 4 | 2016-02-25T13:25:41Z | 2016-02-25T15:24:06Z | 2016-02-25T15:23:15Z | NONE | null | When setting using an indexer which is of length one I get AttributeError on assignment. The same error occurs with series of length greater than 1 and with indexer of length 1, but does not occur in the latter scenario if the indexer is of length greater than one. The .iloc[indexer] below returns a Series.
Encountered in version 0.15.2, reproduced in 0.17.1
``` python
>>> import pandas as pd
>>> import numpy as np
>>> import datetime
>>> mi = pd.MultiIndex.from_tuples([(3465, datetime.datetime.utcnow())])
>>> foo1 = pd.Series([5], index=mi); foo1
3465 2016-02-25 13:06:21.639667+00:00 5
3466 2016-02-25 13:06:21.639674+00:00 6
dtype: int64
>>> foo2 = pd.Series(['foo'], index=mi);foo2
3465 2016-02-25 13:06:21.638573+00:00 foo
dtype: object
>>> indexer= np.where(foo2);indexer
(array([0]),)
>>> foo1.iloc[indexer] = foo2.iloc[indexer]
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-87-320a8afb0fd6> in <module>()
----> 1 foo1.iloc[indexer] = foo2.iloc[indexer]
/python2.7/site-packages/pandas/core/indexing.pyc in __setitem__(self, key, value)
115 def __setitem__(self, key, value):
116 indexer = self._get_setitem_indexer(key)
--> 117 self._setitem_with_indexer(indexer, value)
118
119 def _has_valid_type(self, k, axis):
/python2.7/site-packages/pandas/core/indexing.pyc in _setitem_with_indexer(self, indexer, value)
365 # indexer here
366 if (len(labels) == 1 and
--> 367 isinstance(self.obj[labels[0]].axes[0], MultiIndex)):
368 item = labels[0]
369 obj = self.obj[item]
AttributeError: 'numpy.int64' object has no attribute 'axes'
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-48-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.1
nose: 1.3.7
pip: 1.4.1
setuptools: 17.0
Cython: 0.23.4
numpy: 1.9.0
scipy: 0.14.0
statsmodels: None
IPython: 4.0.0
sphinx: None
patsy: None
dateutil: 1.5
pytz: 2015.6
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.4.4
bs4: None
html5lib: 0.999
httplib2: 0.9
apiclient: 1.1
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: None
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| null | 3 | 2016-02-25T15:41:44Z | 2016-02-25T23:27:09Z | 2016-02-25T17:35:18Z | NONE | null | Hello,
Because of the decimal symbol I'm forced to use the following code. The problem is when the using is "big", for example: 1 356 747 -> 1,35675e+06. Is there any work around?
import locale
loc = locale.getlocale() # get current locale
locale.setlocale(locale.LC_NUMERIC, "Portuguese_Portugal")
html_table = df.to_html(header=True, index=False, na_rep="", float_format="{:n}".format, justify="center")
locale.setlocale(locale.LC_NUMERIC, loc) # restore saved locale
print(html_table)
#### Code Sample, a copy-pastable example if possible
<tr>
<td>AMOUNT</td>
<td>1,35675e+06</td>
<td>95 524</td>
<td>179 347</td>
<td>255 691</td>
</tr>
#### Expected Output
<tr>
<td>AMOUNT</td>
<td>1 356 747</td>
<td>95 524</td>
<td>179 347</td>
<td>255 691</td>
</tr>
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 2012ServerR2
machine: AMD64
processor: Intel64 Family 6 Model 47 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.1
nose: None
pip: 8.0.2
setuptools: 20.1.1
Cython: None
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: None
xlsxwriter: 0.8.4
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: None
None
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| null | 2 | 2016-02-25T17:56:05Z | 2016-02-25T19:09:42Z | 2016-02-25T19:09:33Z | NONE | null | Hello I'm having troubles with the tz_localize function.
I run the code :
```
df = pd.read_csv('input.csv', sep=";", index_col='Date Time', decimal=',')
df.index = pd.to_datetime(df.index, unit='s').tz_localize('Europe/Rome')
```
and I get this error:
```
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2820, in run_code
exec code_obj in self.user_global_ns, self.user_ns
File "<ipython-input-62-1e9889f6b894>", line 1, in <module>
df.index = pd.to_datetime(df.index, unit='s').tz_localize('Europe/Rome')
File "/usr/local/lib/python2.7/dist-packages/pandas/util/decorators.py", line 89, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/pandas/tseries/index.py", line 1724, in tz_localize
ambiguous=ambiguous)
File "pandas/tslib.pyx", line 3781, in pandas.tslib.tz_localize_to_utc (pandas/tslib.c:64980)
NonExistentTimeError: 2013-03-31 02:00:00
```
I have pandas '0.17.1' running on python2.7. In attachment you find the input file [input.zip](https://github.com/pydata/pandas/files/147045/input.zip).
Best
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| null | 1 | 2016-02-25T22:35:02Z | 2016-02-25T22:45:46Z | 2016-02-25T22:45:30Z | NONE | null | #### Code Sample, a copy-pastable example if possible
p.to_hdf('quotes.hdf','df',format='table')
#### Expected Output
Saves files
#### output of `pd.show_versions()`
Traceback (most recent call last):
File "<ipython-input-15-7be322c5e66c>", line 1, in <module>
p.to_hdf('quotes.hdf','df',format='table')
File "C:\Users\LastAncientOne\Anaconda2\lib\site-packages\pandas\core\generic.py", line 939, in to_hdf
return pytables.to_hdf(path_or_buf, key, self, **kwargs)
File "C:\Users\LastAncientOne\Anaconda2\lib\site-packages\pandas\io\pytables.py", line 257, in to_hdf
f(store)
File "C:\Users\LastAncientOne\Anaconda2\lib\site-packages\pandas\io\pytables.py", line 252, in <lambda>
f = lambda store: store.put(key, value, **kwargs)
File "C:\Users\LastAncientOne\Anaconda2\lib\site-packages\pandas\io\pytables.py", line 814, in put
self._write_to_group(key, value, append=append, **kwargs)
File "C:\Users\LastAncientOne\Anaconda2\lib\site-packages\pandas\io\pytables.py", line 1252, in _write_to_group
s.write(obj=value, append=append, complib=complib, **kwargs)
File "C:\Users\LastAncientOne\Anaconda2\lib\site-packages\pandas\io\pytables.py", line 4166, in write
obj=obj, data_columns=data_columns, **kwargs)
File "C:\Users\LastAncientOne\Anaconda2\lib\site-packages\pandas\io\pytables.py", line 3757, in write
**kwargs)
File "C:\Users\LastAncientOne\Anaconda2\lib\site-packages\pandas\io\pytables.py", line 3434, in create_axes
raise e
TypeError: [unicode] is not implemented as a table column
Using Pandas u'0.17.1'
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} | 2 | 2016-02-25T23:47:06Z | 2016-03-08T23:39:51Z | 2016-03-08T23:39:51Z | MEMBER | null | While using master a bit, I discovered some more cases where the new resample API breaks things:
- Plotting. `.plot` is a dedicated groupby/resample method (which adds each group individually to the plot), while I think it is a very common idiom to quickly resample your timeseries and plot it with (old API) eg `s.resample('D').plot()`.
Example with master:
```
In [1]: s = pd.Series(np.random.randn(60), index=date_range('2016-01-01', periods=60, freq='1min'))
In [3]: s.resample('15min').plot()
Out[3]:
2016-01-01 00:00:00 Axes(0.125,0.1;0.775x0.8)
2016-01-01 00:15:00 Axes(0.125,0.1;0.775x0.8)
2016-01-01 00:30:00 Axes(0.125,0.1;0.775x0.8)
2016-01-01 00:45:00 Axes(0.125,0.1;0.775x0.8)
Freq: 15T, dtype: object
```
while previously it would just have given you one continuous line.
This one can be solved I think by special casing `plot` for `resample` (not have it a special groupby-like method, but let it warn and pass the the `resample().mean()` result to `Series.plot()` like the 'deprecated_valids')
- When you previously called a method on the `resample` result that is also a valid Resampler method now. Eg `s.resample(freq).min()` would previously have given you the "minimum daily average" while now it will give you the "minimum per day".
This one is more difficult/impossible to solve I think? As you could detect that case if you know it is old code, but cannot distinguish it from perfectly valid code with the new API. If we can't solve it, I think it deserves some mention in the whatsnew explanation.
- Using `resample` on a `groupby` object (xref #12202). Using the example of that issue, with 0.17.1 you get:
```
In [1]: df = pd.DataFrame({'date': pd.date_range(start='2016-01-01', periods=4,
freq='W'),
...: 'group': [1, 1, 2, 2],
...: 'val': [5, 6, 7, 8]})
In [2]: df.set_index('date', inplace=True)
In [3]: df
Out[3]:
group val
date
2016-01-03 1 5
2016-01-10 1 6
2016-01-17 2 7
2016-01-24 2 8
In [4]: df.groupby('group').resample('1D', fill_method='ffill')
Out[4]:
val
group date
1 2016-01-03 5
2016-01-04 5
2016-01-05 5
2016-01-06 5
2016-01-07 5
2016-01-08 5
2016-01-09 5
2016-01-10 6
2 2016-01-17 7
2016-01-18 7
2016-01-19 7
2016-01-20 7
2016-01-21 7
2016-01-22 7
2016-01-23 7
2016-01-24 8
In [5]: pd.__version__
Out[5]: u'0.17.1'
```
while with master you get:
```
In [29]: df.groupby('group').resample('1D', fill_method='ffill')
Out[29]: <pandas.core.groupby.DataFrameGroupBy object at 0x0000000009BA73C8>
```
which will give you different results/error with further operations on that. Also, this case does not raise any FutureWarning (which should, as the user should adapt the code to `groupby().resample('D').ffill()`)
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} | 24 | 2016-02-26T02:00:01Z | 2016-04-19T13:00:54Z | 2016-03-08T23:39:51Z | CONTRIBUTOR | null | make sure .resample(...).plot() warns and returns a correct plotting object
make sure that .groupby(...).resample(....) is hitting warnings when appropriate
closes #12448
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| null | 3 | 2016-02-26T07:03:04Z | 2021-04-23T01:03:17Z | null | NONE | null | I want to make plots of grouped data, where each plot is plotted in a subplot:
I tried without any kwargs in plotting command, i tried with kwargs `subplots=True` and with `layout=(3,1)` but always only one plot which includes every group is plotted
My code:
```
import sys
import datetime
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.dates as mdates
import numpy as np
%matplotlib notebook
dates = pd.date_range('2013-07-14 11:00:00', '2013-07-16 11:00:00', freq = 's')
cats = np.random.choice(['m', 'f', 'c'], len(dates))
data =np.random.randn(len(dates),1)
df = pd.DataFrame(data, dates)
df.columns = ['data']
df['cats']= cats
plt.figure()
ax1 = df.groupby('cats')['data'].plot(subplots=True) #Figure1
plt.figure()
ax2 = df.groupby('cats')['data'].plot() #Figure2
plt.figure()
ax3 = df.groupby('cats')['data'].plot(layout=(3,1)) #Figure3
```
Im using
```
pd.__version__
'0.18.0rc1'
```
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| null | 2 | 2016-02-26T07:24:09Z | 2016-02-26T12:13:23Z | 2016-02-26T12:13:23Z | NONE | null | I'd like to use the kwarg `subplots=True` when plotting histogramms of groupby
My code:
```
import os
import sys
import datetime
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.dates as mdates
import numpy as np
%matplotlib notebook
dates = pd.date_range('2013-07-14 11:00:00', '2013-07-16 11:00:00', freq = 's')
cats = np.random.choice(['m', 'f', 'c'], len(dates))
data =np.random.randn(len(dates),1)
df = pd.DataFrame(data, dates)
df.columns = ['data']
df['cats']= cats
plt.figure()
ax1 = df.groupby('cats')['data'].hist(bins=20, normed=True, subplots=True) #Figure --> not working with subplots=True
```
Would be nice to have this feature in pandas (same would be nice to have in groupby.plot #12450 )
My pandas:
```
pd.__version__
'0.18.0rc1'
```
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| null | 3 | 2016-02-26T08:56:10Z | 2021-04-23T01:03:45Z | null | NONE | null | Hello!
I'm plotting histogramms and line plots from a groupby - object. It works fine and for each group a figure is generated. But i think a nice feature would be to automatically add the groupname (label) as figure title to each figure. Below you can see a line and a histplot from one group
My code:
```
import os
import sys
import datetime
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.dates as mdates
import numpy as np
%matplotlib notebook
dates = pd.date_range('2013-07-14 11:00:00', '2013-07-16 11:00:00', freq = 's')
cats = np.random.choice(['m', 'f', 'c'], len(dates))
data =np.random.randn(len(dates),1)
data2 =np.random.randn(len(dates),1)
df = pd.DataFrame(data, dates)
df.columns = ['data']
df['cats']= cats
df['data2'] = data2
plt.figure()
ax1 = df.groupby('cats').hist(bins=20, normed=True) #Figure1
#ax1 = df.groupby('cats').plot(subplots=True, layout=(3,1)) #Figure1
plt.figure()
ax1 = df.groupby('cats').plot() #Figure1
```
Pandas:
```
pd.__version__
'0.18.0rc1'
```
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} | 12 | 2016-02-26T12:33:57Z | 2016-06-06T23:20:34Z | 2016-06-06T23:20:34Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
df = pd.DataFrame([ {"JOB": "Worker", "NAME": "Bob", "YEAR": 2013, "MONTH": 12, "DAYS": 3, "SALARY": 17},
{"JOB": "Employ", "NAME": "Mary", "YEAR": 2013, "MONTH": 12, "DAYS": 5, "SALARY": 23},
{"JOB": "Worker", "NAME": "Bob", "YEAR": 2014, "MONTH": 1, "DAYS": 10, "SALARY": 100},
{"JOB": "Worker", "NAME": "Bob", "YEAR": 2014, "MONTH": 1, "DAYS": 11, "SALARY": 110},
{"JOB": "Employ", "NAME": "Mary", "YEAR": 2014, "MONTH": 1, "DAYS": 15, "SALARY": 200},
{"JOB": "Worker", "NAME": "Bob", "YEAR": 2014, "MONTH": 2, "DAYS": 8, "SALARY": 80},
{"JOB": "Employ", "NAME": "Mary", "YEAR": 2014, "MONTH": 2, "DAYS": 5, "SALARY": 190}])
df = df.set_index(["JOB", "NAME", "YEAR", "MONTH"], drop=False, append=False)
df = df.pivot_table(index=["JOB", "NAME"], columns=["YEAR", "MONTH"], values=["DAYS", "SALARY"], aggfunc={"DAYS": "mean", "SALARY": "sum"}, margins=True)
print(df)
file = r"E:\TESTE.xlsx"
writer = pd.ExcelWriter(file, engine="xlsxwriter", datetime_format="yyyy-mm-dd")
df.to_excel(writer, sheet_name="TESTE", index=True)
writer.save()
```
#### Expected Output
| | SALARY | | | | DAYS | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| YEAR | | 2013 | 2014 | | All | 2013 | 2014 | | All |
| MONTH | | 12 | 1 | 2 | | 12 | 1 | 2 | |
| JOB | NAME | | | | | | | | |
| Employ | Mary | 23 | 200 | 190 | 413 | 5 | 15.0 | 5.0 | 8.333333 |
| Worker | Bob | 17 | 210 | 80 | 307 | 3 | 10.5 | 8.0 | 8.000000 |
| All | | 40 | 410 | 270 | 720 | 4 | 12.0 | 6.5 | 8.142857 |
But when exporting to Excel, the Month 2, on SALARY and DAYS, merges with the next column and All above.
2014 .......... | All ...........|
1 ..... | 2 ....................... |
15 ... | 5 ..... | 8.333333 |
When exporting to HTML it's ok with the header but, if the value cell has no value then shows cuts the columns.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 2012ServerR2
machine: AMD64
processor: Intel64 Family 6 Model 47 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.1
nose: None
pip: 8.0.2
setuptools: 20.1.1
Cython: None
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: None
xlsxwriter: 0.8.4
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: None
None
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| null | 1 | 2016-02-26T12:42:12Z | 2021-04-23T01:04:29Z | null | NONE | null | I have a Dataframe with a DatetimeIndex spanning more than one day (`freq=1D`).
From this DataFrame i extract a subset with groupby (--> leads to a Dataframe with `freq=None`)
When I plot these DataFrames, the xaxis (DateTimeIndex) is fromatted in two different ways. If freq is specified (`freq=1D`) the xaxis-ticks look like I desire it.
But the Dataframe with `freq=None` the xaxis shows only timestamps without day, month or year (because the Timerange is more than one day some ticks are doubled..and so its confusing and hard to read)
It would be fine if the xaxis-Ticks would be formatted the same way (show year, month and day)
My code:
```
import os
mport sys
import datetime
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.dates as mdates
import numpy as np
%matplotlib notebook
dates = pd.date_range('2013-07-14 11:00:00', '2013-07-16 11:00:00', freq = 's')
cats = np.random.choice(['m', 'f', 'c'], len(dates))
data =np.random.randn(len(dates),1)
data2 =np.random.randn(len(dates),1)
df = pd.DataFrame(data, dates)
df.columns = ['data']
df['cats']= cats
df['data2'] = data2
df.plot()
data_grouped = df.groupby('cats')
df2 = pd.DataFrame(data_grouped.get_group('m'))
df2.plot()
```
Pandas Version
```
pd.__version__
'0.18.0rc1'
```
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} | 4 | 2016-02-26T13:01:28Z | 2019-10-16T04:09:35Z | 2019-10-16T04:09:35Z | NONE | null | Wan't to group a Dataframe with a DatetimeIndex by a defined frequency. I managed to get the groups by using a TimeGrouper object.
But if I plot the `DataFrameGroupBy` object i get the following
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-55-85ee4aed1e5a> in <module>()
27
28 ax1 = grouped_data1.hist(bins = 20, normed=True) # --> groubb
---> 29 ax2 = grouped_data2.hist(bins = 20, normed=True) # --> groupby
C:\Anaconda3\lib\site-packages\pandas\core\groupby.py in wrapper(*args, **kwargs)
528 # exception below
529 if name in _plotting_methods:
--> 530 return self.apply(curried)
531
532 try:
C:\Anaconda3\lib\site-packages\pandas\core\groupby.py in apply(self, func, *args, **kwargs)
637 # ignore SettingWithCopy here in case the user mutates
638 with option_context('mode.chained_assignment', None):
--> 639 return self._python_apply_general(f)
640
641 def _python_apply_general(self, f):
C:\Anaconda3\lib\site-packages\pandas\core\groupby.py in _python_apply_general(self, f)
644
645 return self._wrap_applied_output(keys, values,
--> 646 not_indexed_same=mutated)
647
648 def _iterate_slices(self):
C:\Anaconda3\lib\site-packages\pandas\core\groupby.py in _wrap_applied_output(self, keys, values, not_indexed_same)
3318 # Handle cases like BinGrouper
3319 return self._concat_objects(keys, values,
-> 3320 not_indexed_same=not_indexed_same)
3321
3322 def _transform_general(self, func, *args, **kwargs):
C:\Anaconda3\lib\site-packages\pandas\core\groupby.py in _concat_objects(self, keys, values, not_indexed_same)
807 group_names = self.grouper.names
808 result = concat(values, axis=self.axis, keys=group_keys,
--> 809 levels=group_levels, names=group_names)
810 else:
811
C:\Anaconda3\lib\site-packages\pandas\tools\merge.py in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)
832 keys=keys, levels=levels, names=names,
833 verify_integrity=verify_integrity,
--> 834 copy=copy)
835 return op.get_result()
836
C:\Anaconda3\lib\site-packages\pandas\tools\merge.py in __init__(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy)
888 for obj in objs:
889 if not isinstance(obj, NDFrame):
--> 890 raise TypeError("cannot concatenate a non-NDFrame object")
891
892 # consolidate
TypeError: cannot concatenate a non-NDFrame object
```
If I only plot a specific column (SeriesGroupBy) there is no problem!
My code:
```
import os
import sys
import datetime
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.dates as mdates
import numpy as np
%matplotlib notebook
dates = pd.date_range('2013-07-14 11:00:00', '2013-07-16 11:00:00', freq = 's')
cats = np.random.choice(['m', 'f', 'c'], len(dates))
data =np.random.randn(len(dates),1)
data2 =np.random.randn(len(dates),1)
df = pd.DataFrame(data, dates)
df.columns = ['data']
df['cats']= cats
df['data2'] = data2
plt.figure()
grouped_data1 = df.groupby(pd.TimeGrouper('1D'))['data'] # SeriesGroupBy object
grouped_data2 = df.groupby(pd.TimeGrouper('1D')) # DataFrameGroupBy object
ax1 = grouped_data1.hist(bins = 20, normed=True) # Does work
ax2 = grouped_data2.hist(bins = 20, normed=True) # gives Type Error
```
Pandas Version:
```
pd.__version__
'0.18.0rc1'
```
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} | 1 | 2016-02-26T15:36:40Z | 2016-05-07T17:12:30Z | 2016-05-07T17:12:30Z | CONTRIBUTOR | null | - [x] closes #10486
- [N/A] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
…ace operator
The relevant issues added are :
BUG: query with invalid dtypes should fallback to python engine #10486
BUG: query modifies the frame when you compare with `=` #8664
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} | 22 | 2016-02-26T15:57:00Z | 2021-04-23T01:10:16Z | 2021-04-23T01:10:16Z | NONE | null | Attempting to create a DataFrame with a single column that is multiindexed, I get "IndexError: list index out of range".
#### Code Sample, a copy-pastable example if possible
```
df = pd.DataFrame(data=zip(range(100)), columns=[['a','b']])
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-9-5b88e876b2de> in <module>()
----> 1 df = pd.DataFrame(data=zip(range(100)), columns=[['a','b']])
/Users/ewozniak/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in __init__(self, data, index, columns, dtype, copy)
273
274 mgr = _arrays_to_mgr(arrays, columns, index, columns,
--> 275 dtype=dtype)
276 else:
277 mgr = self._init_ndarray(data, index, columns, dtype=dtype,
/Users/ewozniak/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in _arrays_to_mgr(arrays, arr_names, index, columns, dtype)
5236 axes = [_ensure_index(columns), _ensure_index(index)]
5237
-> 5238 return create_block_manager_from_arrays(arrays, arr_names, axes)
5239
5240
/Users/ewozniak/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc in create_block_manager_from_arrays(arrays, names, axes)
3894
3895 try:
-> 3896 blocks = form_blocks(arrays, names, axes)
3897 mgr = BlockManager(blocks, axes)
3898 mgr._consolidate_inplace()
/Users/ewozniak/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc in form_blocks(arrays, names, axes)
3929
3930 k = names[name_idx]
-> 3931 v = arrays[name_idx]
3932
3933 if is_sparse(v):
IndexError: list index out of range
```
Note, I use zip() in the example above in order to match what the data would look like when creating one dataframe from the data of another data frame using `df.to_json(orient='split')` (which is what I'm trying to do). If I don't use zip(), then I get:
```
df = pd.DataFrame(data=range(100), columns=[['a','b']])
ValueError: Shape of passed values is (1, 100), indices imply (2, 100)
```
This works fine with two (or more) columns:
```
df = pd.DataFrame(data=zip(range(100),range(100)), columns=[['a','b'],['c','d']])
```
#### output of `pd.show_versions()`
I have tried with both pandas 0.17.1 and 0.18.0rc1.
## INSTALLED VERSIONS
commit: None
python: 2.7.11.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: 0.18.0rc1
nose: 1.3.7
pip: 8.0.3
setuptools: 20.1.1
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
jinja2: 2.8
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} | 2 | 2016-02-26T17:58:41Z | 2016-02-27T15:33:54Z | 2016-02-27T15:33:36Z | NONE | null | - [ ] closes #6625
- [ ] tests passed so far
- [ ] passes `git diff upstream/master | flake8 --diff`
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} | 3 | 2016-02-26T18:06:59Z | 2016-02-27T16:30:24Z | 2016-02-27T14:41:27Z | CONTRIBUTOR | null | Closes #8708
Added note that pd.lib.isscalar() returns True for None, which is different from np.isscalar().
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| null | 0 | 2016-02-26T18:12:05Z | 2021-04-23T01:11:08Z | null | NONE | null | In R, the summary(data) function provides counts for categorical data of the most frequent values. For datetime and numeric values, it gives their mean, quartiles and range.
The describe function in Pandas is similar but does not provide such a human-readable data output for heterogeneous dataframes. As the output is primarily intended for human consumption, we could make a new summary() function with a new tabular layout designed solely for human consumption.
#### Examples of the R summary function
Heterogeneous columns from input data below:
```
A,1
B,2
C,3
A,1
```
```
summary(d)
V1 V2
A:2 Min. :1.00
B:1 1st Qu.:1.75
C:1 Median :2.50
Mean :2.75
3rd Qu.:3.50
Max. :5.00
```
Datetime example:
```
datetime data
Min. :2012-12-22 12:00:00 Min. :-1.392
1st Qu.:2012-12-22 18:00:00 1st Qu.: 7.906
Median :2012-12-23 00:00:00 Median :10.836
Mean :2012-12-23 00:00:00 Mean :11.271
3rd Qu.:2012-12-23 06:00:00 3rd Qu.:16.362
Max. :2012-12-23 12:00:00 Max. :21.097
```
Discussed with @jreback and he suggested that starting with a new function rather than changing describe.
This is how describe works on heterogeneous columns now, showing how it is difficult to read:
```
In [3]: df = pd.DataFrame({'A' : list('aab'), 'B' : range(3)})
In [4]: df
Out[4]:
A B
0 a 0
1 a 1
2 b 2
In [5]: df.describe()
Out[5]:
B
count 3.0
mean 1.0
std 1.0
min 0.0
25% 0.5
50% 1.0
75% 1.5
max 2.0
In [7]: df.describe(include='all')
Out[7]:
A B
count 3 3.0
unique 2 NaN
top a NaN
freq 2 NaN
mean NaN 1.0
std NaN 1.0
min NaN 0.0
25% NaN 0.5
50% NaN 1.0
75% NaN 1.5
max NaN 2.0
```
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} | 3 | 2016-02-26T18:21:08Z | 2016-02-27T14:46:40Z | 2016-02-27T14:46:28Z | CONTRIBUTOR | null | - [X] closes #8020
- [X] tests added / passed
- [X] passes `git diff upstream/master | flake8 --diff`
- [X] whatsnew entry
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} | 11 | 2016-02-26T18:23:29Z | 2016-04-06T13:00:19Z | 2016-04-06T13:00:10Z | CONTRIBUTOR | null | BUG: fix issue with concat of localized timestamps
TST: test concat of dataframes with non-None timezone columns
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} | 4 | 2016-02-26T18:42:14Z | 2016-02-27T18:05:01Z | 2016-02-27T16:02:50Z | CONTRIBUTOR | null | - [x] closes #8848
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Not 100% complete, but putting this in as a place to house comments:
- I am not sure where to put the whatsnew entry - 0.18.0 or 0.18.1?
- Also not sure if you'd like tests on the actual usage in the .dt accessor, there are actually no tests anywhere in pandas (as far as I can tell) on period_series.dt.\* (as opposed to PeriodIndex). I just added a couple PeriodIndex tests.
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| null | 4 | 2016-02-26T18:48:30Z | 2016-02-27T15:17:19Z | 2016-02-27T02:28:26Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import numpy as np
import pandas as pd
np.seterr(all='raise')
s = pd.Series([np.nan,np.nan,np.nan],index=[1,2,3]); print(s); print(s.head())
```
#### Expected Output
Certainly not a FloatingPointError:
`FloatingPointError: invalid value encountered in greater.`
The issue appears to lie in numpy, as
`np.array([np.nan, np.nan]) > 1e8`
also raises the error. I have cross-posted the issue [there](https://github.com/numpy/numpy/issues/7352), but thought you guys also would want to be aware of this.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-49-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: None
pip: 8.0.3
setuptools: 20.1.1
Cython: None
numpy: 1.10.4
scipy: 0.16.0
statsmodels: None
IPython: 4.0.1
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: 2.5
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
Jinja2: None
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} | 1 | 2016-02-26T18:51:34Z | 2016-02-27T15:23:16Z | 2016-02-27T15:22:59Z | NONE | null | - closes #5986
- 3 tests added / passed: rank, factorize, mode
- confirm the other 3 tests already exist: unique, quantile, value_counts
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| null | 1 | 2016-02-26T19:20:09Z | 2016-02-26T19:48:17Z | 2016-02-26T19:48:03Z | NONE | null | #### Issue
The following code will raise a TypeError exception:
``` python
from pandas import DataFrame
import numpy as np
df = DataFrame(np.random.randn(5,2), columns=['x', 'y'])
df['s'] = np.random.random_integers(low=100, high=200, size=5)
df.plot.scatter(x='x', y='y', s='s')
```
However, there's no problem in using matplotlib directly:
``` python
from pandas import DataFrame
import numpy as np
import matplotlib.pyplot as plt
df = DataFrame(np.random.randn(5,2), columns=['x', 'y'])
df['s'] = np.random.random_integers(low=100, high=200, size=5)
plt.scatter(x=df['x'], y=df['y'], s=df['s'])
```
Note: This issue only arises when argument `s` is used. The ordinary x-y scatter plot works fine.
#### Traceback details
``` python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-28-a2c96d4293aa> in <module>()
4 df = DataFrame(np.random.randn(5,2), columns=['x', 'y'])
5 df['s'] = np.random.random_integers(low=100, high=200, size=5)
----> 6 df.plot.scatter(x='x', y='y', s='s')
/Users/fzw/anaconda/envs/ds/lib/python3.5/site-packages/pandas/tools/plotting.py in scatter(self, x, y, s, c, **kwds)
3847 axes : matplotlib.AxesSubplot or np.array of them
3848 """
-> 3849 return self(kind='scatter', x=x, y=y, c=c, s=s, **kwds)
3850
3851 def hexbin(self, x, y, C=None, reduce_C_function=None, gridsize=None,
/Users/fzw/anaconda/envs/ds/lib/python3.5/site-packages/pandas/tools/plotting.py in __call__(self, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)
3669 fontsize=fontsize, colormap=colormap, table=table,
3670 yerr=yerr, xerr=xerr, secondary_y=secondary_y,
-> 3671 sort_columns=sort_columns, **kwds)
3672 __call__.__doc__ = plot_frame.__doc__
3673
/Users/fzw/anaconda/envs/ds/lib/python3.5/site-packages/pandas/tools/plotting.py in plot_frame(data, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)
2554 yerr=yerr, xerr=xerr,
2555 secondary_y=secondary_y, sort_columns=sort_columns,
-> 2556 **kwds)
2557
2558
/Users/fzw/anaconda/envs/ds/lib/python3.5/site-packages/pandas/tools/plotting.py in _plot(data, x, y, subplots, ax, kind, **kwds)
2382 plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)
2383
-> 2384 plot_obj.generate()
2385 plot_obj.draw()
2386 return plot_obj.result
/Users/fzw/anaconda/envs/ds/lib/python3.5/site-packages/pandas/tools/plotting.py in generate(self)
985 self._compute_plot_data()
986 self._setup_subplots()
--> 987 self._make_plot()
988 self._add_table()
989 self._make_legend()
/Users/fzw/anaconda/envs/ds/lib/python3.5/site-packages/pandas/tools/plotting.py in _make_plot(self)
1557 label = None
1558 scatter = ax.scatter(data[x].values, data[y].values, c=c_values,
-> 1559 label=label, cmap=cmap, **self.kwds)
1560 if cb:
1561 img = ax.collections[0]
/Users/fzw/anaconda/envs/ds/lib/python3.5/site-packages/matplotlib/__init__.py in inner(ax, *args, **kwargs)
1810 warnings.warn(msg % (label_namer, func.__name__),
1811 RuntimeWarning, stacklevel=2)
-> 1812 return func(ax, *args, **kwargs)
1813 pre_doc = inner.__doc__
1814 if pre_doc is None:
/Users/fzw/anaconda/envs/ds/lib/python3.5/site-packages/matplotlib/axes/_axes.py in scatter(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, **kwargs)
3891 offsets=offsets,
3892 transOffset=kwargs.pop('transform', self.transData),
-> 3893 alpha=alpha
3894 )
3895 collection.set_transform(mtransforms.IdentityTransform())
/Users/fzw/anaconda/envs/ds/lib/python3.5/site-packages/matplotlib/collections.py in __init__(self, paths, sizes, **kwargs)
831 Collection.__init__(self, **kwargs)
832 self.set_paths(paths)
--> 833 self.set_sizes(sizes)
834 self.stale = True
835
/Users/fzw/anaconda/envs/ds/lib/python3.5/site-packages/matplotlib/collections.py in set_sizes(self, sizes, dpi)
804 self._sizes = np.asarray(sizes)
805 self._transforms = np.zeros((len(self._sizes), 3, 3))
--> 806 scale = np.sqrt(self._sizes) * dpi / 72.0 * self._factor
807 self._transforms[:, 0, 0] = scale
808 self._transforms[:, 1, 1] = scale
TypeError: ufunc 'sqrt' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 13.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: None
pip: 8.0.1
setuptools: 19.4
Cython: None
numpy: 1.10.2
scipy: 0.16.1
statsmodels: None
IPython: 4.0.3
sphinx: None
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
```
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} | 2 | 2016-02-26T19:33:34Z | 2016-04-06T13:00:10Z | 2016-04-06T13:00:10Z | CONTRIBUTOR | null | This snippet
``` python
import pandas as pd
df1 = pd.DataFrame(dict(time=[pd.Timestamp('2015-01-01', tz=None)]))
df2 = pd.DataFrame(dict(time=[pd.Timestamp('2015-01-01', tz='UTC')]))
pd.concat([df1, df2])
```
causes
> .../pandas/tseries/common.py in _concat_compat(to_concat, axis)
> 282 if 'datetime' in typs or 'object' in typs:
> 283 to_concat = [convert_to_pydatetime(x, axis) for x in to_concat]
> --> 284 return np.concatenate(to_concat, axis=axis)
> 285
> 286 # we require ALL of the same tz for datetimetz
>
> ValueError: all the input arrays must have same number of dimensions
We would expect it to return something like this
> pd.DataFrame(dict(time=pd.Series([pd.Timestamp('2015-01-01', tz=None), pd.Timestamp('2015-01-01', tz='UTC')], dtype=object)))
> Out[18]:
> time
> 0 2015-01-01 00:00:00
> 1 2015-01-01 00:00:00+00:00
output of `pd.show_versions()`
### INSTALLED VERSIONS
commit: fe584e7829dc4dd9fa585a7fbf5fd47fc6f4b057
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-53-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0rc1+47.gfe584e7
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: 0.9.4
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
jinja2: 2.8
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} | 1 | 2016-02-26T19:58:05Z | 2016-02-27T14:28:15Z | 2016-02-27T14:28:05Z | CONTRIBUTOR | null | Closes #12176
- [✓] tests pass
Manually tested with condas environments w/ all and no dependencies.
- [✓] passes `git diff upstream/master | flake8 --diff`
- [✗] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/12469 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12469/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12469/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12469/events | https://github.com/pandas-dev/pandas/pull/12469 | 136,799,611 | MDExOlB1bGxSZXF1ZXN0NjA4OTU2NzU= | 12,469 | CLN: consolidate Series.quantile and DataFrame.quantile | {
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| null | 2 | 2016-02-26T20:02:49Z | 2016-04-03T17:03:18Z | 2016-04-03T17:03:18Z | NONE | null | - closes #10207
- Slightly modified test in tests/series/tests_analytics.py:575 to standardize behavior across Series and DataFrame
- passes `git diff upstream/master | flake8 --diff`
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https://api.github.com/repos/pandas-dev/pandas/issues/12470 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12470/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12470/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12470/events | https://github.com/pandas-dev/pandas/issues/12470 | 136,809,824 | MDU6SXNzdWUxMzY4MDk4MjQ= | 12,470 | [Feature Request] Pass array of timezones to pandas.Series.tz_convert | {
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| null | 4 | 2016-02-26T20:43:29Z | 2016-02-27T14:20:46Z | 2016-02-27T02:51:06Z | NONE | null | This feature request spurs from this StackOverflow question: http://stackoverflow.com/questions/35659927/how-can-i-get-the-timezone-adjusted-hour-for-each-row-in-a-pandas-dataframe-when
The relevant portions are copied here.
Given a DataFrame like:
```
# publication_timestamp timezone
# uuid
#0 2015-07-28 00:10:05.852 US/Central
#1 2015-10-03 00:17:43.000 US/Eastern
#2 2015-08-20 01:15:52.693 US/Eastern
#3 2015-09-09 00:02:03.083 US/Pacific
#4 2015-12-08 00:02:41.390 US/Mountain
```
I would like to output a DataFrame like this:
```
# publication_timestamp timezone hour adjusted_hour time_of_day
# uuid
#0 2015-07-28 00:10:05.852 US/Central 0 19 evening
#1 2015-10-03 00:17:43.000 US/Eastern 0 20 evening
#2 2015-08-20 01:15:52.693 US/Eastern 1 21 evening
#3 2015-09-09 00:02:03.083 US/Pacific 0 17 evening
#4 2015-12-08 00:02:41.390 US/Mountain 0 17 evening
```
I would like to be able to pass `tz_convert` a column of timezones and have each row using a different timezone.
**I understand that you can't (shouldn't?) combine multiple dtypes in the same column**, HOWEVER - I would like to just use this as an intermediate step in getting the timezone adjusted hour (i.e. time of day) for each row.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.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: 8.0.3
setuptools: 19.4
Cython: None
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
IPython: 4.1.1
sphinx: 1.3.5
patsy: 0.4.1
dateutil: 2.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: 2.2.0-b1
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: 3.5.0
bs4: 4.4.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.4.2
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
Jinja2: 2.8
```
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} | 2 | 2016-02-26T20:43:54Z | 2016-02-27T14:56:53Z | 2016-02-27T14:55:55Z | CONTRIBUTOR | null | - [x] progress towards closing #5385
- [x] whatsnew entry
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} | 5 | 2016-02-26T20:54:16Z | 2016-04-27T14:02:18Z | 2016-04-27T14:01:13Z | CONTRIBUTOR | null | - [ ] closes #9815
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
This is the beginning of a quick reference section. It's incomplete,
just did a rough translation of
http://nbviewer.jupyter.org/urls/gist.githubusercontent.com/TomAugspurger/6e052140eaa5fdb6e8c0/raw/811585624e843f3f80b9b6fe89e18119d7d2d73c/dplyr_pandas.ipynb
into tables. Should try to get some R experts to comment, and it would
be nice to have the pandas versions link to docs for the functions being
used, but I'm terrible at reStructuredText and gave up for the moment.
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} | 2 | 2016-02-26T21:04:14Z | 2020-07-15T19:58:08Z | 2016-04-18T17:17:44Z | NONE | null | Create test DF.
```
df = pd.DataFrame({"uuid": [0,1,2,3,4], "publication_timestamp": ["2015-07-28 00:10:05.852", "2015-10-03 00:17:43.000", "2015-08-20 01:15:52.693", "2015-09-09 00:02:03.083", "2015-12-08 00:02:41.390"], "timezone": ["US/Central", "US/Eastern", "US/Eastern", "US/Pacific", "US/Mountain"]}).set_index('uuid')
```
```
publication_timestamp timezone
uuid
0 2015-07-28 04:10:05.852000+00:00 US/Central
1 2015-10-03 04:17:43+00:00 US/Eastern
2 2015-08-20 05:15:52.693000+00:00 US/Eastern
3 2015-09-09 04:02:03.083000+00:00 US/Pacific
4 2015-12-08 05:02:41.390000+00:00 US/Mountain
```
This call to `map` works fine:
```
df.publication_timestamp.map(lambda x: x) # works fine
```
**Localizing the datetime64 causes it to no longer have the map function**
```
df['publication_timestamp'] = df.publication_timestamp.astype("datetime64[ms]").dt.tz_localize("UTC")
```
Doesn't work:
```
df.publication_timestamp.map(lambda x: x) # no longer works
```
#### Error Message
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-13-ba941613799a> in <module>()
----> 1 df.publication_timestamp.map(lambda x: x)
/Users/johria/.pyenv/versions/3.5.1/lib/python3.5/site-packages/pandas/core/series.py in map(self, arg, na_action)
2052 index=self.index).__finalize__(self)
2053 else:
-> 2054 mapped = map_f(values, arg)
2055 return self._constructor(mapped,
2056 index=self.index).__finalize__(self)
TypeError: Argument 'arr' has incorrect type (expected numpy.ndarray, got DatetimeIndex)
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.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: 8.0.3
setuptools: 19.4
Cython: None
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
IPython: 4.1.1
sphinx: 1.3.5
patsy: 0.4.1
dateutil: 2.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: 2.2.0-b1
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: 3.5.0
bs4: 4.4.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.4.2
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
Jinja2: 2.8
```
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} | 1 | 2016-02-26T22:59:24Z | 2016-02-27T16:10:12Z | 2016-02-27T16:10:01Z | CONTRIBUTOR | null | - [ ] closes #10165
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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} | 2 | 2016-02-27T00:36:51Z | 2016-02-27T10:55:27Z | 2016-02-27T01:23:17Z | NONE | null | #### Code Sample, a copy-pastable example if possible
python 3.5
pandas 0.17
for g, grp in p.groupby(level='stock'):
grp.to_csv('quotes/{0}.csv'.format(g))
#### Expected Output
save all the quotes in csv
#### output of `pd.show_versions()`
Traceback (most recent call last):
File "<ipython-input-21-7884f2db115e>", line 79, in <module>
grp.to_csv('quotes/{0}.csv'.format(g))
File "C:\Users\LastAncientOne\Anaconda3\lib\site-packages\pandas\core\frame.py", line 1332, in to_csv
formatter.save()
File "C:\Users\LastAncientOne\Anaconda3\lib\site-packages\pandas\core\format.py", line 1481, in save
compression=self.compression)
File "C:\Users\LastAncientOne\Anaconda3\lib\site-packages\pandas\io\common.py", line 354, in _get_handle
f = open(path, mode, errors='replace')
FileNotFoundError: [Errno 2] No such file or directory: 'quotes/AMZN.csv'
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| null | 2 | 2016-02-27T00:40:15Z | 2016-05-07T19:07:36Z | 2016-05-07T19:07:35Z | NONE | null | First cut. Please let me know your thoughts and thanks again to @jreback for teaching me.
Looks like this
```
A_cat B_str C_bool D_num \
0 count=24 count=24 count=24 count=24
1 cardinality=2 cardinality=4 mean=0.5 mean=12
2 value:foo=16 (67%) value:d=6 (25%) std=0.511 std=7.07
3 value:bar=8 (33%) value:b=6 (25%) min:p47=False min=0.5
4 value:c=6 (25%) median=True p25=6.25
5 value:a=6 (25%) p53:max=True median=12
6 cardinality=2 p75=17.8
7 value:True=12 (50%) max=23.5
8 value:False=12 (50%) cardinality=24
E_regular
0 count=24
1 mean=4.17
2 std=3.14
3 min:p65=2
4 p70:p82=8
5 p87:max=9
6 cardinality=3
7 value:2=16 (67%)
8
```
for input
```
df = pd.DataFrame({'A_cat': pd.Categorical(['foo', 'foo', 'bar'] * 8),
'B_str': ['a', 'b', 'c', 'd'] * 6,
'C_bool': [True] * 12 + [False] * 12,
'D_num': np.arange(24.) + .5,
'E_regular': [9,8,2,2,2,2]*4,
})
```
- [ ] closes #12460
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} | 1 | 2016-02-27T01:00:05Z | 2016-02-27T17:53:27Z | 2016-02-27T17:52:43Z | NONE | null | - [ ] closes #10503
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2016-02-27T02:13:19Z | 2016-02-27T10:55:40Z | 2016-02-27T02:15:42Z | NONE | null | #### Code Sample, a copy-pastable example if possible
rets.rolling(window=22).min().plot.line(figsize=(15,10))
#### Expected Output
#### output of `pd.show_versions()`
Traceback (most recent call last):
File "<ipython-input-78-e1069c24fa37>", line 1, in <module>
rets.rolling(window=22).min().plot.line(figsize=(15,10))
File "C:\Users\LastAncientOne\Anaconda3\lib\site-packages\pandas\core\generic.py", line 2360, in **getattr**
(type(self).**name**, name))
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} | 1 | 2016-02-27T06:09:33Z | 2016-02-27T18:17:52Z | 2016-02-27T15:09:04Z | MEMBER | null | Closes #10712. This is already fixed in other PR, thus added explicit tests.
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} | 6 | 2016-02-27T12:34:04Z | 2017-08-07T10:50:24Z | 2017-08-06T21:15:06Z | NONE | null | Timestamp stores the value in a `datetime64[ns]` internal representation. For most purposes this is fine, but I'd love if I could choose the internal precision, for example to use `datetime64[s]` instead.
When I do timeseries manipulations that span more than a day, I generally do not need sub-second resolution and I could definitely use the extended range instead. But, more conveniently, this would also allow to pretty-print the result without a ton of trailing zeros without setting some arbitrary threshold.
Incidentally, this would remove the limit of ~500 years, but it's more of an ergonomic change than an actual need. For example, when I need to perform direct manipulations of the values in a DatetimeIndex, I often have to scale up to seconds if I want to perform meaningful fitting and avoid numerical instability. When plotting, it's an extra indirection I need to care about.
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| null | 8 | 2016-02-27T15:27:12Z | 2016-04-18T18:04:39Z | 2016-04-18T17:52:32Z | CONTRIBUTOR | null | - Closes #12373
- Added test_rolling_min_max_types to test_window.py
- passes `git diff upstream/master | flake8 --diff`
Ran the performance tests with no changes detected:
$ asv continuous -b 'stat_op.*rolling' upstream/master GH12373
· Creating environments
· Discovering benchmarks
·· Uninstalling from py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
·· Building for py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt..................................................
·· Installing into py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt..
· Running 18 total benchmarks (2 commits \* 1 environments \* 9 benchmarks)
[ 0.00%] · For pandas commit hash 170fb27a:
[ 0.00%] ·· Building for py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt.......................................................
[ 0.00%] ·· Benchmarking py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[ 5.56%] ··· Running stat_ops.stats_rolling_mean.time_rolling_kurt 11.39ms
[ 11.11%] ··· Running stat_ops.stats_rolling_mean.time_rolling_max 7.48ms
[ 16.67%] ··· Running stat_ops.stats_rolling_mean.time_rolling_mean 7.22ms
[ 22.22%] ··· Running stat_ops.stats_rolling_mean.time_rolling_median 114.21ms
[ 27.78%] ··· Running stat_ops.stats_rolling_mean.time_rolling_min 7.55ms
[ 33.33%] ··· Running stat_ops.stats_rolling_mean.time_rolling_skew 11.35ms
[ 38.89%] ··· Running stat_ops.stats_rolling_mean.time_rolling_std 9.13ms
[ 44.44%] ··· Running stat_ops.stats_rolling_mean.time_rolling_sum 6.93ms
[ 50.00%] ··· Running stat_ops.stats_rolling_mean.time_rolling_var 7.98ms
[ 50.00%] · For pandas commit hash 56e285a6:
[ 50.00%] ·· Building for py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt.......................................................
[ 50.00%] ·· Benchmarking py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[ 55.56%] ··· Running stat_ops.stats_rolling_mean.time_rolling_kurt 11.66ms
[ 61.11%] ··· Running stat_ops.stats_rolling_mean.time_rolling_max 8.68ms
[ 66.67%] ··· Running stat_ops.stats_rolling_mean.time_rolling_mean 7.48ms
[ 72.22%] ··· Running stat_ops.stats_rolling_mean.time_rolling_median 107.71ms
[ 77.78%] ··· Running stat_ops.stats_rolling_mean.time_rolling_min 8.74ms
[ 83.33%] ··· Running stat_ops.stats_rolling_mean.time_rolling_skew 11.53ms
[ 88.89%] ··· Running stat_ops.stats_rolling_mean.time_rolling_std 9.25ms
[ 94.44%] ··· Running stat_ops.stats_rolling_mean.time_rolling_sum 6.97ms
[100.00%] ··· Running stat_ops.stats_rolling_mean.time_rolling_var 8.24msBENCHMARKS NOT SIGNIFICANTLY CHANGED.
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} | 30 | 2016-02-27T15:47:56Z | 2016-05-20T16:14:51Z | 2016-05-20T14:16:17Z | CONTRIBUTOR | null | - [X] closes #10758
- [X] tests added / passed
- [X] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
If more tests are desired, please let me know where to put them. Same for the whatsnew entry. This is my first pull request for Pandas.
cc @jreback
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} | 10 | 2016-02-27T15:53:02Z | 2016-03-06T20:45:41Z | 2016-03-06T20:44:23Z | CONTRIBUTOR | null | - [X] closes #10041
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Includes some refactoring in the ISO8601 parser. If more tests are desired, please let me know where to put them. Same for the whatsnew entry. This is effectively my first pull request for Pandas.
cc @jreback
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} | 1 | 2016-02-27T16:26:01Z | 2016-04-26T17:22:57Z | 2016-04-26T17:22:44Z | NONE | null | I'm working on a little pandas lesson. The CSV has a column with leading '0' which should be treated as text. When I try to read the CSV and indicate that that column is the `index`, the `dtype` argument doesn't work. I can use `S7` as the `dtype` instead of `object` but @wesm [suggests using 'object'](http://stackoverflow.com/questions/16929056/pandas-read-csv-dtype-leading-zeros#comment24510329_16929535)
#### Code Sample, a copy-pastable example if possible
from StringIO import StringIO
import pandas as pd
data = """
fips,name,popest2014
0800760,"Aguilar town, Colorado",479
0800925,"Akron town, Colorado",1694
0801090,"Alamosa city, Colorado",9531
"""
error_case = pd.read_csv(StringIO(data), index_col=0, dtype={'fips': object})
print "Error case, dtype should be object "
print error_case.index.dtype # 'int64'
expected1 = pd.read_csv(StringIO(data), dtype={'fips': object})
print "Works with object, not an index column"
print expected1.fips.dtype # 'O'
expected2 = pd.read_csv(StringIO(data), index_col=0, dtype={'fips': 'S7'})
print "Works with 'S7', as an index column"
print expected2.index.dtype # 'O'
#### Expected Output
That the dtype for the FIPS column as an index would be `object`
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.9.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: 0.17.1
nose: None
pip: 8.0.3
setuptools: 20.1.1
Cython: None
numpy: 1.10.4
scipy: None
statsmodels: None
IPython: 4.1.1
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: None
```
```
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| null | 4 | 2016-02-27T20:00:35Z | 2016-03-11T15:43:44Z | 2016-03-11T15:43:44Z | CONTRIBUTOR | null | Hey folks, I'm starting to use the pyspark.ml library and it'd be cool to create a translation layer from pandas to spark to make it easy for local code to easily translate into spark. I have two ideas and would would be great to hear feedback. If positive I'd be happy to create a PR.
1) Direct translation
```
spark_df = pandas_df.to_spark(spark_context)
```
2) Pseudo translation: only permit spark compatible operations on this DataFrame
```
restricted_spark_df = pandas_df.restrict_spark()
```
Would error if any operation that would not work on a spark df were performed on this df. Harder, but potentially useful for seeing if code designed for local use can be used for spark, without even installing spark.
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} | 0 | 2016-02-27T20:24:10Z | 2016-04-26T15:03:02Z | 2016-04-26T15:03:02Z | CONTRIBUTOR | null | xref #12448 / #12449
and on [SO](http://stackoverflow.com/questions/36293620/pandas-0-18-changes-to-resample-how-to-upsample-with-groupby/36294398#36294398)
```
In [1]: df = DataFrame({'date': pd.date_range(start='2016-01-01',
...: periods=4,
...: freq='W'),
...: 'group': [1, 1, 2, 2],
...: 'val': [5, 6, 7, 8]}).set_index('date')
In [2]: df
Out[2]:
group val
date
2016-01-03 1 5
2016-01-10 1 6
2016-01-17 2 7
2016-01-24 2 8
```
This replicates 0.17.1 (something slightly off with it including the grouper column)
```
In [3]: df.groupby('group').apply(lambda x: x.resample('1D').ffill())[['val']]
Out[3]:
val
group date
1 2016-01-03 5
2016-01-04 5
2016-01-05 5
2016-01-06 5
2016-01-07 5
2016-01-08 5
2016-01-09 5
2016-01-10 6
2 2016-01-17 7
2016-01-18 7
2016-01-19 7
2016-01-20 7
2016-01-21 7
2016-01-22 7
2016-01-23 7
2016-01-24 8
# ideally this would work. Its possible but requires some intelligently filling according to each group level.
In [4]: df.groupby('group').resample('1D').ffill()
Out[4]:
group val
date
2016-01-03 1 5
2016-01-10 1 6
2016-01-17 2 7
```
A pure asfreq operation
```
data = [['2010-01-01', 'A', 2], ['2010-01-02', 'A', 3], ['2010-01-05', 'A', 8],
['2010-01-10', 'A', 7], ['2010-01-13', 'A', 3], ['2010-01-01', 'B', 5],
['2010-01-03', 'B', 2], ['2010-01-04', 'B', 1], ['2010-01-11', 'B', 7],
['2010-01-14', 'B', 3]]
df = pd.DataFrame(data, columns=['Date', 'ID', 'Score'])
df.Date = pd.to_datetime(df.Date)
In [27]: df.groupby('ID').apply(lambda x: x.set_index('Date').Score.resample('D').asfreq())
Out[27]:
ID Date
A 2010-01-01 2.0
2010-01-02 3.0
2010-01-03 NaN
2010-01-04 NaN
2010-01-05 8.0
2010-01-06 NaN
2010-01-07 NaN
2010-01-08 NaN
2010-01-09 NaN
2010-01-10 7.0
2010-01-11 NaN
2010-01-12 NaN
2010-01-13 3.0
B 2010-01-01 5.0
2010-01-02 NaN
2010-01-03 2.0
2010-01-04 1.0
2010-01-05 NaN
2010-01-06 NaN
2010-01-07 NaN
2010-01-08 NaN
2010-01-09 NaN
2010-01-10 NaN
2010-01-11 7.0
2010-01-12 NaN
2010-01-13 NaN
2010-01-14 3.0
Name: Score, dtype: float64
```
Would be nice for this to work
```
df.groupby(['ID',pd.Grouper(key='Date',freq='D')]).asfreq()
```
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} | 2 | 2016-02-28T08:26:08Z | 2016-03-04T22:23:40Z | 2016-03-02T12:43:50Z | MEMBER | null | - Merged `strings._wrap_result` and `strings._wrap_result_expand` for cleanup.
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} | 1 | 2016-02-28T15:56:09Z | 2016-02-28T23:11:36Z | 2016-02-28T23:11:31Z | CONTRIBUTOR | null | fix typo in comment
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} | 5 | 2016-02-28T17:40:29Z | 2016-06-28T15:58:03Z | 2016-03-03T12:11:02Z | CONTRIBUTOR | null | Under some circumstances, numexpr 2.4.4 can lead to flaky output (see #12023 and links therein). This happened again once on SO in the last few days, and it's a suspect in another case.
[UPDATE: the suspect was convicted of the second crime as well!]
We should just refuse to work with 2.4.4, rather than leave the impression pandas is unstable, and require a later version as soon as we can. There are apparently still enough people out there with up-to-date pandas and out-of-date numexpr that a warning could probably help someone. Accordingly, after some discussion on gitter, it sounds like we should
(1) add a warning to the docs
(2) centralize the version check (right now both computation/eval and computation/expressions check the numexpr version)
(3) only allow numexpr if >= 2.1 and not == 2.4.4; otherwise, set _USE_NUMEXPR to False
(4) in the warning we raise, recommend upgrading from 2.4.4 because we can't easily prevent other dependencies from using it
(5) add a test to make sure that the presence of 2.4.4 disables _USE_NUMEXPR (say, by adding it to the requirements-3.4_SLOW).
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} | 7 | 2016-02-28T23:07:54Z | 2016-03-01T22:27:12Z | 2016-03-01T22:27:12Z | CONTRIBUTOR | null | - [x] closes #12386
- [x] tests passed
- [x] passes `git diff upstream/master | flake8 --diff`
Want to remove pandas.util.testing.choice. First eliminating all
references to it. Doing this in more than one commit as there are a lot
of them.
#12386
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| null | 1 | 2016-02-29T01:04:27Z | 2016-03-01T02:56:33Z | 2016-03-01T02:56:15Z | CONTRIBUTOR | null | …sql.rst
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} | 12 | 2016-02-29T03:01:48Z | 2016-03-07T00:48:31Z | 2016-03-06T14:57:59Z | CONTRIBUTOR | null | - Closes #10789
- Passes Travis CI, nosetests, and flake8
- Covered by new tests in test_window.py:TestMoments
- Documented in computation.rst and v0.18.0.txt
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} | 4 | 2016-02-29T09:13:19Z | 2016-04-13T01:30:28Z | 2016-04-13T01:30:28Z | NONE | null | xref #9755
If the file is empty, it is logical to compensate with extra columns.
#### Code Sample, a copy-pastable example if possible
``` python
"""Example of Pandas bug."""
from pandas import read_csv
try:
from StringIO import StringIO
except ImportError:
from io import StringIO
name_list = ['Dummy', 'X', 'Dummy_2']
s = StringIO(',,')
df = read_csv(s, names=name_list, usecols=name_list[1:2], header=None)
print(list(df))
s = StringIO('')
df = read_csv(s, names=name_list, usecols=name_list[1:2], header=None)
print(list(df))
```
#### Expected Output
Current output:
```
['X']
['Dummy', 'X', 'Dummy_2']
```
Expected output:
```
['X']
['X']
```
#### output of `pd.show_versions()`
## 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: 0.17.1
nose: None
pip: 7.1.2
setuptools: 18.3.2
Cython: None
numpy: 1.10.1
scipy: 0.16.1
statsmodels: None
IPython: 4.0.1
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.0
openpyxl: 2.3.2
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: 2.8
None
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} | 5 | 2016-02-29T09:19:07Z | 2016-03-08T19:18:20Z | 2016-03-03T22:08:57Z | NONE | null | #### Code Sample, a copy-pastable example if possible
``` python
"""Example of Pandas bug."""
from pandas import read_csv
try:
from StringIO import StringIO
except ImportError:
from io import StringIO
s = StringIO(',,')
df = read_csv(s)
print(list(df))
s = StringIO(',,,')
df = read_csv(s)
print(list(df))
```
#### Expected Output
Current output:
```
['Unnamed: 0', 'Unnamed: 1', 'Unnamed: 2']
Traceback (most recent call last):
File "pandas_bug4.py", line 17, in <module>
df = read_csv(s)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/io/parsers.py", line 498, in parser_f
return _read(filepath_or_buffer, kwds)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/io/parsers.py", line 275, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/io/parsers.py", line 590, in __init__
self._make_engine(self.engine)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/io/parsers.py", line 731, in _make_engine
self._engine = CParserWrapper(self.f, **self.options)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/io/parsers.py", line 1103, in __init__
self._reader = _parser.TextReader(src, **kwds)
File "pandas/parser.pyx", line 515, in pandas.parser.TextReader.__cinit__ (pandas/parser.c:4948)
File "pandas/parser.pyx", line 632, in pandas.parser.TextReader._get_header (pandas/parser.c:6493)
File "pandas/parser.pyx", line 829, in pandas.parser.TextReader._tokenize_rows (pandas/parser.c:8838)
File "pandas/parser.pyx", line 1833, in pandas.parser.raise_parser_error (pandas/parser.c:22649)
pandas.parser.CParserError: Error tokenizing data. C error: Buffer overflow caught - possible malformed input file.
```
Expected output:
```
['Unnamed: 0', 'Unnamed: 1', 'Unnamed: 2']
['Unnamed: 0', 'Unnamed: 1', 'Unnamed: 2', 'Unnamed: 3']
```
#### output of `pd.show_versions()`
## 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: 0.17.1
nose: None
pip: 7.1.2
setuptools: 18.3.2
Cython: None
numpy: 1.10.1
scipy: 0.16.1
statsmodels: None
IPython: 4.0.1
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.0
openpyxl: 2.3.2
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: 2.8
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https://api.github.com/repos/pandas-dev/pandas/issues/12495 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12495/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12495/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12495/events | https://github.com/pandas-dev/pandas/issues/12495 | 137,352,728 | MDU6SXNzdWUxMzczNTI3Mjg= | 12,495 | transform() drops groupby columns if not in index or if not numeric | {
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| null | 5 | 2016-02-29T19:49:32Z | 2018-08-12T19:21:43Z | 2016-03-03T02:08:25Z | NONE | null | unlike when I use .aggregate('mean'), I lose the groupedby columns and any nonnumeric columns when using transform
``` python
>>> d = {'a':['a', 'a', 'c', 'c'], 'b':['z','z','z','y'], 'dat':[4.2, 2.4, 3.5, 5.3], 'dat2':[1.3,2.6,3.4,4.1]}
>>> df = pd.DataFrame(d)
>>> zscore = lambda x: (x - x.mean()) / x.std()
>>> df.groupby('a').transform(zscore)
dat dat2
0 0.707107 -0.707107
1 -0.707107 0.707107
2 -0.707107 -0.707107
3 0.707107 0.707107
```
If I add 'a' to the index, and then transform, I still lose column b
``` python
>>> df = df.set_index('a')
>>> df.groupby(level='a').transform(zscore)
dat dat2
a
a 0.707107 -0.707107
a -0.707107 0.707107
c -0.707107 -0.707107
c 0.707107 0.707107
```
The only way to keep non-numeric information columns is to add them all to the index, regardless of whether I need to group on them or not
``` python
>>> df = df.set_index(['a', 'b'])
>>> df.groupby(level='a').transform(zscore)
dat dat2
a b
c z 0.993399 -0.475293
z 0.198680 -0.095059
z 0.198680 1.425880
y -1.390759 -0.855528
```
#### Expected Output
I would expect the columns that I grouped on to remain, like they do when using aggregate('mean') for example.
``` python
a b dat dat2
0 a z 0.707107 -0.707107
1 a z -0.707107 0.707107
2 c z -0.707107 -0.707107
3 c y 0.707107 0.707107
```
#### 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 70 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.1
nose: 1.3.7
pip: 8.0.2
setuptools: 18.5
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.0
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: 0.7.1.None
psycopg2: None
Jinja2: None
```
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} | 1 | 2016-02-29T19:50:12Z | 2016-03-01T12:44:51Z | 2016-03-01T12:44:42Z | CONTRIBUTOR | null | A fix for a small problem with the docs for extract.
Also the first code block on http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180-enhancements-extract should show a warning but it does not. any ideas how to fix that?
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} | 2 | 2016-02-29T20:37:35Z | 2017-02-06T00:10:45Z | 2016-03-01T12:14:11Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import numpy as np
import pandas as pd
# Generate some random dataframes with a common "id" column to merge on
# Transform some columns into type "category"
some_strings = ['aa', 'bb', 'cc', 'dd']
d1 = {'id': np.arange(100),
'var1': np.random.randint(0,10,size=100),
'var2': np.random.uniform(size=100),
'var3': np.random.randint(0,2,size=100),
'var4': np.random.choice(some_strings, 100)}
df1 = pd.DataFrame(d1)
df1['var1'] = df1['var1'].astype('category')
d2 = {'id': np.arange(100),
'var1': np.random.randint(0,10,size=100),
'var2': np.random.uniform(size=100),
'var3': np.random.randint(0,2,size=100),
'var4': np.random.choice(some_strings, 100)}
df2 = pd.DataFrame(d2)
df2['var1'] = df2['var1'].astype('category')
df2['var4'] = df2['var4'].astype('category')
print 'df1'
print df1.info() #Shows categorical columns
print 'df2'
print df2.info() #Shows categorical columns
print pd.merge(df1, df2, on='id').info() #Categorical columns are no longer categorical
```
#### Expected Output
The merged df1 and df2 no longer has category columns, and are reset to their original types.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.23.3.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 18.5
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.0
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
Jinja2: None
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} | 15 | 2016-03-01T02:11:13Z | 2016-03-06T15:43:07Z | 2016-03-06T15:28:43Z | MEMBER | null | closes #12344
in which assignment to columns in `DataFrame` with duplicate column names caused all columns with the same name to be reassigned. The bug was located <a href="https://github.com/pydata/pandas/blob/master/pandas/core/indexing.py#L545">here</a>.
When you try to index into the DataFrame using `.iloc`, `pandas` will find the corresponding column name (or key) first before setting that key with the given value. Unfortunately, since all of your columns have the same key name, `pandas` ends up choosing all of the columns corresponding to that name.
This PR introduces a `_set_item_by_index` method for `DataFrame` objects that allows you to bypass that issue by using the indices of the columns to set the columns themselves whenever there are duplicates involved.
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} | 3 | 2016-03-01T03:46:44Z | 2021-04-23T01:12:35Z | null | NONE | null | ```
import datetime
import pandas as pd
import pytz
data = [{'one': 0, 'two': datetime.datetime(2016, 3, 1, 3, 13, 22, 98986, tzinfo=pytz.timezone('UTC'))}]
df = pd.DataFrame(data)
df.loc[1] = [np.nan, np.datetime64('NaT')]
```
traceback
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-14-7cc4bcff4319> in <module>()
4 data = [{'one': 0, 'two': datetime.datetime(2016, 3, 1, 3, 13, 22, 98986, tzinfo=pytz.timezone('UTC'))}]
5 df = pd.DataFrame(data)
----> 6 df.loc[1] = [np.nan, np.datetime64('NaT')]
/home/jstrong/src/envs/vc4/local/lib/python2.7/site-packages/pandas/core/indexing.pyc in __setitem__(self, key, value)
115 def __setitem__(self, key, value):
116 indexer = self._get_setitem_indexer(key)
--> 117 self._setitem_with_indexer(indexer, value)
118
119 def _has_valid_type(self, k, axis):
/home/jstrong/src/envs/vc4/local/lib/python2.7/site-packages/pandas/core/indexing.pyc in _setitem_with_indexer(self, indexer, value)
337 value = Series(value,index=self.obj.columns,name=indexer)
338
--> 339 self.obj._data = self.obj.append(value)._data
340 self.obj._maybe_update_cacher(clear=True)
341 return self.obj
/home/jstrong/src/envs/vc4/local/lib/python2.7/site-packages/pandas/core/frame.pyc in append(self, other, ignore_index, verify_integrity)
4229 to_concat = [self, other]
4230 return concat(to_concat, ignore_index=ignore_index,
-> 4231 verify_integrity=verify_integrity)
4232
4233 def join(self, other, on=None, how='left', lsuffix='', rsuffix='',
/home/jstrong/src/envs/vc4/local/lib/python2.7/site-packages/pandas/tools/merge.pyc in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)
811 verify_integrity=verify_integrity,
812 copy=copy)
--> 813 return op.get_result()
814
815
/home/jstrong/src/envs/vc4/local/lib/python2.7/site-packages/pandas/tools/merge.pyc in get_result(self)
993
994 new_data = concatenate_block_managers(
--> 995 mgrs_indexers, self.new_axes, concat_axis=self.axis, copy=self.copy)
996 if not self.copy:
997 new_data._consolidate_inplace()
/home/jstrong/src/envs/vc4/local/lib/python2.7/site-packages/pandas/core/internals.pyc in concatenate_block_managers(mgrs_indexers, axes, concat_axis, copy)
4454 copy=copy),
4455 placement=placement)
-> 4456 for placement, join_units in concat_plan]
4457
4458 return BlockManager(blocks, axes)
/home/jstrong/src/envs/vc4/local/lib/python2.7/site-packages/pandas/core/internals.pyc in concatenate_join_units(join_units, concat_axis, copy)
4551 to_concat = [ju.get_reindexed_values(empty_dtype=empty_dtype,
4552 upcasted_na=upcasted_na)
-> 4553 for ju in join_units]
4554
4555 if len(to_concat) == 1:
/home/jstrong/src/envs/vc4/local/lib/python2.7/site-packages/pandas/core/internals.pyc in get_reindexed_values(self, empty_dtype, upcasted_na)
4799
4800 if self.is_null and not getattr(self.block,'is_categorical',None):
-> 4801 missing_arr = np.empty(self.shape, dtype=empty_dtype)
4802 if np.prod(self.shape):
4803 # NumPy 1.6 workaround: this statement gets strange if all
TypeError: data type not understood
```
Same if you `loc[1] = [np.nan, np.nan]`. Seems the problem is loc assignment involving NaN for a datetime index column. Not sure the full breadth of the bug.
#### Expected Output
```
one two
0 0 2016-03-01 03:13:22.098986+00:00
1 1 NaT
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.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: 0.17.1
nose: 1.3.7
pip: 8.0.2
setuptools: 0.6
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
IPython: 4.0.1
sphinx: None
patsy: 0.4.1
dateutil: 2.5.0
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.0+1301.g7b517da
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.4.1
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
Jinja2: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12500 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12500/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12500/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12500/events | https://github.com/pandas-dev/pandas/issues/12500 | 137,461,456 | MDU6SXNzdWUxMzc0NjE0NTY= | 12,500 | Serialize numpy scalars in pd.msgpack | {
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} | 8 | 2016-03-01T03:59:39Z | 2019-12-11T13:30:14Z | 2019-12-11T13:30:13Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
``` python
import numpy as np
import pandas as pd
pd.msgpack.dumps(np.int32(0))
```
Currently procduces:
``` python
TypeError: can't serialize 0
```
#### Expected Output
```
b'\x00'
```
#### output of `pd.show_versions()`
``` python
In [5]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.4.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-59-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.0rc1+752.gfe201a2
nose: 1.3.7
pip: 8.0.3
setuptools: 20.1.1
Cython: 0.22.1
numpy: 1.10.4
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.4
blosc: 1.2.8
bottleneck: 1.0.0
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.3
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
```
Related to https://github.com/msgpack/msgpack-python/issues/61
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} | 6 | 2016-03-01T10:29:29Z | 2016-03-10T10:34:53Z | 2016-03-01T12:29:48Z | NONE | null | I don't know if this is a bug or not (I guess not), but I think this behavior might be very confusing to many users who like me until now might not be aware of this.
When converting a string column to Date using pd.to_datetime or pd.DatetimeIndex the way the date is interpreted might change from row to row. I experienced this today where March 1st was interpreted as January 3rd. Probably pandas does this simply on a per row set considering only one sample at a time. One way to improve this would be to first go through the whole
At least I'd suggest to implement a warning if none of the format/dayfirst parameters are used.
Here's a screenshot to clarify:
http://snag.gy/mXT7g.jpg
#### output of `pd.show_versions()`
## 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: de_DE.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 8.0.2
setuptools: 19.6.2
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
IPython: 4.1.1
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: None
Jinja2: 2.8
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https://api.github.com/repos/pandas-dev/pandas/issues/12502 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12502/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12502/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12502/events | https://github.com/pandas-dev/pandas/issues/12502 | 137,559,417 | MDU6SXNzdWUxMzc1NTk0MTc= | 12,502 | Setting values in DataFrame with timezone-aware index fails using .loc | {
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| null | 1 | 2016-03-01T12:22:49Z | 2016-03-01T12:31:43Z | 2016-03-01T12:31:27Z | CONTRIBUTOR | null | In Pandas 0.17.1, setting values in a DataFrame with .loc does not seem the work well in case of timezone-aware indices in combination with a list of columns:
```
In [1]: import pandas as pd
...: start = pd.Timestamp('2015-7-12', tz='utc')
...: end = pd.Timestamp('2015-7-12 12:00', tz='utc')
...: timestamps = pd.date_range(start, end, freq='H')
...: df1 = pd.DataFrame(index=timestamps, columns=['var'])
...: df2 = pd.DataFrame(1.2, index=timestamps, columns=['var'])
...: df1.loc[:,['var']] = df2
...: df1
Out[1]:
var
2015-07-12 00:00:00+00:00 NaN
2015-07-12 01:00:00+00:00 NaN
2015-07-12 02:00:00+00:00 NaN
2015-07-12 03:00:00+00:00 NaN
2015-07-12 04:00:00+00:00 NaN
2015-07-12 05:00:00+00:00 NaN
2015-07-12 06:00:00+00:00 NaN
2015-07-12 07:00:00+00:00 NaN
2015-07-12 08:00:00+00:00 NaN
2015-07-12 09:00:00+00:00 NaN
2015-07-12 10:00:00+00:00 NaN
2015-07-12 11:00:00+00:00 NaN
2015-07-12 12:00:00+00:00 NaN
```
Though it does work when replacing `['var']` by `'var'`:
```
In [2]: start = pd.Timestamp('2015-7-12', tz='utc')
...: end = pd.Timestamp('2015-7-12 12:00', tz='utc')
...: timestamps = pd.date_range(start, end, freq='H')
...: df1 = pd.DataFrame(index=timestamps, columns=['var'])
...: df2 = pd.DataFrame(1.2, index=timestamps, columns=['var'])
...: df1.loc[:,'var'] = df2
...: df1
Out[2]:
var
2015-07-12 00:00:00+00:00 1.2
2015-07-12 01:00:00+00:00 1.2
2015-07-12 02:00:00+00:00 1.2
2015-07-12 03:00:00+00:00 1.2
2015-07-12 04:00:00+00:00 1.2
2015-07-12 05:00:00+00:00 1.2
2015-07-12 06:00:00+00:00 1.2
2015-07-12 07:00:00+00:00 1.2
2015-07-12 08:00:00+00:00 1.2
2015-07-12 09:00:00+00:00 1.2
2015-07-12 10:00:00+00:00 1.2
2015-07-12 11:00:00+00:00 1.2
2015-07-12 12:00:00+00:00 1.2
```
Also, it works with a non-timezone-aware index:
```
In [3]: start = pd.Timestamp('2015-7-12')
...: end = pd.Timestamp('2015-7-12 12:00')
...: timestamps = pd.date_range(start, end, freq='H')
...: df1 = pd.DataFrame(index=timestamps, columns=['var'])
...: df2 = pd.DataFrame(1.2, index=timestamps, columns=['var'])
...: df1.loc[:,['var']] = df2
...: df1
Out[3]:
var
2015-07-12 00:00:00 1.2
2015-07-12 01:00:00 1.2
2015-07-12 02:00:00 1.2
2015-07-12 03:00:00 1.2
2015-07-12 04:00:00 1.2
2015-07-12 05:00:00 1.2
2015-07-12 06:00:00 1.2
2015-07-12 07:00:00 1.2
2015-07-12 08:00:00 1.2
2015-07-12 09:00:00 1.2
2015-07-12 10:00:00 1.2
2015-07-12 11:00:00 1.2
2015-07-12 12:00:00 1.2
```
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} | 3 | 2016-03-01T13:31:42Z | 2016-08-11T15:54:05Z | 2016-07-13T11:52:44Z | CONTRIBUTOR | null | some ideas on reorging.
- [X] rename `pandas/util/misc.py` to `pandas/util/tools.py` or `pandas/util/util.py` (https://github.com/pydata/pandas/commit/a69d628db77cc67f79590bbb3f13e178cb8b0e93)
- [X] move `core/generic.py/_validate_kwargs` (and new `validate_args`) here (https://github.com/pydata/pandas/commit/6e6478764a8e002e2436da3ee981512917d9b7c4)
- [x] move the `various python utilities` section from `core/common` (#13147)
- [x] create `pandas/types/` (#12804)
- [x] move `pandas/core/dtypes.py` here
- [x] create `pandas/types/types.py` (#12804)
- [x] move `bind_method`, `create_pandas_abc_types` here (from `core/common.py`)
- [x] move the `is_...._dtype` to here (#13147)
- [x] move `core/common.py/concat*` to new sub-package `types/concat` (#12844)
- [x] move `core/common.py/take*` stuff to `algorithms` (may need to create a sub-package for this) (#12804)
- [x] create new sub-package `pandas/formats` (#12804)
- [x] move `core/common.py/printing utilities` to new sub-package `pandas/formats/printing.py` (#12804)
- [x] move `core/format.py` here as well (maybe split later) (#12804)
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