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| 15 | 2016-12-06T11:42:59Z | 2016-12-24T10:50:47Z | 2016-12-24T10:50:47Z |
CONTRIBUTOR
| null |
- [ ] 3.4 slow https://travis-ci.org/pandas-dev/pandas/jobs/181534281
```
======================================================================
FAIL: test_bar_log_no_subplots (pandas.tests.plotting.test_frame.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/pandas-dev/pandas/pandas/tests/plotting/test_frame.py", line 1130, in test_bar_log_no_subplots
tm.assert_numpy_array_equal(ax.yaxis.get_ticklocs(), expected)
File "/home/travis/build/pandas-dev/pandas/pandas/util/testing.py", line 1079, in assert_numpy_array_equal
_raise(left, right, err_msg)
File "/home/travis/build/pandas-dev/pandas/pandas/util/testing.py", line 1062, in _raise
.format(obj), left.shape, right.shape)
File "/home/travis/build/pandas-dev/pandas/pandas/util/testing.py", line 1018, in raise_assert_detail
raise AssertionError(msg)
AssertionError: numpy array are different
numpy array shapes are different
[left]: (5,)
[right]: (4,)
======================================================================
FAIL: test_bar_log_subplots (pandas.tests.plotting.test_frame.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/pandas-dev/pandas/pandas/tests/plotting/test_frame.py", line 1141, in test_bar_log_subplots
tm.assert_numpy_array_equal(ax[0].yaxis.get_ticklocs(), expected)
File "/home/travis/build/pandas-dev/pandas/pandas/util/testing.py", line 1079, in assert_numpy_array_equal
_raise(left, right, err_msg)
File "/home/travis/build/pandas-dev/pandas/pandas/util/testing.py", line 1062, in _raise
.format(obj), left.shape, right.shape)
File "/home/travis/build/pandas-dev/pandas/pandas/util/testing.py", line 1018, in raise_assert_detail
raise AssertionError(msg)
AssertionError: numpy array are different
numpy array shapes are different
[left]: (7,)
[right]: (6,)
======================================================================
FAIL: test_bar_log (pandas.tests.plotting.test_series.TestSeriesPlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/pandas-dev/pandas/pandas/tests/plotting/test_series.py", line 207, in test_bar_log
tm.assert_numpy_array_equal(ax.yaxis.get_ticklocs(), expected)
File "/home/travis/build/pandas-dev/pandas/pandas/util/testing.py", line 1079, in assert_numpy_array_equal
_raise(left, right, err_msg)
File "/home/travis/build/pandas-dev/pandas/pandas/util/testing.py", line 1062, in _raise
.format(obj), left.shape, right.shape)
File "/home/travis/build/pandas-dev/pandas/pandas/util/testing.py", line 1018, in raise_assert_detail
raise AssertionError(msg)
AssertionError: numpy array are different
numpy array shapes are different
[left]: (7,)
[right]: (6,)
----------------------------------------------------------------------
Ran 252 tests in 1103.335s
```
- [x] numpy-dev build: https://travis-ci.org/pandas-dev/pandas/jobs/181534284, closed by #14809
```
======================================================================
ERROR: test_scalar_na_cmp_corners (pandas.tests.series.test_operators.TestSeriesOperators)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/pandas-dev/pandas/pandas/core/ops.py", line 1170, in na_op
raise_on_error=True, **eval_kwargs)
File "/home/travis/build/pandas-dev/pandas/pandas/computation/expressions.py", line 210, in evaluate
**eval_kwargs)
File "/home/travis/build/pandas-dev/pandas/pandas/computation/expressions.py", line 63, in _evaluate_standard
return op(a, b)
TypeError: unsupported operand type(s) for &: 'float' and 'float'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/travis/build/pandas-dev/pandas/pandas/core/internals.py", line 1160, in eval
result = get_result(other)
File "/home/travis/build/pandas-dev/pandas/pandas/core/internals.py", line 1129, in get_result
result = func(values, other)
File "/home/travis/build/pandas-dev/pandas/pandas/core/ops.py", line 1179, in na_op
yrav = yrav[mask]
IndexError: boolean index did not match indexed array along dimension 0; dimension is 1 but corresponding boolean dimension is 9
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/travis/build/pandas-dev/pandas/pandas/tests/series/test_operators.py", line 1417, in test_scalar_na_cmp_corners
self.assertRaises(ValueError, lambda: d.__and__(s, axis='columns'))
File "/home/travis/build/pandas-dev/pandas/pandas/util/testing.py", line 2380, in assertRaises
_callable(*args, **kwargs)
File "/home/travis/build/pandas-dev/pandas/pandas/tests/series/test_operators.py", line 1417, in <lambda>
self.assertRaises(ValueError, lambda: d.__and__(s, axis='columns'))
File "/home/travis/build/pandas-dev/pandas/pandas/core/ops.py", line 1223, in f
return self._combine_series(other, na_op, fill_value, axis, level)
File "/home/travis/build/pandas-dev/pandas/pandas/core/frame.py", line 3539, in _combine_series
fill_value=fill_value)
File "/home/travis/build/pandas-dev/pandas/pandas/core/frame.py", line 3573, in _combine_match_columns
axes=[left.columns, self.index])
File "/home/travis/build/pandas-dev/pandas/pandas/core/internals.py", line 3163, in eval
return self.apply('eval', **kwargs)
File "/home/travis/build/pandas-dev/pandas/pandas/core/internals.py", line 3057, in apply
applied = getattr(b, f)(**kwargs)
File "/home/travis/build/pandas-dev/pandas/pandas/core/internals.py", line 1167, in eval
result = handle_error()
File "/home/travis/build/pandas-dev/pandas/pandas/core/internals.py", line 1150, in handle_error
(repr(other), str(detail))) # noqa
TypeError: Could not operate array([nan], dtype=object) with block values boolean index did not match indexed array along dimension 0; dimension is 1 but corresponding boolean dimension is 9
----------------------------------------------------------------------
```
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MDExOlB1bGxSZXF1ZXN0OTY3MTA5MTY=
| 14,809 |
COMPAT: numpy compat with 1-ndim object array compat and broadcasting
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| 0 | 2016-12-06T11:58:47Z | 2016-12-06T13:00:30Z | 2016-12-06T13:00:30Z |
CONTRIBUTOR
| null |
xref #14808
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groupby function: different printed result after irrelevant change
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| 11 | 2016-12-06T17:55:25Z | 2016-12-14T11:37:40Z | 2016-12-06T22:41:08Z |
NONE
| null |
(1)
```python
def groupby_func(x):
#computing speedup, relative to first line
return x.iloc[0, :] / x.iloc[0: , :] # (with '0')
sdf_size = odf.loc[:, cols].groupby(by="size").apply(groupby_func)
```
(2)
```python
def groupby_func(x):
#computing speedup, relative to first line
return x.iloc[0, :] / x.iloc[: , :] # (without '0')
sdf_size = odf.loc[:, cols].groupby(by="size").apply(groupby_func)
```
#### Problem description
For the version (1) and (2) I get two different outputs. When I print 'sdf_size' on the console:
(1)
(2)
Somehow, **with** '0:' the (printed) result is how I wanted it to be (see screenshots, grouping of size in index). But after deleting the 0, which I expected to be unnecessary, I got a different result, which I didn't expect to be different (I think '0:' and ':' to be same -- correct me if I am wrong on this). An explicit setting of "group_keys=True" didn't change anything.
Just ask if something is unclear.
Thank you.
#### Output of ``pd.show_versions()``
<details>
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-51-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.7
pip: 9.0.1
setuptools: 0.6
Cython: None
numpy: 1.11.2
scipy: 0.18.0
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: 0.9.1
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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| 193,881,415 |
MDU6SXNzdWUxOTM4ODE0MTU=
| 14,811 |
Calling shift(n != 0) on an empty DatetimeIndex raises an IndexError
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| 2 | 2016-12-06T20:54:53Z | 2020-09-30T02:12:04Z | 2020-09-30T02:12:04Z |
NONE
| null |
Calling `shift(0)` on an empty DatetimeIndex seems to return an empty index, but calling `shift(n)` with a non-zero `n` raises an IndexError. I would expect this to return an empty index.
```python
# this works
pd.date_range(start='20161021', end='20161021', freq='BM').shift(0)
# this raises
pd.date_range(start='20161021', end='20161021', freq='BM').shift(1)
# IndexError: index 0 is out of bounds for axis 0 with size 0
pd.__version__
# u'0.18.1'
```
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Apply min_itemsize to index even when not appending
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| 5 | 2016-12-06T22:07:52Z | 2016-12-11T21:54:59Z | 2016-12-11T21:54:59Z |
MEMBER
| null |
- [x] closes #10381
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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MDU6SXNzdWUxOTM5MTkyNzc=
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TST: reorg groupby tests
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| 3 | 2016-12-06T23:48:39Z | 2016-12-17T15:43:50Z | 2016-12-17T15:43:50Z |
CONTRIBUTOR
| null |
see suggestions here: https://github.com/pandas-dev/pandas/pull/12607#issuecomment-265310458
we have lots and lots of groupby tests and so getting pretty unweidly
so should reorg to a sub-dir setup like we have for other things
eg.
pandas/tests/groupby/.....
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MAINT: Style check *.c and *.h files
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| 8 | 2016-12-07T05:33:24Z | 2016-12-19T16:12:25Z | 2016-12-15T11:23:52Z |
MEMBER
| null |
`cpplint` was introduced #14740, and this commit extends to check other `*.c` and `*.h` files.
Currently, they all reside in `pandas/src`, and this commit expands the lint to check all of the following:
1) `datetime` (dir)
2) `ujson` (dir)
3) `period_helper.c`
4) `All header files`
The parser directory was handled in #14740, and the others have been deliberately omitted per the discussion <a href="https://github.com/pandas-dev/pandas/pull/14740#issuecomment-265260209">here</a>.
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BUG: Fix linux clipboard QApplication() creation
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| 4 | 2016-12-07T06:24:15Z | 2017-03-20T13:47:15Z | 2017-03-20T13:47:14Z |
CONTRIBUTOR
| null |
- [x] closes #14372
A Qt application cannot instantiate multiple `QApplication` instances,
so we create a new `QApplication` only when the global
`QApplication.instance()` is None.
Failing sample (gtk should not be installed for pandas to use qt clipboard):
```
from PyQt4.QtGui import QApplication
myapp = QApplication([])
from pandas.util.clipboard import clipboard_get # <--- ERROR
File "prefix/lib/python2.7/site-packages/pandas/util/clipboard.py", line 164, in <module>
app = qt4.QtGui.QApplication([])
RuntimeError: A QApplication instance already exists.
```
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| 194,086,115 |
MDExOlB1bGxSZXF1ZXN0OTY5NDk1ODI=
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BUG: Prevent addition overflow with TimedeltaIndex
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| 20 | 2016-12-07T15:50:04Z | 2016-12-24T02:15:57Z | 2016-12-17T23:25:08Z |
MEMBER
| null |
Expands checked-add array addition introduced in #14237 to include all other addition cases (i.e.
`TimedeltaIndex` and `Timedelta`). Follow-up to #14453.
|
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| 194,131,968 |
MDExOlB1bGxSZXF1ZXN0OTY5ODIyOTE=
| 14,817 |
Groupby tests restructure
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| 13 | 2016-12-07T18:40:10Z | 2016-12-17T15:47:12Z | 2016-12-17T15:43:50Z |
CONTRIBUTOR
| null |
closes #14813
|
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MDExOlB1bGxSZXF1ZXN0OTcwMDI1MzM=
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Changed pandas-qt python2/3 friendly qtpandas.
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| 8 | 2016-12-07T20:38:44Z | 2016-12-29T22:27:38Z | 2016-12-29T14:56:29Z |
CONTRIBUTOR
| null |
Just changed the link to the abandoned repository python 2 only pandas-qt to the new functional Python 2/3 friendly qtpandas.
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| 194,183,748 |
MDU6SXNzdWUxOTQxODM3NDg=
| 14,819 |
Feature Request: symbol datatype (for string compression)
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| 3 | 2016-12-07T22:02:14Z | 2016-12-15T04:14:03Z | 2016-12-07T22:30:38Z |
NONE
| null |
Should pandas have a _Symbol_ datatype for strings which are frequently re-used?
For example, consider a table of stock trades with columns ['Timestamp','Price','Size','StockSymbol']. Suppose there are 10 million trades, but only 500 different stock symbols. Instead of storing 10 million strings, we could fill the 'StockSymbol' column with integers 0-499 where 0 represents AAPL, 1 represents AMZN, etc.
The _Symbol_ type in Ruby, Scala, and Q does this compression/expansion automatically. It's a big memory saver in big tables, and _Symbol_ comparison can be much faster than string comparison.
I often use `.astype('category')` for string compression in pandas. But at a talk I saw recently, @wesm suggested this might not be a great idea. I've definitely caused several confusing problems by abusing categoricals this way: comparing two columns with different levels, appending rows with new symbols, wrongly assuming `sort_values()` will sort symbols lexicographically, etc.
Related question: Without a _Symbol_ datatype, is there a consensus on best practice for storing repetitive strings in a Series or DataFrame? Should we leave them as `Object` type? Compress them as un-ordered categoricals? Do something else I haven't thought of?
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Fixed KDE Plot to drop the missing values
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| 10 | 2016-12-08T01:38:48Z | 2016-12-24T02:07:26Z | 2016-12-15T15:40:38Z |
CONTRIBUTOR
| null |
- [x] closes https://github.com/pandas-dev/pandas/issues/14821
- [x] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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KDE Plot does not drop missing values
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| 0 | 2016-12-08T01:40:01Z | 2016-12-24T02:07:34Z | 2016-12-15T15:40:38Z |
CONTRIBUTOR
| null |
```python
df = pd.Series([np.nan, 4.0,5.0,6.0,np.nan,1.0,4.0,4.0,2.0,np.nan,np.nan,6.0,2.0,np.nan,np.nan,np.nan,np.nan,np.nan,
3.0,2.0,1.0,10.0,np.nan,np.nan,np.nan,np.nan,1.0,np.nan,np.nan,3.0])
df.plot(kind='kde')
df.plot(kind='hist')
```
The KDE Plot of a series with missing values fails, producing an empty plot whereas the histogram is able to drop the missing values.
#### Expected Output
* The KDE Plot is generated when the missing values are removed manually or by using `dropna()`
* According to the [Pandas doc on plotting with missing data](http://pandas.pydata.org/pandas-docs/version/0.18.1/visualization.html#plotting-with-missing-data), the default way NaNs are handled is by dropping them. But this does not happen - an empty plot is generated
* I have created a PR to fix this: [#14820](https://github.com/pandas-dev/pandas/pull/14820)
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-92-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.1
nose: 1.3.7
pip: 9.0.1
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
</details>
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PERF: PeriodIndex.size
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| 17 | 2016-12-08T04:03:54Z | 2016-12-21T11:11:36Z | 2016-12-21T11:11:36Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [1]: import pandas as pd
In [2]: index = pd.PeriodIndex(start='1952', periods=50000, freq='B')
In [3]: %timeit index.size
10 loops, best of 3: 102 ms per loop
In [4]: %timeit index._values.size
The slowest run took 24.79 times longer than the fastest. This could mean that an intermediate result is being cached.
1000000 loops, best of 3: 233 ns per loop
```
#### Problem description
@sinhrks - now that the `PeriodIndex` call to `.values` unboxes all the periods, operations like `PeriodIndex.size` are much slower.
What's the best way around this? Should we override more methods so that they call into `._values` rather than `.values`?
#### Output of ``pd.show_versions()``
<details>
In [6]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.1
nose: 1.3.7
pip: 9.0.1
setuptools: 28.8.0
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.8.2
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: 1.2.0
tables: None
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: 1.1.2
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.5
sqlalchemy: 1.1.4
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.43.0
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0OTcwNjA1MTY=
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BUG: MutliIndex variable length tuples
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[] | null | 16 | 2016-12-08T05:15:34Z | 2017-09-07T00:42:45Z | 2017-09-07T00:42:45Z |
CONTRIBUTOR
| null |
- [ ] closes #14794
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
|
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frame_methods benchmarking sum instead of mean
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| 4 | 2016-12-08T05:49:12Z | 2016-12-10T21:31:15Z | 2016-12-10T21:31:12Z |
MEMBER
| null |
Not sure if the function name or the method call is incorrect, but it looks like this should be benchmarking `mean` instead of `sum`
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to_json() output format issue
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| 1 | 2016-12-08T07:22:40Z | 2016-12-08T15:01:33Z | 2016-12-08T15:01:08Z |
NONE
| null |
https://github.com/hupili/pandas-to-json-multi-index-issue/blob/master/pandas%20to_json%20test.ipynb
#### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame(
[
['1', '2', 'a'],
['1', '3', 'b'],
['2', '4', 'c'],
['3', '5', 'd']
],
columns=['key1', 'key2', 'key3']
)
j = df.set_index(['key1', 'key2']).to_json()
json.loads(j)
```
#### Problem description
The exported JSON is ill-formatted
```
---------------------------------------------------------------------------
JSONDecodeError Traceback (most recent call last)
<ipython-input-8-5f4db1ed154c> in <module>()
----> 1 json.loads(j)
/usr/local/Cellar/python3/3.5.1/Frameworks/Python.framework/Versions/3.5/lib/python3.5/json/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
317 parse_int is None and parse_float is None and
318 parse_constant is None and object_pairs_hook is None and not kw):
--> 319 return _default_decoder.decode(s)
320 if cls is None:
321 cls = JSONDecoder
/usr/local/Cellar/python3/3.5.1/Frameworks/Python.framework/Versions/3.5/lib/python3.5/json/decoder.py in decode(self, s, _w)
337
338 """
--> 339 obj, end = self.raw_decode(s, idx=_w(s, 0).end())
340 end = _w(s, end).end()
341 if end != len(s):
/usr/local/Cellar/python3/3.5.1/Frameworks/Python.framework/Versions/3.5/lib/python3.5/json/decoder.py in raw_decode(self, s, idx)
353 """
354 try:
--> 355 obj, end = self.scan_once(s, idx)
356 except StopIteration as err:
357 raise JSONDecodeError("Expecting value", s, err.value) from None
JSONDecodeError: Expecting ':' delimiter: line 1 column 13 (char 12)
```
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 16.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 7.1.2
setuptools: 28.6.1
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.4
sqlalchemy: 1.1.2
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.43.0
pandas_datareader: None
In [4]:
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MDU6SXNzdWUxOTQyOTAwNTk=
| 14,826 |
Inconsistent behavior of DatetimeIndex Partial String Indexing on Series and DataFrames
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| 18 | 2016-12-08T09:48:33Z | 2016-12-22T20:27:58Z | 2016-12-20T17:44:52Z |
CONTRIBUTOR
| null |
This bugreport is related to [this](http://stackoverflow.com/questions/40999840/why-is-pandas-not-returing-a-scalar-string-instead-of-a-series-when-accessing-a) SO question and the discussion there.
#### Summary
I believe that current [DatetimeIndex Partial String Indexing](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#datetimeindex-partial-string-indexing) behavior is either inconsistent or underdocumented as the result depends nontrivially on whether we are working with `Series` or `DataFrame` and whether `DateTimeIndex` is periodic or not.
#### `Series` vs. `DataFrame`
```python
series = pd.Series([1, 2, 3], pd.DatetimeIndex(['2016-12-07 09:00:00',
'2016-12-08 09:00:00',
'2016-12-09 09:00:00']))
print(type(series["2016-12-07 09:00:00"]))
# <class 'numpy.int64'>
```
```python
df = pd.DataFrame(series)
df["2016-12-07 09:00:00"]
KeyError: '2016-12-07 09:00:00'
```
Here we see that the behaviour depends on what we are indexing: `Series` returns scalar while `DataFrame` raises an exception. This exception is consistent with the documentation notice:
> **Warning** The following selection will raise a KeyError; otherwise this selection methodology would be inconsistent with other selection methods in pandas (as this is not a slice, nor does it resolve to one)
Why we do not get the same exception for `Series` object?
#### Periodic vs. Non-periodic
```python
series = pd.Series([1, 2, 3], pd.DatetimeIndex(['2016-12-07 09:00:00',
'2016-12-08 09:00:00',
'2016-12-09 09:00:01']))
# now it is not periodic due to 1 second in the last timestamp
print(type(series["2016-12-07 09:00:00"]))
# <class 'pandas.core.series.Series'>
```
In contrast with the previous example, we get an instance of `Series` here, so the same timestamp is considered as a slice, not index. Why it depends in such a way on periodicity of the index?
```python
df = pd.DataFrame(series)
print(type(df["2016-12-07 09:00:00"]))
# <class 'pandas.core.frame.DataFrame'>
```
No exceptions here, in contrast with periodic case.
Is it intended behavior? If yes, I believe that this should be clearly documented and rationale provided.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0+157.g2466ecb
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.1
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
</details>
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MDU6SXNzdWUxOTQzMjAxNDg=
| 14,827 |
Pandas.to_numeric raises ValueError when applied to python Decimal object
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| 1 | 2016-12-08T12:10:41Z | 2016-12-20T13:29:23Z | 2016-12-20T13:29:23Z |
NONE
| null |
When pandas tries to convert columns to numeric using to_numeric a value error is raised
Example
```python
import decimal
df = pd.DataFrame(dict(col=[decimal.Decimal(9.1)]))
df['col'] = df['col'].apply(pd.to_numeric)
```
however
```python
import decimal
df = pd.DataFrame(dict(col=[decimal.Decimal(9.1)]))
df['col'] = df['col'].astype(float)
```
works fine
#### Problem description
to_numeric should be able to convert decimal objects to float types
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-53-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_IE.UTF-8
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.7
pip: 9.0.1
setuptools: 28.3.0
Cython: 0.23.4
numpy: 1.11.2
scipy: 0.16.1
statsmodels: 0.6.1
xarray: None
IPython: None
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: 3.2.0
numexpr: 2.4.6
matplotlib: 1.5.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: None
</details>
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MDU6SXNzdWUxOTQzODM5NzI=
| 14,828 |
Pandas can not import CSV file
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[] | null | 2 | 2016-12-08T16:39:12Z | 2017-02-03T21:50:46Z | 2017-02-03T21:50:46Z |
NONE
| null |
#### Code Sample
```python
import pandas as pd
import numpy as np
reimbursements = pd.read_csv('../data/2016-11-19-reimbursements.xz',
dtype={'cnpj_cpf': np.str,
'reimbursement_numbers': np.str},
low_memory=False)
print(reimbursements.columns)
```
#### Problem description
I'm running Pandas and Jupyter on Windows 10 Enterprise, and I cant import my csv file. When I run pandas on PyCharm, on terminal or even in Jupyter, the code runs flawlessly until we load the dataset, but to make a request or operation on the dataframe, the output is the following:
```
---------------------------------------------------------------------------
CParserError Traceback (most recent call last)
<ipython-input-6-445ca5d3e350> in <module>()
4 dtype={'cnpj_cpf': np.str,
5 'reimbursement_numbers': np.str},
----> 6 low_memory=False)
7
8 reimbursements.columns
c:\users\vinicius\appdata\local\programs\python\python35-32\lib\site-packages\pandas\io\parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)
560 skip_blank_lines=skip_blank_lines)
561
--> 562 return _read(filepath_or_buffer, kwds)
563
564 parser_f.__name__ = name
c:\users\vinicius\appdata\local\programs\python\python35-32\lib\site-packages\pandas\io\parsers.py in _read(filepath_or_buffer, kwds)
323 return parser
324
--> 325 return parser.read()
326
327 _parser_defaults = {
c:\users\vinicius\appdata\local\programs\python\python35-32\lib\site-packages\pandas\io\parsers.py in read(self, nrows)
813 raise ValueError('skip_footer not supported for iteration')
814
--> 815 ret = self._engine.read(nrows)
816
817 if self.options.get('as_recarray'):
c:\users\vinicius\appdata\local\programs\python\python35-32\lib\site-packages\pandas\io\parsers.py in read(self, nrows)
1312 def read(self, nrows=None):
1313 try:
-> 1314 data = self._reader.read(nrows)
1315 except StopIteration:
1316 if self._first_chunk:
pandas\parser.pyx in pandas.parser.TextReader.read (pandas\parser.c:8643)()
pandas\parser.pyx in pandas.parser.TextReader._read_rows (pandas\parser.c:9772)()
pandas\parser.pyx in pandas.parser.raise_parser_error (pandas\parser.c:23295)()
CParserError: Error tokenizing data. C error: out of memory
```
#### Expected Output
```
Index(['year', 'applicant_id', 'document_id', 'reimbursement_value_total',
'total_net_value', 'reimbursement_numbers', 'congressperson_name',
'congressperson_id', 'congressperson_document', 'term', 'state',
'party', 'term_id', 'subquota_number', 'subquota_description',
'subquota_group_id', 'subquota_group_description', 'supplier',
'cnpj_cpf', 'document_number', 'document_type', 'issue_date',
'document_value', 'remark_value', 'net_values', 'month', 'installment',
'passenger', 'leg_of_the_trip', 'batch_number', 'reimbursement_values'],
dtype='object')
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 32
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 9.0.1
setuptools: 20.7.0
Cython: None
numpy: 1.11.1
scipy: None
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext)
jinja2: 2.8
boto: None
pandas_datareader: None
None
```
</details>
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MDU6SXNzdWUxOTQzODQ0MDA=
| 14,829 |
API: allow DataFrame.rename to take a list-like of colums
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| 16 | 2016-12-08T16:40:49Z | 2021-10-29T02:01:53Z | null |
CONTRIBUTOR
| null |
This is closely related to #12392, but I think separate issue. Proposal would be to be able to pass a list-like to `DataFrame.rename` to make method-chaining easier. I think it would also be consistent with #11980 (Series rename)
```python
df = pd.DataFrame({'a': [1,2], 'b': [3, 4]})
# make this
df = df.rename(columns=['j', 'k'])
# equivalent to
df.columns = ['j', 'k']
```
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| 194,392,805 |
MDU6SXNzdWUxOTQzOTI4MDU=
| 14,830 |
hardcoded expectations on endianness in the tests -- feature or a bug? ;)
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| 3 | 2016-12-08T17:13:04Z | 2016-12-09T19:34:32Z | 2016-12-09T19:34:32Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
looking at code of pandas/tests/series/test_datetime_values.py which was introduced in c76ca444fd42675982e81394736d941e2f2629db I believe:
```python
period_index = period_range('20150301', periods=5)
result = period_index.strftime("%Y/%m/%d")
expected = np.array(['2015/03/01', '2015/03/02', '2015/03/03',
'2015/03/04', '2015/03/05'], dtype='<U10')
self.assert_numpy_array_equal(result, expected)
```
#### Problem description
above (and some other tests... see e.g. https://buildd.debian.org/status/fetch.php?pkg=pandas&arch=s390x&ver=0.19.1-2&stamp=1480348101) lead to failed tests... which sometimes look as informative as
```
numpy array values are different (0.0 %)
[left]: [2015/03/01, 2015/03/02, 2015/03/03, 2015/03/04, 2015/03/05]
[right]: [2015/03/01, 2015/03/02, 2015/03/03, 2015/03/04, 2015/03/05]
```
;-)
but the real reason is different endianness:
```
(Pdb) l
325
326 period_index = period_range('20150301', periods=5)
327 result = period_index.strftime("%Y/%m/%d")
328 expected = np.array(['2015/03/01', '2015/03/02', '2015/03/03',
329 '2015/03/04', '2015/03/05'], dtype='<U10')
330 -> self.assert_numpy_array_equal(result, expected)
331
332 s = Series([datetime(2013, 1, 1, 2, 32, 59), datetime(2013, 1, 2, 14,
333 32, 1)])
334 result = s.dt.strftime('%Y-%m-%d %H:%M:%S')
335 expected = Series(["2013-01-01 02:32:59", "2013-01-02 14:32:01"])
(Pdb) p expected
array([u'2015/03/01', u'2015/03/02', u'2015/03/03', u'2015/03/04',
u'2015/03/05'],
dtype='<U10')
(Pdb) p result
array([u'2015/03/01', u'2015/03/02', u'2015/03/03', u'2015/03/04',
u'2015/03/05'],
dtype='>U10')
```
tentative *fix* is easy -- remove '>' from type definition. But I wondered if that is desired -- does pandas assume to enforce little-endianness or not? if it doesn't force -- then actually a proper fix, to maintain the same check of consistent endianness, would be to prefix with `=` and not `<`.
Please advise!
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MDExOlB1bGxSZXF1ZXN0OTcxNzAyMTY=
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BF: boost min cython to 0.23
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| 11 | 2016-12-08T17:56:35Z | 2016-12-12T11:48:25Z | 2016-12-12T11:48:25Z |
CONTRIBUTOR
| null |
closes #14699
closes #14508
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| 14,832 |
BF(TST): use = (native) instead of < (little endian) for target data types
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| 3 | 2016-12-08T17:59:51Z | 2017-01-12T14:44:57Z | 2016-12-09T19:34:32Z |
CONTRIBUTOR
| null |
- [ ] closes #14830
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
It is still WiP -- just thought to submit a PR to see how travis etc reacts on typical platforms
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Multiarray searchsorted fails
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| 12 | 2016-12-08T18:04:17Z | 2021-05-02T00:55:15Z | null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
pandas.MultiIndex([[0],["a"]], [[0],[0]]).searchsorted((1,"b"))
```
#### Problem description
The entry `(1,"b")` should come after the existing `(0,"a")` in the `MultiIndex`. (Alternatively, MultiIndex could throw a clean error message.) Instead, an intransparent exception is raised:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python3.5/site-packages/pandas/core/base.py", line 1156, in searchsorted
return self.values.searchsorted(key, side=side, sorter=sorter)
TypeError: unorderable types: tuple() > str()
```
This is because `Index.searchsorted` naïvely passes its arguments to `numpy.searchsorted`, which is unaware that its second argument is a sequence of tuples, not a plain array just of dimension one higher.
#### Expected Output
1
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
NSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.8.10-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.19.1
nose: 1.3.7
pip: 9.0.1
setuptools: 28.8.0
Cython: 0.25.1
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: 1.0.0
xlwt: 0.8.0
xlsxwriter: None
lxml: None
bs4: 4.5.1
html5lib: 0.9999999
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.1.4
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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| 194,405,292 |
MDU6SXNzdWUxOTQ0MDUyOTI=
| 14,834 |
test_round_trip_exception_ got stuck (on s390x)
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| 3 | 2016-12-08T18:10:05Z | 2016-12-11T20:33:21Z | 2016-12-09T14:02:59Z |
CONTRIBUTOR
| null |
Just FYI and for reference (feel free to close)... was running all the tests and they got stuck on this one
then tried to rerun this one specifically and just lead to certificate verification problem :-/
```shell
(sid_s390x-dchroot)yoh@zelenka:~/pandas$ HOME=$PWD/build LC_ALL=C.UTF-8 xvfb-run -a -s "-screen 0 1280x1024x24 -noreset" nosetests -s -v pandas/io/tests/json/test_pandas.py:TestPandasContainer.test_round_trip_exception_
test_round_trip_exception_ (pandas.io.tests.json.test_pandas.TestPandasContainer) ... ERROR
======================================================================
ERROR: test_round_trip_exception_ (pandas.io.tests.json.test_pandas.TestPandasContainer)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/yoh/pandas/pandas/util/testing.py", line 2259, in wrapper
return t(*args, **kwargs)
File "/home/yoh/pandas/pandas/io/tests/json/test_pandas.py", line 762, in test_round_trip_exception_
df = pd.read_csv(csv)
File "/home/yoh/pandas/pandas/io/parsers.py", line 655, in parser_f
return _read(filepath_or_buffer, kwds)
File "/home/yoh/pandas/pandas/io/parsers.py", line 385, in _read
compression=kwds.get('compression', None))
File "/home/yoh/pandas/pandas/io/common.py", line 240, in get_filepath_or_buffer
req = _urlopen(str(filepath_or_buffer))
File "/usr/lib/python2.7/urllib2.py", line 154, in urlopen
return opener.open(url, data, timeout)
File "/usr/lib/python2.7/urllib2.py", line 429, in open
response = self._open(req, data)
File "/usr/lib/python2.7/urllib2.py", line 447, in _open
'_open', req)
File "/usr/lib/python2.7/urllib2.py", line 407, in _call_chain
result = func(*args)
File "/usr/lib/python2.7/urllib2.py", line 1241, in https_open
context=self._context)
File "/usr/lib/python2.7/urllib2.py", line 1198, in do_open
raise URLError(err)
URLError: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:661)>
----------------------------------------------------------------------
Ran 1 test in 0.120s
FAILED (errors=1)
```
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MDU6SXNzdWUxOTQ0NDU3NTM=
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Efficient way to create a DataFrame iteratively
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| 2 | 2016-12-08T21:11:59Z | 2016-12-09T08:36:09Z | 2016-12-09T07:11:04Z |
NONE
| null |
I am working on a financial simulation ([financial_life](https://github.com/MartinPyka/financial_life)), in which I would like to collect my timeseries data in a DataFrame during simulation. However, while looking through several posts on StackOverflow and experimenting with timeit, I noticed that appending data to a Dataframe (e.g. via append or via loc-expansion) is far more inefficient than the append-function for a simple list and then transforming everything to a dataframe, if requested. The latter one is, what I am doing now.
So I was wondering, whether there is any performant way of creating a DataFrame in an iterative manner?
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BUG: label-based indexing fails with certain list indexers in case of mixed integers/strings columns names
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| 4 | 2016-12-08T23:30:48Z | 2019-07-23T21:16:16Z | 2019-07-23T21:16:16Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
This works, when all columns are integer labels:
```python
df = pd.DataFrame(np.random.rand(3,3), columns=[2011,2012,2013], index=list('ABC'))
df.ix[pd.IndexSlice[['A','B','C'],[2011,2012]]]
Out:
2011 2012
A 0.289341 0.651091
B 0.528271 0.682148
C 0.742617 0.578734
```
This doesn't, when columns are a mix of integers and strings:
```python
df = pd.DataFrame(np.random.rand(3,3), columns=[2011,2012,'All'], index=list('ABC'))
# This crashes
df.ix[pd.IndexSlice[['A','B','C'],[2011,2012]]]
> IndexError: index 2011 is out of bounds for axis 0 with size 3
# this works though:
df.ix[pd.IndexSlice[['A','B','C'],[2011,'All']]]
```
#### Problem description
It seems that in the second case it's trying to find it by position rather than label.
#### Expected Output
I would expect pandas to understand that I'm trying to lookup by label rather than position.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 28.7.1
Cython: 0.25.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.8.2
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: None
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: None
bs4: 4.5.1
html5lib: 0.999999999
httplib2: None
apiclient: None
sqlalchemy: 1.1.4
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: 0.2.1
</details>
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| 14,837 |
DOC: astype now takes dict mapping col names to datatypes (#14761)
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| 7 | 2016-12-09T12:06:00Z | 2016-12-22T13:37:19Z | 2016-12-22T13:37:19Z |
CONTRIBUTOR
| null |
- [x] closes https://github.com/pandas-dev/pandas/issues/14761
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
Updating documentation to reflect change
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MDU6SXNzdWUxOTQ2MTE5MDk=
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Plot does not work in the 10 Minutes tutorial
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| 12 | 2016-12-09T14:40:34Z | 2017-03-01T16:41:40Z | 2016-12-19T17:53:36Z |
NONE
| null |
The following codes from [10 Minutes to pandas](http://pandas.pydata.org/pandas-docs/stable/10min.html) could not be run properly.
```python
In [135]: ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
In [136]: ts = ts.cumsum()
In [137]: ts.plot()
```
#### Problem description
It gives error as follows. Is that caused by some dependency problem or other issues? Thanks.
```python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-154-72917dc50e90> in <module>()
3 ts = ts.cumsum()
4
----> 5 ts.plot()
/home/myubuntu/anaconda2/envs/tensorflow3/lib/python3.5/site-packages/pandas/tools/plotting.py in __call__(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)
3597 colormap=colormap, table=table, yerr=yerr,
3598 xerr=xerr, label=label, secondary_y=secondary_y,
-> 3599 **kwds)
3600 __call__.__doc__ = plot_series.__doc__
3601
/home/myubuntu/anaconda2/envs/tensorflow3/lib/python3.5/site-packages/pandas/tools/plotting.py in plot_series(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)
2671 yerr=yerr, xerr=xerr,
2672 label=label, secondary_y=secondary_y,
-> 2673 **kwds)
2674
2675
/home/myubuntu/anaconda2/envs/tensorflow3/lib/python3.5/site-packages/pandas/tools/plotting.py in _plot(data, x, y, subplots, ax, kind, **kwds)
2467 plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)
2468
-> 2469 plot_obj.generate()
2470 plot_obj.draw()
2471 return plot_obj.result
/home/myubuntu/anaconda2/envs/tensorflow3/lib/python3.5/site-packages/pandas/tools/plotting.py in generate(self)
1041 self._compute_plot_data()
1042 self._setup_subplots()
-> 1043 self._make_plot()
1044 self._add_table()
1045 self._make_legend()
/home/myubuntu/anaconda2/envs/tensorflow3/lib/python3.5/site-packages/pandas/tools/plotting.py in _make_plot(self)
1724 stacking_id=stacking_id,
1725 is_errorbar=is_errorbar,
-> 1726 **kwds)
1727 self._add_legend_handle(newlines[0], label, index=i)
1728
/home/myubuntu/anaconda2/envs/tensorflow3/lib/python3.5/site-packages/pandas/tools/plotting.py in _ts_plot(cls, ax, x, data, style, **kwds)
1762 ax._plot_data.append((data, cls._kind, kwds))
1763
-> 1764 lines = cls._plot(ax, data.index, data.values, style=style, **kwds)
1765 # set date formatter, locators and rescale limits
1766 format_dateaxis(ax, ax.freq)
/home/myubuntu/anaconda2/envs/tensorflow3/lib/python3.5/site-packages/pandas/tools/plotting.py in _plot(cls, ax, x, y, style, column_num, stacking_id, **kwds)
1739 cls._initialize_stacker(ax, stacking_id, len(y))
1740 y_values = cls._get_stacked_values(ax, stacking_id, y, kwds['label'])
-> 1741 lines = MPLPlot._plot(ax, x, y_values, style=style, **kwds)
1742 cls._update_stacker(ax, stacking_id, y)
1743 return lines
/home/myubuntu/anaconda2/envs/tensorflow3/lib/python3.5/site-packages/pandas/tools/plotting.py in _plot(cls, ax, x, y, style, is_errorbar, **kwds)
1359 else:
1360 args = (x, y)
-> 1361 return ax.plot(*args, **kwds)
1362
1363 def _get_index_name(self):
/home/myubuntu/anaconda2/envs/tensorflow3/lib/python3.5/site-packages/matplotlib/__init__.py in inner(ax, *args, **kwargs)
1817 warnings.warn(msg % (label_namer, func.__name__),
1818 RuntimeWarning, stacklevel=2)
-> 1819 return func(ax, *args, **kwargs)
1820 pre_doc = inner.__doc__
1821 if pre_doc is None:
/home/myubuntu/anaconda2/envs/tensorflow3/lib/python3.5/site-packages/matplotlib/axes/_axes.py in plot(self, *args, **kwargs)
1381
1382 for line in self._get_lines(*args, **kwargs):
-> 1383 self.add_line(line)
1384 lines.append(line)
1385
/home/myubuntu/anaconda2/envs/tensorflow3/lib/python3.5/site-packages/matplotlib/axes/_base.py in add_line(self, line)
1701 line.set_clip_path(self.patch)
1702
-> 1703 self._update_line_limits(line)
1704 if not line.get_label():
1705 line.set_label('_line%d' % len(self.lines))
/home/myubuntu/anaconda2/envs/tensorflow3/lib/python3.5/site-packages/matplotlib/axes/_base.py in _update_line_limits(self, line)
1723 Figures out the data limit of the given line, updating self.dataLim.
1724 """
-> 1725 path = line.get_path()
1726 if path.vertices.size == 0:
1727 return
/home/myubuntu/anaconda2/envs/tensorflow3/lib/python3.5/site-packages/matplotlib/lines.py in get_path(self)
936 """
937 if self._invalidy or self._invalidx:
--> 938 self.recache()
939 return self._path
940
/home/myubuntu/anaconda2/envs/tensorflow3/lib/python3.5/site-packages/matplotlib/lines.py in recache(self, always)
623 x = ma.asarray(xconv, np.float_).filled(np.nan)
624 else:
--> 625 x = np.asarray(xconv, np.float_)
626 x = x.ravel()
627 else:
/home/myubuntu/anaconda2/envs/tensorflow3/lib/python3.5/site-packages/numpy/core/numeric.py in asarray(a, dtype, order)
480
481 """
--> 482 return array(a, dtype, copy=False, order=order)
483
484 def asanyarray(a, dtype=None, order=None):
TypeError: float() argument must be a string or a number, not 'Period'
```
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-38-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 27.2.0
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0OTczMjA1Mjc=
| 14,839 |
Fix To Numeric on Decimal Fields
|
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| 2 | 2016-12-09T14:43:49Z | 2016-12-09T15:38:57Z | 2016-12-09T14:53:47Z |
NONE
| null |
- [x] closes #14827
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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MDU6SXNzdWUxOTQ2MTUxNDg=
| 14,840 |
Data corruption in numerical DataFrame using joblib and shared memory
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[] | null | 1 | 2016-12-09T14:54:15Z | 2016-12-09T15:33:44Z | 2016-12-09T15:33:35Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import joblib
import multiprocessing
import numpy as np
import os
import pandas as pd
import tempfile
df = pd.DataFrame(np.random.random((1000,3)))
# df.head() is
# 0 1 2
# 0 0.204271 0.250022 0.333579
# 1 0.894605 0.262193 0.412623
# 2 0.260175 0.056537 0.084432
# 3 0.774219 0.824558 0.521398
# 4 0.414248 0.913390 0.735691
temp_folder = tempfile.mkdtemp()
filename = os.path.join(temp_folder, 'joblib_test.mmap')
if os.path.exists(filename): os.unlink(filename)
_ = joblib.dump(df, filename)
memmap = joblib.load(filename, mmap_mode='r')
def myFunc(df) :
return df
results = joblib.Parallel(n_jobs=2)(joblib.delayed(myFunc)(memmap) for _ in range(10))
# results[0].head() is
# 0 1 2
# 0 1.105907e+169 5.745991e+169 5.298748e+180
# 1 3.036710e+213 9.708092e+189 5.587160e+238
# 2 2.379254e+233 7.281947e+223 1.278075e+161
# 3 7.658495e+151 1.452701e-253 6.235120e-85
# 4 6.240245e-85 6.340871e+160 9.041151e+271
np.allclose(results[0], df) # False!
```
#### Problem description
When a Pandas DataFrame is used as a shared memory object for parallel computing using ```joblib``` and ```multiprocessing```, the data gets corrupted.
In the above example, the column name and index stays correct, while the internal data (```df.values```) shows random values. However, if not a DataFrame but a NumPy array (simply ```df.values```) is stored in the memory map, everything works as expected.
We found that the error only occurs if all of the following criteria are met:
- a Pandas DataFrame is used
- the DataFrame contains numerical data (strings and other objects work as expected)
- the DataFrame has a certain size, e.g., the above example works as expected with small data
- the DataFrame is stored and loaded using ```joblib.dump``` and ```joblib.load```
- more than one processor is used
#### Expected Output
We would expect that the all of the elements in results are the same as the original DataFrame and that
```np.allclose(results[0], df)``` is True.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.8.12-moby
machine: x86_64
processor:
byteorder: little
LC_ALL: C.UTF-8
LANG: C.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.1
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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| 194,619,086 |
MDU6SXNzdWUxOTQ2MTkwODY=
| 14,841 |
Groupby iteration fails when one of the key's values is None
|
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| 2 | 2016-12-09T15:10:43Z | 2016-12-09T15:30:15Z | 2016-12-09T15:23:34Z |
NONE
| null |
#### Code Sample
```python
import pandas as pd
df = pd.DataFrame({
'a': [1, 1, 2, 3],
'b': [None, 1, 2, 3],
'c': [1,2,3,4],
})
grouped = df.groupby(('a', 'b'))
print('Number of groups:', len(grouped))
# Number of groups: 4
num_iterations = 0
for _ in grouped:
num_iterations += 1
print('Number of groups iterated over:', num_iterations)
# Number of groups iterated over: 3
```
#### Problem description
Presently, when a `GroupBy` object is iterated over, any group where one of the columns grouped by is `None` is skipped.
This is a problem because when iterating over groups, we expect to iterate over all groups.
It is also a problem because it is not sensible to say the length of an iterable is `x` when iterating over it only performs some `y < x` number of iterations.
#### Expected Output
I'd expect iterating over a `GroupBy` object to iterate over all groups, regardless of the value in the key columns.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-504.12.2.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 9.0.1
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.1.0
tables: 3.3.0
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext)
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
</details>
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| 194,625,128 |
MDExOlB1bGxSZXF1ZXN0OTczMjkxODQ=
| 14,842 |
Fix to numeric on decimal fields
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| 10 | 2016-12-09T15:35:21Z | 2016-12-20T13:32:02Z | 2016-12-20T13:29:23Z |
NONE
| null |
- [X] closes #14827
- [X] tests added / passed
- [X] passes ``git diff upstream/master | flake8 --diff``
- [X] whatsnew entry
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DOC: DataFrame.resample docs could be improved
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| 3 | 2016-12-09T19:15:43Z | 2017-03-09T16:49:20Z | 2017-03-09T16:49:20Z |
CONTRIBUTOR
| null |
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.resample.html?highlight=dataframe%20resample#pandas.DataFrame.resample
These are the *same* docs for both Series & DataFrame. so might be nice to add some DataFrame examples (and use on / axis parameters). So may need to move these to a ``shared_docs`` template to suppport this.
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merge_asof() must be able to operate with timezone-aware DatetimeIndex
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| 0 | 2016-12-09T19:52:08Z | 2016-12-10T15:41:42Z | 2016-12-10T15:41:42Z |
CONTRIBUTOR
| null |
I can perform the following merge just fine:
```python
left = pd.DataFrame({'date': pd.DatetimeIndex(start=pd.to_datetime('2016-01-02'),
freq='D', periods=5),
'value1':np.arange(5)})
right = pd.DataFrame({'date': pd.DatetimeIndex(start=pd.to_datetime('2016-01-01'),
freq='D', periods=5),
'value2':list("ABCDE")})
pd.merge_asof(left, right, on='date', tolerance=pd.Timedelta('1 day'))
```
However, adding a timezone to the DatetimeIndex doesn't work:
```python
import pytz
left = pd.DataFrame({'date': pd.DatetimeIndex(start=pd.to_datetime('2016-01-02'),
freq='D', periods=5, tz=pytz.timezone('UTC')),
'value1':np.arange(5)})
right = pd.DataFrame({'date': pd.DatetimeIndex(start=pd.to_datetime('2016-01-01'),
freq='D', periods=5, tz=pytz.timezone('UTC')),
'value2':list("ABCDE")})
pd.merge_asof(left, right, on='date', tolerance=pd.Timedelta('1 day'))
```
I get the oddly worded
```python
MergeError: incompatible tolerance, must be compat with type <class 'pandas.tseries.index.DatetimeIndex'>
```
The solution is actually very simple. `_AsOfMerge._get_merge_keys()` needs to check for `is_datetime64tz_dtype()` in addition to `is_datetime64_dtype()` when there is a tolerance. I should also fix the error message to be clearer.
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BUG: Allow TZ-aware DatetimeIndex in merge_asof() (#14844)
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| 4 | 2016-12-09T20:15:26Z | 2016-12-10T15:41:42Z | 2016-12-10T15:41:42Z |
CONTRIBUTOR
| null |
- [x] closes #14844
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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BUG: DataFrame.nlargest() returns incorrect result when DataFrame has non-unique index
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[] | null | 3 | 2016-12-09T21:01:30Z | 2017-02-21T14:44:18Z | 2016-12-09T21:25:38Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df=pd.DataFrame({'variable': ['a','a','b','b','c','c'],
'value' : [1000,2000,10,20,100,200]},
index=[1,2]*3)
df.nlargest(3,'value')
```
#### Problem description
The result should only have 3 rows. If the index has unique values, it is correct. But when the index has duplicate values, the incorrect result is produced.
The example produces the following output:
```
value variable
2 2000 a
2 2000 a
1 1000 a
2 200 c
2 200 c
1 100 c
2 20 b
2 20 b
1 10 b
```
#### Expected Output
```
value variable
2 2000 a
1 1000 a
2 200 c
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.0
nose: None
pip: 8.1.2
setuptools: 27.2.0
Cython: None
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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MDU6SXNzdWUxOTQ3MTg3Mjg=
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what is a KeyError in Python 3.5/Pandas?
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| 2 | 2016-12-09T23:05:34Z | 2016-12-10T09:59:01Z | 2016-12-09T23:15:58Z |
NONE
| null |
I work with files were exported from stream twitter . but when run code then error : KeyError: 'text' on tweets['python'] = tweets['text'].apply(lambda tweet: word_in_text('python', tweet)). how to fix it ? thank everyone..!.
`import re
import json
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
tweets_data_path = '...\\..\\log1000.txt'
tweets_data = []
tweets_file = open(tweets_data_path, "r")
for line in tweets_file:
try:
tweet = json.loads(line)
tweets_data.append(tweet)
except:
continue
def word_in_text(word, text):
word = word.lower()
text = text.lower()
match = re.search(word, text)
if match:
return True
return False
#------------------------DataFrame--------------------------
tweets = pd.DataFrame()
#------------------------------------------------------------------------
tweets['python'] = tweets['text'].apply(lambda tweet: word_in_text('python', tweet))
#----------------------------------------------------------------
print (tweets['python'].value_counts()[True])`
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| 3 | 2016-12-10T00:31:23Z | 2017-01-13T17:38:20Z | 2017-01-13T17:38:20Z |
NONE
| null |
This is the weirdest bug I have seen in Pandas. But I am guessing (hoping) the fix will not be too difficult.
#### Code Sample
Consider the following two code blocks:
**Block 1: key column is called "k"**
```
>>> df1 = pd.DataFrame({'x' : [1,2,3,4,5]*3,
'y' : [10,20,30,40,50]*3,
'z' : [100,200,300,400,500]*3})
>>> df1['k'] = [(0,0,1),(0,1,0),(1,0,0)]*5
```
**Block 2: key column is called "key"**
```
>>> df2 = pd.DataFrame({'x' : [1,2,3,4,5]*3,
'y' : [10,20,30,40,50]*3,
'z' : [100,200,300,400,500]*3})
>>> df2['key'] = [(0,0,1),(0,1,0),(1,0,0)]*5
```
Note that the same, static data is used, so that nothing else may be different, and hence culpable.
#### Problem description
Running a simple `.groupby().describe()` operation produces the following results:
```
>>> df1.groupby('k').describe()
# No Result
>>> df2.groupby('key').describe()
x y z
key
(0, 0, 1) count 5.000000 5.000000 5.000000
mean 3.000000 30.000000 300.000000
std 1.581139 15.811388 158.113883
min 1.000000 10.000000 100.000000
25% 2.000000 20.000000 200.000000
50% 3.000000 30.000000 300.000000
75% 4.000000 40.000000 400.000000
max 5.000000 50.000000 500.000000
(0, 1, 0) count 5.000000 5.000000 5.000000
mean 3.000000 30.000000 300.000000
std 1.581139 15.811388 158.113883
min 1.000000 10.000000 100.000000
25% 2.000000 20.000000 200.000000
50% 3.000000 30.000000 300.000000
75% 4.000000 40.000000 400.000000
max 5.000000 50.000000 500.000000
(1, 0, 0) count 5.000000 5.000000 5.000000
mean 3.000000 30.000000 300.000000
std 1.581139 15.811388 158.113883
min 1.000000 10.000000 100.000000
25% 2.000000 20.000000 200.000000
50% 3.000000 30.000000 300.000000
75% 4.000000 40.000000 400.000000
max 5.000000 50.000000 500.000000
```
Note that `groupby().mean()`, `sum()`, and a few others work fine. `describe()` is the only one I think is causing the problem.
#### Expected Output
Obviously, the expected output for `df1.groupby('k').describe()` should be the same as `df2.groupby('key').describe()`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-53-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.8.2
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.9.4
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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| 194,730,027 |
MDU6SXNzdWUxOTQ3MzAwMjc=
| 14,849 |
groupby type coercion dependent on presence of datetime column in grouped data
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| 3 | 2016-12-10T00:34:17Z | 2020-01-21T01:06:10Z | 2020-01-21T01:06:10Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
foo = pd.DataFrame.from_records(
[
(pd.datetime(2016,1,1), 'red', 'dark', 1, '8'),
(pd.datetime(2015,1,1), 'green', 'stormy', 2, '9'),
(pd.datetime(2014,1,1), 'blue', 'bright', 3, '10'),
(pd.datetime(2013,1,1), 'blue', 'calm', 4, 'potato')
],
columns=['observation', 'color', 'mood', 'intensity', 'score'])
# The type of 'score' changes depending on the types passed through the groupby
print(pd.concat(
[
foo.dtypes,
foo.loc[:,['observation', 'color', 'mood', 'intensity', 'score']].groupby('color').apply(lambda g: g.iloc[0]).dtypes,
foo.loc[:,[ 'color', 'mood', 'intensity', 'score']].groupby('color').apply(lambda g: g.iloc[0]).dtypes
],
axis=1,
keys=['original DF', 'w/ datetime', 'w/o datetime']))
```
#### Problem description
When the results of a groupby contain a Series with a datetime and are aggregated back into a DataFrame, columns of object type are cast numeric when possible. When that Series contains no datetime, they are not.
The presence of a datetime elsewhere in the Series should not have effects on unrelated columns. Doing no implicit type coercion seems (to me) like the safest option (especially in a language where "1" != 1). But regardless, whether or not type coercion is done for a column 'A' should not depend on the types of all the column 'B's.
Issue #14423 is a different problem over the same code.
#### Expected Output
Current:
```
original DF w/ datetime w/o datetime
color object object object
intensity int64 int64 int64
mood object object object
observation datetime64[ns] datetime64[ns] NaN
score object int64 object
```
Expected:
```
original DF w/ datetime w/o datetime
color object object object
intensity int64 int64 int64
mood object object object
observation datetime64[ns] datetime64[ns] NaN
score object object object
```
**-or-**
```
original DF w/ datetime w/o datetime
color object object object
intensity int64 int64 int64
mood object object object
observation datetime64[ns] datetime64[ns] NaN
score object int64 int64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-101-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.1
nose: 1.3.1
pip: 1.5.4
setuptools: 3.3
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: 3.3.3
bs4: 4.2.1
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
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| 194,754,983 |
MDExOlB1bGxSZXF1ZXN0OTc0MTg2OTI=
| 14,850 |
BUG: Spurious IncompatibleFrequency error prevented plotting of non-standard intervals (Fixes #14763)
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[] | null | 3 | 2016-12-10T09:04:31Z | 2016-12-18T20:21:01Z | 2016-12-18T20:21:01Z |
CONTRIBUTOR
| null |
- [x] closes #14763
- [x] tests added: tests/plotting/test_series.py: test_non_standard_intervals
- [x] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry - Confirm version and I'll add
Fix Summary: A check on frequency compatibility was unnecessarily strict. Now IncompatibleFrequency exceptions will only get raised if the unit types differ.
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TST: skip testing on windows for specific formatting which sometimes hang
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| 3 | 2016-12-10T14:52:35Z | 2016-12-10T15:31:53Z | 2016-12-10T15:31:53Z |
CONTRIBUTOR
| null |
xref #14626
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SystemError: ../Objects/listobject.c:290: bad argument to internal function [i386,armel,...]
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[] | null | 6 | 2016-12-10T18:07:57Z | 2021-05-02T00:57:14Z | 2021-05-02T00:57:14Z |
CONTRIBUTOR
| null |
Any idea on what could cause such an exception on 32bit platform (Debian sid i386), 0.19.1
```
======================================================================
ERROR: test_frame_from_json_to_json (pandas.io.tests.json.test_pandas.TestPandasContainer)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/json/test_pandas.py", line 272, in test_frame_from_json_to_json
_check_all_orients(self.frame)
File "/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/json/test_pandas.py", line 246, in _check_all_orients
check_index_type=False, check_column_type=False)
File "/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/json/test_pandas.py", line 165, in _check_orient
numpy=numpy, convert_axes=convert_axes)
File "/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/json.py", line 281, in read_json
date_unit).parse()
File "/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/json.py", line 346, in parse
self._parse_numpy()
File "/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/json.py", line 541, in _parse_numpy
precise_float=self.precise_float)
SystemError: ../Objects/listobject.c:290: bad argument to internal function
======================================================================
ERROR: test_misc_example (pandas.io.tests.json.test_pandas.TestPandasContainer)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/json/test_pandas.py", line 743, in test_misc_example
result = read_json('[{"a": 1, "b": 2}, {"b":2, "a" :1}]', numpy=True)
File "/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/json.py", line 281, in read_json
date_unit).parse()
File "/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/json.py", line 346, in parse
self._parse_numpy()
File "/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/json.py", line 541, in _parse_numpy
precise_float=self.precise_float)
SystemError: ../Objects/listobject.c:290: bad argument to internal function
======================================================================
ERROR: testArrayNumpyLabelled (pandas.io.tests.json.test_ujson.NumpyJSONTests)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/json/test_ujson.py", line 1178, in testArrayNumpyLabelled
output = ujson.loads(input_dumps, numpy=True, labelled=True)
SystemError: ../Objects/listobject.c:290: bad argument to internal function
======================================================================
ERROR: testDataFrameNumpyLabelled (pandas.io.tests.json.test_ujson.PandasJSONTests)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/json/test_ujson.py", line 1286, in testDataFrameNumpyLabelled
numpy=True, labelled=True))
SystemError: ../Objects/listobject.c:290: bad argument to internal function
```
full build log: https://buildd.debian.org/status/fetch.php?pkg=pandas&arch=i386&ver=0.19.1-3&stamp=1481348917
happens also on some other architectures: e.g. armel https://buildd.debian.org/status/fetch.php?pkg=pandas&arch=armel&ver=0.19.1-3&stamp=1481359184
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TST: Test DatetimeIndex weekend offset
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| 2 | 2016-12-10T21:23:26Z | 2016-12-11T14:07:20Z | 2016-12-11T14:06:54Z |
MEMBER
| null |
closes #5348
closes #5115
closes #5152
The original issues pass on master as of 0.19.1. These related issues might have been fixed part of #5004, but it doesn't look like that any PRs fixed this recently.
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TST: timeseries tests reorg
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| 6 | 2016-12-10T22:08:22Z | 2017-02-09T23:40:59Z | 2017-02-09T23:40:59Z |
CONTRIBUTOR
| null |
Datetime
------------
- [x] We have *some* DatetimeIndex tests in [here](https://github.com/pandas-dev/pandas/blob/master/pandas/tseries/tests/test_timeseries.py) and some in [here](https://github.com/pandas-dev/pandas/blob/master/pandas/tests/indexes/test_datetimelike.py) and *a lot* [here](https://github.com/pandas-dev/pandas/blob/master/pandas/tseries/tests/test_base.py) (PR #15266)
we can separate these out to a single file in ``pandas/tests/indexes/test_datetime.py``
The ``Base`` class (which powers lots of tests for the sub-classes ``DatetimeIndex``, ``PeriodIndex``, ``TimedeltaIndex``), can move to ``pandas/tests/indexes/tests_datetimelike.py`` (and merge with the existing ``Datetimelike`` class).
- [ ] create a ``pandas/tests/scalar/test_timestamp.py`` with ``Timestamp`` specific tests
There are a lot of tests also in [tseries/test_tslib.py](https://github.com/pandas-dev/pandas/blob/master/pandas/tseries/tests/test_tslib.py), that can be merged into ``test_timestamp.py``
- [ ] the residuals (from ``tests_timeseries.py`` are Series tests speficially, these can move to ``pandas/tests/series/test_timeseries.py``
``Legacy Tests``
---------------
- [ ] [timeseries_legacy](https://github.com/pandas-dev/pandas/blob/master/pandas/tseries/tests/test_timeseries_legacy.py) need a look, but can prob be deleted
``Period``
----------
- [ ] same idea with Period, the actual ``Period`` tests can be moved to ``tests/scalar/test_period.py``, but move the index tests to ``tests/indexes/test_period.py`` (which can be stripped from [here](https://github.com/pandas-dev/pandas/blob/master/pandas/tests/indexes/test_datetimelike.py) and [here](https://github.com/pandas-dev/pandas/blob/master/pandas/tseries/tests/test_base.py))
``Timedelta``
-----------
- [ ] same idea with Timedelta, the actual ``Timedelta`` tests can be moved to ``tests/scalar/test_timedelta.py`` but move the index tests to ``tests/indexes/test_timedelta.py`` (which can be stripped from [here](https://github.com/pandas-dev/pandas/blob/master/pandas/tests/indexes/test_datetimelike.py) and [here](https://github.com/pandas-dev/pandas/blob/master/pandas/tseries/tests/test_base.py))
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scatter_matrix `color` vs `c`
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| 1 | 2016-12-11T01:30:23Z | 2017-04-05T21:47:34Z | 2017-04-05T21:47:34Z |
NONE
| null |
I reported the issue below to matplotlib [here](https://github.com/matplotlib/matplotlib/issues/7603#issuecomment-266250371), but they have requested to report it here as it appears (to the matplotlib folks) related to pandas.
The following code gives error, but works replacing `color=` with `c=`
I suspect is either a bug, or some piece of documentation missing?
(matplotlib folks think it is a pandas / scatter_matrix bug - see below)
I'm using:
matplotlib 1.5.3
pandas 0.19.1
python 3.5
This is a sample code:
```
import pandas as pd
import numpy as np
from sklearn.datasets import load_iris
%matplotlib inline
iris = load_iris()
colors = list()
palette = {0: "red", 1: "green", 2: "blue"}
for c in np.nditer(iris.target): colors.append(palette[int(c)])
# using the palette dictionary, we convert
# each numeric class into a color string
dataframe = pd.DataFrame(iris.data,
columns=iris.feature_names)
scatterplot = pd.scatter_matrix(dataframe, alpha=0.3,
figsize=(10, 10), diagonal='hist', color=colors, marker='o', grid=True)
```
This is the error trace:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-4-f3d30057e124> in <module>()
14 columns=iris.feature_names)
15 scatterplot = pd.scatter_matrix(dataframe, alpha=0.3,
---> 16 figsize=(10, 10), diagonal='hist', color=colors, marker='o', grid=True)
/Users/e/anaconda/lib/python3.5/site-packages/pandas/tools/plotting.py in scatter_matrix(frame, alpha, figsize, ax, grid, diagonal, marker, density_kwds, hist_kwds, range_padding, **kwds)
393
394 ax.scatter(df[b][common], df[a][common],
--> 395 marker=marker, alpha=alpha, **kwds)
396
397 ax.set_xlim(boundaries_list[j])
/Users/e/anaconda/lib/python3.5/site-packages/matplotlib/__init__.py in inner(ax, *args, **kwargs)
1817 warnings.warn(msg % (label_namer, func.__name__),
1818 RuntimeWarning, stacklevel=2)
-> 1819 return func(ax, *args, **kwargs)
1820 pre_doc = inner.__doc__
1821 if pre_doc is None:
/Users/e/anaconda/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)
3787 facecolors = co
3788 if c is not None:
-> 3789 raise ValueError("Supply a 'c' kwarg or a 'color' kwarg"
3790 " but not both; they differ but"
3791 " their functionalities overlap.")
ValueError: Supply a 'c' kwarg or a 'color' kwarg but not both; they differ but their functionalities overlap.
```
According to @goyodiaz:
> It looks like a pandas issue. pandas.scatter_matrix always pass c to scatter but the caller can optionally pass more keywords arguments, including color. If the caller pass only color then scatter_matrix will pass both c and color and matplotlib will raise with a message that is confusing to the user because they did not pass c. Probably scatter_matrix should either raise with a clearer message or drop the c keyword argument when the user pass color
.
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Fix inconsistency in Partial String Index with 'second' resolution
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| 9 | 2016-12-11T07:27:14Z | 2016-12-22T22:10:08Z | 2016-12-20T17:44:52Z |
CONTRIBUTOR
| null |
- Closes #14826
- Tests added: test_partial_string_indexes. Comprehensive testing of partial string indexes.
## Whatsnew entry
Fix inconsistency in Partial String Index with 'second' resolution. See #14826. Now if the timestamp and the index both have resolution `second`, timestamp is considered as an exact match try and not a slice. Therefore, for `Series`, scalar will be returned, for `DataFrame` `KeyError` raised.
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df.info() returning negative byte value
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| 18 | 2016-12-11T12:47:00Z | 2017-10-14T21:39:14Z | 2017-10-14T21:39:14Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
df.info()
dtypes: float64(9), int64(1), object(17)
memory usage: -611431520.0+ bytes
```
#### Problem description
Value returned is negative and should be positive.
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
#### Expected Output
Positive value > 4GB
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.7
pip: 9.0.1
setuptools: 20.10.1
Cython: 0.25.2
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.8.1
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.2
blosc: 1.4.4
bottleneck: 1.2.0
tables: None
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.2
bs4: 4.5.1
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
</details>
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BUG: fillna with inplace does not work with multiple columns selection by loc
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| 3 | 2016-12-11T18:08:23Z | 2018-10-11T14:35:27Z | 2016-12-11T19:55:54Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame(np.random.randn(3, 4), columns=list('ABCD'))
df.iloc[1, 2:4] = np.nan
df.loc[:, ['C', 'D']].fillna(-1, inplace=True)
display(df)
```
Output:
```
A B C D
0 1.387547 -1.299578 0.360015 1.290783
1 -0.395182 -0.112581 NaN NaN
2 -0.649372 -1.831869 -0.103746 0.533153
```
#### Problem description
It's expected to modify the Nan to -1 but it does **NOT**.
Please see the following comparisons.
#### Comparison (1)
On contrary, the following codes behave as expected.
(The **only difference** is selection by **iloc** or by **loc**)
```python
df = pd.DataFrame(np.random.randn(3, 4), columns=list('ABCD'))
df.iloc[1, 2:4] = np.nan
df.iloc[:, 2:4].fillna(-1, inplace=True)
display(df)
```
Output:
```
A B C D
0 -0.522821 -1.600520 -1.468871 0.715790
1 0.493071 0.722474 -1.000000 -1.000000
2 0.545852 -0.877946 0.993169 -0.582661
```
#### Comparison (2)
When only **one** column is selected with loc, it behaves properly.
```python
df = pd.DataFrame(np.random.randn(3, 4), columns=list('ABCD'))
df.iloc[1, 2:4] = np.nan
df.loc[:, 'C'].fillna(-1, inplace=True)
display(df)
```
Output:
```
A B C D
0 -0.549106 0.261093 -1.278554 2.017178
1 -1.424498 0.439482 -1.000000 NaN
2 -1.281520 1.190736 0.356319 0.416363
```
#### Expected Output of the first code sample
```
A B C D
0 1.181106 1.101231 -0.198445 0.295238
1 -0.654265 -1.129840 -1.000000 -1.000000
2 -1.070404 0.096556 0.499020 -1.835347
```
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-358.14.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: zh_TW.big5
LOCALE: zh_TW.big5
pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 27.2.0
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0OTc0ODA2NzE=
| 14,859 |
PERF: use StringHasTable for strings
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| 4 | 2016-12-12T00:31:17Z | 2016-12-15T23:09:37Z | 2016-12-15T23:09:37Z |
CONTRIBUTOR
| null |
xref #13745
provides a modest speedup for all string hashing. The key thing is, it will release the GIL on more operations where this is possible (mainly ``factorize``).
can be easily extended to ``.value_counts()`` and ``.duplicated()`` (for strings), new issue for that one
```
In [9]: np.random.seed(1234)
In [10]: strings = tm.makeStringIndex(1000000)
In [11]: def f():
...: for i in range(2):
...: pd.factorize(strings)
...:
In [12]: @tm.test_parallel(num_threads=2)
...: def g():
...: pd.factorize(strings)
...:
In [13]: %timeit f()
1 loop, best of 3: 685 ms per loop
In [14]: %timeit g()
1 loop, best of 3: 446 ms per loop
In [15]: strings = strings.take(np.random.randint(0,1000,size=len(strings)))
In [16]: strings.nunique()
Out[16]: 1000
In [17]: %timeit f()
1 loop, best of 3: 222 ms per loop
In [18]: %timeit g()
10 loops, best of 3: 190 ms per loop
```
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PERF: use StringHashTable for value_counts / duplicated with strings
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| 0 | 2016-12-12T00:32:24Z | 2020-04-03T03:47:10Z | null |
CONTRIBUTOR
| null |
xref https://github.com/pandas-dev/pandas/pull/14859
- [ ] ``.value_counts()``
- [ ] ``.duplicated()``
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DOC: groupby.rst examples broken
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| 2 | 2016-12-12T01:37:45Z | 2016-12-14T10:43:59Z | 2016-12-12T11:16:15Z |
CONTRIBUTOR
| null |
the df for the groupby.resample examples are hiding the correct frame that was previously being used by the docs below
http://pandas-docs.github.io/pandas-docs-travis/groupby.html#dispatching-to-instance-methods
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TST: Parse dates with empty space (#6428)
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| 2 | 2016-12-12T05:07:13Z | 2016-12-14T11:07:36Z | 2016-12-14T11:07:26Z |
MEMBER
| null |
- [x] closes #6428
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
Parsing a date with an empty space no longer returns today's date on master 0.19.1. Not sure when this was fixed, but it doesn't seem like it occurred recently.
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DOC: fix groupby.rst building errors #14861
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| 2 | 2016-12-12T08:44:23Z | 2016-12-14T10:42:37Z | 2016-12-12T11:16:15Z |
CONTRIBUTOR
| null |
Rename _resample()_ `df` to `df_re` under **New syntax to window and resample operations** section.
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| 1 | 2016-12-12T10:21:00Z | 2017-03-24T20:14:03Z | 2016-12-12T11:10:35Z |
CONTRIBUTOR
| null |
- [ ] closes #14556
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
…o_datetime
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Series values cannot be accessed by numeric categorical index
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| 24 | 2016-12-12T11:12:32Z | 2021-05-02T00:58:24Z | null |
NONE
| null |
#### Code Sample
```python
>>> s1 = pd.Series(['a', 'b', 'c'], index=pd.Series([1, 2, 3]))
>>> s1.get(3)
'c'
>>> s2 = pd.Series(['a', 'b', 'c'], index=pd.Series([1, 2, 3], dtype='category'))
>>> s2.get(3) is None
True
>>> s2.get(0)
'a'
```
#### Problem description
In my opinion behavior in the second case can be error-prone (when there is an overlap between positional index and categorical one) and inconvenient (forces to use positional index).
#### Expected Output
```python
>>> s2.get(3)
'c'
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 27.2.0
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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on 32bit -- Series is int64 but cut produces int32 series
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| 6 | 2016-12-12T15:53:59Z | 2017-03-21T17:41:03Z | 2017-03-21T17:41:02Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
failing on 32bit test
```python
def test_single_bin(self):
# issue 14652
expected = Series([0, 0])
s = Series([9., 9.])
result = cut(s, 1, labels=False)
tm.assert_series_equal(result, expected)
s = Series([-9., -9.])
result = cut(s, 1, labels=False)
tm.assert_series_equal(result, expected)
```
#### Problem description
orig: https://buildd.debian.org/status/fetch.php?pkg=pandas&arch=i386&ver=0.19.1%2Bgit174-g81a2f79-1&stamp=1481436461 (still don't know about "bad argument to internal function" -- can't replicate locally, so ignore for this one):
```
======================================================================
FAIL: test_single_bin (pandas.tools.tests.test_tile.TestCut)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/«BUILDDIR»/pandas-0.19.1+git174-g81a2f79/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tools/tests/test_tile.py", line 281, in test_single_bin
tm.assert_series_equal(result, expected)
File "/«BUILDDIR»/pandas-0.19.1+git174-g81a2f79/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 1154, in assert_series_equal
assert_attr_equal('dtype', left, right)
File "/«BUILDDIR»/pandas-0.19.1+git174-g81a2f79/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 878, in assert_attr_equal
left_attr, right_attr)
File "/«BUILDDIR»/pandas-0.19.1+git174-g81a2f79/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 1018, in raise_assert_detail
raise AssertionError(msg)
AssertionError: Attributes are different
Attribute "dtype" are different
[left]: int32
[right]: int64
```
```
(Pdb) p expected
0 0
1 0
dtype: int64
(Pdb) p result
0 0
1 0
dtype: int32
(Pdb) p s
0 9.0
1 9.0
dtype: float64
```
so I guess if Series should use int64 regardless of underlying architecture default type, then cut should also produce int64, I guess.... or should Series have arch-default int??
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| 14,867 |
TST: `read_csv` with chunksize set returns empty `DataFrame` Instead of `TextFileReader`
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| 1 | 2016-12-12T20:24:01Z | 2016-12-20T16:03:24Z | 2016-12-20T16:03:24Z |
NONE
| null |
#### Code Sample
```python
import pandas as pd
# data.csv is an empty file
df = pd.read_csv('data.csv', chunksize=20, header=None, names=['a', 'b', 'c'])
```
#### Problem description
`read_csv` returns an empty DataFrame instead of a `pandas.io.parsers.TextFileReader` object when the above code is run. While this isn't a big issue, it is unexpected and the only way to tell the file is empty (through Pandas) is to check if a DataFrame was returned by `read_csv`. It would make more sense if `None` was returned instead, or the `TextFileReader`.
#### Expected Output
`pandas.io.parsers.TextFileReader` object
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-47-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 9.0.1
setuptools: 25.1.1
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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DOC: Update to docstring of pd.DataFrame(dtype)
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| 7 | 2016-12-12T20:45:59Z | 2017-05-24T21:40:36Z | 2017-02-27T17:15:05Z |
CONTRIBUTOR
| null |
- [ ] closes #14764
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
All suggestions to improve this are welcome.
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CustomBusinessMonthBegin offset parameter
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| 4 | 2016-12-12T22:09:36Z | 2021-10-31T02:16:24Z | 2021-10-31T02:16:24Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
from pandas.tseries.offsets import CustomBusinessMonthBegin
my_offset= CustomBusinessMonthBegin(offset=timedelta(days=5))
```
#### Problem description
when creating the offset instance with specific day offset, shows 'offset' is an invalid keyword.
#### Expected Output
Trying to use the CustomBusinessMonthBegin together with my customized calender and a day offset to get the N_th business day of a certain calender.
Return value should be a caloffset instance.
MonthBegin to get the first business day works well. What if I want to get the second/third/...n_th business day?
#### Output of ``pd.show_versions()``
0.18.0
<details>
# Paste the output here pd.show_versions() here
</details>
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| 195,106,821 |
MDU6SXNzdWUxOTUxMDY4MjE=
| 14,870 |
.plot() method breaks for two ostensibly identical time series DataFrames
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| 4 | 2016-12-12T22:39:04Z | 2017-01-05T22:19:20Z | 2017-01-05T22:13:41Z |
NONE
| null |
pd.__version__ == 0.19.0
I have two DataFrames which have the same index types, dtypes & columns. For some reason, the first DataFrame plots while the second does not. However, when I store them as .csv's and read them back in again, the problem is solved.
I suspect it has something to do with the columnar index or the DateTime index. Since saving them as .csv files wouldn't capture the issue I'm having, I saved them as .pkl files and zipped them below.
[dataframes.zip](https://github.com/pandas-dev/pandas/files/647451/dataframes.zip)
Finally, to replicate the issue, I ran `a.plot()` and `b.plot()`. Upon running `b.plot()` I get:
```
Traceback (most recent call last):
File "<ipython-input-507-904a8054cbcc>", line 1, in <module>
b.plot()
File "/.../site-packages/pandas/tools/plotting.py", line 3773, in __call__
sort_columns=sort_columns, **kwds)
File "/.../site-packages/pandas/tools/plotting.py", line 2642, in plot_frame
**kwds)
File "/.../site-packages/pandas/tools/plotting.py", line 2469, in _plot
plot_obj.generate()
File "/.../site-packages/pandas/tools/plotting.py", line 1043, in generate
self._make_plot()
File "/.../site-packages/pandas/tools/plotting.py", line 1698, in _make_plot
data = _maybe_convert_index(self._get_ax(0), self.data)
File "/.../site-packages/pandas/tseries/plotting.py", line 255, in _maybe_convert_index
data = data.to_period(freq=freq)
File "/.../site-packages/pandas/core/frame.py", line 5224, in to_period
new_data.set_axis(1, self.index.to_period(freq=freq))
File "/.../site-packages/pandas/tseries/index.py", line 928, in to_period
return PeriodIndex(self.values, name=self.name, freq=freq, tz=self.tz)
File "/.../site-packages/pandas/tseries/period.py", line 213, in __new__
ordinal, freq = cls._from_arraylike(data, freq, tz)
File "/.../site-packages/pandas/tseries/period.py", line 292, in _from_arraylike
data = dt64arr_to_periodarr(data, freq, tz)
File "/.../site-packages/pandas/tseries/period.py", line 62, in dt64arr_to_periodarr
base, mult = _gfc(freq)
File "/.../site-packages/pandas/tseries/frequencies.py", line 282, in get_freq_code
base, stride = _base_and_stride(freqstr)
File "/.../site-packages/pandas/tseries/frequencies.py", line 507, in _base_and_stride
groups = opattern.match(freqstr)
TypeError: expected string or buffer
```
Update: the issue seems to disappear when I reassign the index with `b.index = b.index.map(str)`; that is, I drop the datetime datatype.
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BUG: addresses #14855 by fixing color kwarg conflict
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| 17 | 2016-12-13T00:34:57Z | 2017-04-05T21:57:36Z | 2017-04-05T21:47:34Z |
CONTRIBUTOR
| null |
- [x] closes #14855
- [x] tests passed
- [x] passes ``git diff upstream/master | flake8 --diff``
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fillna() containing timezone aware datetime64 rounded
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| 1 | 2016-12-13T03:36:20Z | 2016-12-18T19:42:32Z | 2016-12-18T19:42:28Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import datetime, pytz
#Non-timezone aware datetime
s = pd.Series( [pd.NaT, pd.NaT, datetime.datetime(2016, 12, 12, 22, 24, 6, 100001) ] )
print(s)
0 NaT
1 NaT
2 2016-12-12 22:24:06.100001
dtype: datetime64[ns]
print(s.fillna(method='bfill'))
0 2016-12-12 22:24:06.100001
1 2016-12-12 22:24:06.100001
2 2016-12-12 22:24:06.100001
dtype: datetime64[ns]
# Timezone aware datetime
s = pd.Series( [pd.NaT, pd.NaT, datetime.datetime(2016, 12, 12, 22, 24, 6, 100001, tzinfo=pytz.utc) ] )
print(s)
0 NaT
1 NaT
2 2016-12-12 22:24:06.100001+00:00
print(s.fillna(method='bfill'))
0 2016-12-12 22:24:06.100001024+00:00
1 2016-12-12 22:24:06.100001024+00:00
2 2016-12-12 22:24:06.100001024+00:00
dtype: datetime64[ns, UTC]
```
#### Problem description
This is similar to bug #6587 but difference is a timezone aware datetime. It looks like the dtype is being stripped and the value is converted to a float and back.
#### Expected Output
```python
s = pd.Series( [pd.NaT, pd.NaT, datetime.datetime(2016, 12, 12, 22, 24, 6, 100001, tzinfo=pytz.utc) ] )
print(s)
0 NaT
1 NaT
2 2016-12-12 22:24:06.100001+00:00
print(s.fillna(method='bfill'))
0 2016-12-12 22:24:06.100001000+00:00
1 2016-12-12 22:24:06.100001000+00:00
2 2016-12-12 22:24:06.100001000+00:00
dtype: datetime64[ns, UTC]
```
#### Output of ``pd.show_versions()``
<details>
pandas: 0.19.1
numpy: 1.11.2
scipy: 0.18.1
pytz: 2016.7
</details>
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BUG: groupby.agg coercing booleans
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| 5 | 2016-12-13T09:19:59Z | 2019-02-19T14:11:42Z | 2019-02-19T14:11:42Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
dat = pd.DataFrame({'a': [1, 1, 2, 2], 'b': [0, 1, 2, 3], 'c': [None, None, 1, 1]})
gp = dat.groupby('a')
case_one = gp['b'].aggregate(lambda x: (x != 0).all())
case_two = gp['c'].aggregate(lambda x: x.isnull().all())
```
#### Problem description
By comparing ``case_one`` and ``case_two``, it seems that ``aggregate`` function turns boolean values sometimes to integers 1 or 0.
For ``case_one`` we get,
```
a
1 False
2 True
Name: b, dtype: bool
```
However for ``case_two`` we get
```
a
1 1.0
2 0.0
Name: c, dtype: float64
```
#### Expected Output
For ``case_two`` we should get
```
a
1 True
2 False
Name: b, dtype: bool
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-53-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_AU.UTF-8
LOCALE: en_AU.UTF-8
pandas: 0.19.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: None
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
This same issue appears in a different environment as well.
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-53-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_AU.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 9.0.1
setuptools: 23.0.0
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.0.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.3
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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MDU6SXNzdWUxOTUyOTMzMjg=
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Reading bz2-compressed tables from URL fails with c-engine in Python 2
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| 0 | 2016-12-13T16:03:37Z | 2016-12-17T23:09:20Z | 2016-12-17T23:09:20Z |
CONTRIBUTOR
| null |
In https://github.com/pandas-dev/pandas/pull/14576, we consolidated compression code and introduced support for compressed URLs beyond just gzip. However, as described in https://github.com/pandas-dev/pandas/pull/14576#issuecomment-264963575, the c-engine cannot read bz2-compressed URLs in python 2. This error is coming from [`parser.pyx#L615-L621`](https://github.com/pandas-dev/pandas/blob/b3dd9bafbfcbac1a2b6ab756e588d6e167f90439/pandas/parser.pyx#L615-L621). There may be potential to entirely bypass this offending code as it appears to duplicate the functionality of `_get_handle`.
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Create tests for reading compressed tables from S3 URLs
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| 2 | 2016-12-13T16:08:23Z | 2020-05-22T03:15:28Z | null |
CONTRIBUTOR
| null |
In https://github.com/pandas-dev/pandas/pull/14576, we consolidated compression code and expanded support for compressed URLs. While we added tests for compressed URLs, we did not add tests for compressed S3 files. First, someone will have to upload the compressed tables to Amazon: see https://github.com/pandas-dev/pandas/pull/14587#issuecomment-264674413.
Also tagging https://github.com/pandas-dev/pandas/issues/13137, a pull request which may want to also add these tests.
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| 14,876 |
API: DataFrame.melt(), DataFrame.wide_to_long()
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[] | null | 4 | 2016-12-13T23:36:54Z | 2017-04-04T17:52:28Z | 2017-04-04T17:52:28Z |
CONTRIBUTOR
| null |
xref https://github.com/pandas-dev/pandas/pull/14779
So I think it might be nice to have these as convenience methods on DataFrame().
we can deprecate (``pd.melt`` & ``pd.wide_to_long``), though we *didn't* deprecate ``pd.pivot_table``, even though it exists on ``DataFrame``, so this can be a discussion point.
but if we do this, then maybe have a shorter / better name than ``wide_to_long``? (even though it is strictly speaking pretty informative).
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BUG: Respect value of raise_on_error
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| 2 | 2016-12-14T08:41:37Z | 2017-05-26T12:34:00Z | 2017-01-02T10:20:10Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
Pasteable snippet.
```python
import numpy as np
import pandas as pd
x = pd.DataFrame(data=[[1,2],[3,4]], columns=['A','B'])
x.astype({'C': np.int32}, raise_on_error=False)
```
Output
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/tomdr/VirtualEnvs/work-py3/lib/python3.5/site-packages/pandas/core/generic.py", line 3041, in astype
raise KeyError('Only a column name can be used for the '
KeyError: 'Only a column name can be used for the key in a dtype mappings argument.'
```
#### Problem description
The method argument `raise_on_error` of `DataFrame.astype` suggests there is the option to ignore errors caused by mismatch in column names. Currently, an exception is raised regardless of the value of this argument. This is fixed in this pull request.
Before: https://github.com/pandas-dev/pandas/blob/v0.19.1/pandas/core/generic.py#L3041
#### Expected Output
An exception should not be raised.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.1
nose: 1.3.7
pip: 9.0.1
setuptools: 21.0.0
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.0.0
sphinx: 1.4.8
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.14
pymysql: 0.7.9.None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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MDU6SXNzdWUxOTU1MzQyMjQ=
| 14,878 |
DEPR: change .astype(.., raise_on_error) -> errors
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| 5 | 2016-12-14T13:44:56Z | 2017-01-03T20:53:59Z | 2017-01-03T20:53:59Z |
CONTRIBUTOR
| null |
xref #14877
this is inconsistent with our current API handling
so need to deprecate ``raise_on_error``, AFAICT, this is the only public use, and add
``errors='raise'|'ignore'``, with a default a ``raise``.
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CLN: remove need for *VectorData c-structures in hashtable.pyx
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| 1 | 2016-12-14T16:20:54Z | 2016-12-15T11:20:02Z | 2016-12-15T11:20:02Z |
CONTRIBUTOR
| null |
post #14859
See what I did for StringHashTable [here](https://github.com/pandas-dev/pandas/pull/14859/files#diff-086515be6caf140878bb9189b088525aR547)
I think we should just remove entirely the *VectorData structures and simply create them in a single pass at the end.
I don't think this will actually have a perf penalty, but should see if anything changes.
This will make the code easier to understand and still provide all of the GIL release benefits (because we actually need to hold the GIL to resize), if we use the method above, then can simply allocate the unique array all at once, then fill it with a single expression that doesn't require resizing, because we know the nuniques at this point.
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DOC for refactored compression (GH14576) + BUG: bz2-compressed URL with C engine (GH14874)
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| 9 | 2016-12-14T16:45:26Z | 2016-12-17T23:09:29Z | 2016-12-17T23:09:20Z |
CONTRIBUTOR
| null |
Follow up on https://github.com/pandas-dev/pandas/pull/14576, which refactored compression code to expand URL support.
Fixes up some small remaining issues and adds a what's new entry.
- [x] Closes #14874
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DataFrame constructor converts uint64 series to object series
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| 1 | 2016-12-14T18:03:12Z | 2016-12-19T16:59:50Z | 2016-12-19T16:59:50Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
uint64s = pd.Series([9710005220884355087, 9710005220138399309], dtype=np.dtype('uint64'))
uint64s.dtype # dtype('uint64')
df = pd.DataFrame({'viadict': uint64s})
df['viacolumn'] = uint64s
df.info()
# Data columns (total 2 columns):
# viadict 2 non-null object
# viacolumn 2 non-null uint64
```
#### Problem description
It seems like these ways of adding columns to a dataframe should have the same behavior.
#### Expected Output
```python
df.info()
# Data columns (total 2 columns):
# viadict 2 non-null uint64
# viacolumn 2 non-null uint64
```
(i.e. both columns are uint64s)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 45 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: None
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: None
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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| 195,621,245 |
MDU6SXNzdWUxOTU2MjEyNDU=
| 14,882 |
Incorrect index label displayed on MultiIndex DataFrame
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| 5 | 2016-12-14T19:21:21Z | 2017-01-21T23:07:44Z | 2017-01-21T23:07:44Z |
NONE
| null |
I have a DataFrame with a hierarchical index as follows:
```python
MultiIndex(levels=[[30, 40, 50], [6, 12, 24], ['MRIgFUS', 'ablation', 'hysterectomy', 'iud', 'myomectomy', 'none', 'uae']],
labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2], [0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6]],
names=['age', 'followup', 'next intervention'])
```
Notice in particular the values of the first two levels, and that they are balanced in the labels. However, the display of the table is incorrect. Here is what it looks like:
Notice the second label of the first two levels of the hierarchical index are repeated (40 and 40 instead of 40 and 50, and 12 and 12 instead of 12 and 24).
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 31.0.1
Cython: 0.25.2
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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MDU6SXNzdWUxOTU2NDk3Njc=
| 14,883 |
ENH: json_normalize should allow a different separator than .
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| 5 | 2016-12-14T21:24:36Z | 2017-03-28T21:50:38Z | 2017-03-28T21:50:37Z |
NONE
| null |
```python
>>> import pandas
>>> col_in = ['c1', 'c2.x']
>>> df = pandas.DataFrame([['A', 0], ['B', 1]], columns=col_in)
>>> df.c1
0 A
1 B
Name: c1, dtype: object
>>> df.c2.x
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/generic.py", line 2744, in __getattr__
return object.__getattribute__(self, name)
AttributeError: 'DataFrame' object has no attribute 'c2'
```
#### Problem description
The above snippet shows that it's not ideal to have `.` as a character in a column name. (I'm running into this when using Vega for data visualization, https://github.com/vega/vega-lite/issues/1775.) When `json_normalize` flattens a nested input JSON, it separates the nesting levels with a `.`. I believe this happens on this line:
https://github.com/pandas-dev/pandas/blob/7d8bc0deaeb8237a0cf361048363c78f4867f218/pandas/io/json.py#L831
I'd like to see an additional argument to `json_normalize`, `separator`, with default `.`, that specified the character (string) that separated nesting levels. In the line of code above, `'.'.join(val)` would be replaced by `separator.join(val)` (if I'm reading what that line does correctly). I could use, say, `_` to use underscore instead of period.
n00b at pandas, please correct me if I'm doing anything wrong.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
>>> pandas.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 16.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.US-ASCII
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.7
pip: 9.0.1
setuptools: 30.3.0
Cython: 0.25.2
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: 3.6.0
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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| 14,884 |
TST: Test datetime array assignment with different units (#7492)
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| 3 | 2016-12-15T06:06:56Z | 2016-12-17T23:13:52Z | 2016-12-17T23:13:43Z |
MEMBER
| null |
- [x] closes #7492
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
The example in this issue currently works in master. Added a test to confirm. Based on a quick skim of the recent PRs, doesn't look like it was fixed recently.
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Wrong "Too many indexers" error message when indexing a Series with MultiIndex
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| 4 | 2016-12-15T10:02:04Z | 2019-04-26T01:12:43Z | 2019-04-26T01:12:43Z |
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [2]: s = pd.Series(range(4), index=pd.MultiIndex.from_product([['a', 'b'], ['c', 'd']]))
In [3]: s.loc['a', 'e']
---------------------------------------------------------------------------
IndexingError Traceback (most recent call last)
<ipython-input-3-042ed7ab0463> in <module>()
----> 1 s.loc['a', 'e']
/home/pietro/nobackup/repo/pandas/pandas/core/indexing.py in __getitem__(self, key)
1308
1309 if type(key) is tuple:
-> 1310 return self._getitem_tuple(key)
1311 else:
1312 return self._getitem_axis(key, axis=0)
/home/pietro/nobackup/repo/pandas/pandas/core/indexing.py in _getitem_tuple(self, tup)
799
800 # no multi-index, so validate all of the indexers
--> 801 self._has_valid_tuple(tup)
802
803 # ugly hack for GH #836
/home/pietro/nobackup/repo/pandas/pandas/core/indexing.py in _has_valid_tuple(self, key)
148 for i, k in enumerate(key):
149 if i >= self.obj.ndim:
--> 150 raise IndexingError('Too many indexers')
151 if not self._has_valid_type(k, i):
152 raise ValueError("Location based indexing can only have [%s] "
IndexingError: Too many indexers
```
#### Problem description
The raised error, and the message, seem wrong to me.
#### Expected Output
```
KeyError: ('a', 'e')
```
This is what I get if I I take a ``DataFrame`` with the same index and do
```
In [7]: df.loc[('a', 'e'), :]
```
By the way, ``s.loc['a', 'b']`` (valid key) works just fine, so this is clearly a problem of missing key, and [the docs](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.loc.html) say "``.loc`` will raise a ``KeyError`` when the items are not found."
... and by the way, I would expect _the following_ to raise an ``IndexingError: Too many indexers``:
```
In [12]: s.loc[('a', 'e'), :]
Out[12]:
a c 0
d 1
dtype: int64
```
... instead the tuple is interpreted as a list of labels rather than as a key, and hence it works "fine". Is this behavior desired? (looks a bit inconsistent to me, but I see that it is generalized, i.e. ``DataFrame``s also work this way) If it is, then it is worth fixing the docs where [they mention](url) "A list or array of labels, e.g. ['a', 'b', 'c']." to also mention tuples.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.7.0-1-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
LOCALE: it_IT.UTF-8
pandas: 0.19.0+196.g5f889a2.dirty
nose: 1.3.7
pip: 8.1.2
setuptools: 28.0.0
Cython: 0.23.4
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.8.0.dev0+f80669e
xarray: None
IPython: 5.1.0.dev
sphinx: 1.4.8
patsy: 0.3.0-dev
dateutil: 2.5.3
pytz: 2015.7
blosc: None
bottleneck: 1.2.0dev
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.3
openpyxl: None
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: None
bs4: 4.5.1
html5lib: 0.999
httplib2: 0.9.1
apiclient: 1.5.2
sqlalchemy: 1.0.15
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: 0.2.1
</details>
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| 14,886 |
BUG: regression in DataFrame.combine_first with integer columns (GH14687)
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| 5 | 2016-12-15T10:53:24Z | 2016-12-16T09:43:42Z | 2016-12-16T09:43:42Z |
MEMBER
| null |
closes #14687
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Add "forward" and "nearest" direction to merge_asof()
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| 4 | 2016-12-15T16:05:23Z | 2017-10-06T14:45:08Z | 2017-01-18T16:08:10Z |
CONTRIBUTOR
| null |
Currently `pd.merge_asof()` looks _backwards_, meaning that it gets the last row from the right table whose timestamp is less the left table's timestamp. It might be nice to look _forwards_, meaning get the first row in the right table whose timestamps is greater than the left table's timestamp. And we could have a _nearest_, meaning get the row from the right table whose timestamp is closest to the left table's timestamp regardless of direction.
I propose a new `direction` parameter whose default value is `"backward"`, which looks for prior timestamps as `pd.merge_asof()` currently does. A value of `"forward"` will cause cause the function to look for subsequent timestamps. A value of `"nearest"` will look on both directions.
Here's a modified example from the docstring:
```python
In [16]: left
Out[16]:
a left_val
0 1 a
1 5 b
2 10 c
In [17]: right
Out[17]:
a right_val
0 1 1
1 2 2
2 3 3
3 6 6
4 7 7
In [18]: pd.merge_asof(left, right, on='a')
Out[18]:
a left_val right_val
0 1 a 1
1 5 b 3
2 10 c 7
In [19]: pd.merge_asof(left, right, on='a', direction='forward')
Out[19]:
a left_val right_val
0 1 a 1.0
1 5 b 6.0
2 10 c NaN
In [20]: pd.merge_asof(left, right, on='a', direction='nearest')
Out[20]:
a left_val right_val
0 1 a 1
1 5 b 6
2 10 c 7
```
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Odd behavior with .resample('SM')
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| 4 | 2016-12-15T17:27:41Z | 2017-10-23T08:59:04Z | 2017-10-23T08:59:04Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
from pandas import Timestamp
example = pd.DataFrame({'aux': {0: Timestamp('2000-01-03 00:00:00'),
1: Timestamp('2000-01-04 00:00:00'), 2: Timestamp('2000-01-05 00:00:00'),
3: Timestamp('2000-01-06 00:00:00'), 4: Timestamp('2000-01-07 00:00:00'),
5: Timestamp('2000-01-10 00:00:00'), 6: Timestamp('2000-01-11 00:00:00'),
7: Timestamp('2000-01-12 00:00:00'), 8: Timestamp('2000-01-13 00:00:00'),
9: Timestamp('2000-01-14 00:00:00'), 10: Timestamp('2000-01-17 00:00:00'),
11: Timestamp('2000-01-18 00:00:00'), 12: Timestamp('2000-01-19 00:00:00'),
13: Timestamp('2000-01-20 00:00:00'), 14: Timestamp('2000-01-21 00:00:00'),
15: Timestamp('2000-01-24 00:00:00'), 16: Timestamp('2000-01-26 00:00:00'),
17: Timestamp('2000-01-27 00:00:00'), 18: Timestamp('2000-01-28 00:00:00'),
19: Timestamp('2000-01-31 00:00:00'), 20: Timestamp('2000-02-01 00:00:00'),
21: Timestamp('2000-02-02 00:00:00'), 22: Timestamp('2000-02-03 00:00:00'),
23: Timestamp('2000-02-04 00:00:00'), 24: Timestamp('2000-02-07 00:00:00'),
25: Timestamp('2000-02-08 00:00:00'), 26: Timestamp('2000-02-09 00:00:00'),
27: Timestamp('2000-02-10 00:00:00'), 28: Timestamp('2000-02-11 00:00:00'),
29: Timestamp('2000-02-14 00:00:00'), 30: Timestamp('2000-02-15 00:00:00'),
31: Timestamp('2000-02-16 00:00:00'), 32: Timestamp('2000-02-17 00:00:00'),
33: Timestamp('2000-02-18 00:00:00'), 34: Timestamp('2000-02-21 00:00:00'),
35: Timestamp('2000-02-22 00:00:00'), 36: Timestamp('2000-02-23 00:00:00'),
37: Timestamp('2000-02-24 00:00:00'), 38: Timestamp('2000-02-25 00:00:00'),
39: Timestamp('2000-02-28 00:00:00'), 40: Timestamp('2000-02-29 00:00:00'),
41: Timestamp('2000-03-01 00:00:00'), 42: Timestamp('2000-03-02 00:00:00'),
43: Timestamp('2000-03-03 00:00:00'), 44: Timestamp('2000-03-08 00:00:00'),
45: Timestamp('2000-03-09 00:00:00'), 46: Timestamp('2000-03-10 00:00:00'),
47: Timestamp('2000-03-13 00:00:00'), 48: Timestamp('2000-03-14 00:00:00'),
49: Timestamp('2000-03-15 00:00:00'), 50: Timestamp('2000-03-16 00:00:00'),
51: Timestamp('2000-03-17 00:00:00'), 52: Timestamp('2000-03-20 00:00:00'),
53: Timestamp('2000-03-21 00:00:00'), 54: Timestamp('2000-03-22 00:00:00'),
55: Timestamp('2000-03-23 00:00:00'), 56: Timestamp('2000-03-24 00:00:00'),
57: Timestamp('2000-03-27 00:00:00'), 58: Timestamp('2000-03-28 00:00:00'),
59: Timestamp('2000-03-29 00:00:00'), 60: Timestamp('2000-03-30 00:00:00'),
61: Timestamp('2000-03-31 00:00:00')}, u'date': {0: Timestamp('2000-01-03 00:00:00'),
1: Timestamp('2000-01-04 00:00:00'), 2: Timestamp('2000-01-05 00:00:00'),
3: Timestamp('2000-01-06 00:00:00'), 4: Timestamp('2000-01-07 00:00:00'),
5: Timestamp('2000-01-10 00:00:00'), 6: Timestamp('2000-01-11 00:00:00'),
7: Timestamp('2000-01-12 00:00:00'), 8: Timestamp('2000-01-13 00:00:00'),
9: Timestamp('2000-01-14 00:00:00'), 10: Timestamp('2000-01-17 00:00:00'),
11: Timestamp('2000-01-18 00:00:00'), 12: Timestamp('2000-01-19 00:00:00'),
13: Timestamp('2000-01-20 00:00:00'), 14: Timestamp('2000-01-21 00:00:00'),
15: Timestamp('2000-01-24 00:00:00'), 16: Timestamp('2000-01-26 00:00:00'),
17: Timestamp('2000-01-27 00:00:00'), 18: Timestamp('2000-01-28 00:00:00'),
19: Timestamp('2000-01-31 00:00:00'), 20: Timestamp('2000-02-01 00:00:00'),
21: Timestamp('2000-02-02 00:00:00'), 22: Timestamp('2000-02-03 00:00:00'),
23: Timestamp('2000-02-04 00:00:00'), 24: Timestamp('2000-02-07 00:00:00'),
25: Timestamp('2000-02-08 00:00:00'), 26: Timestamp('2000-02-09 00:00:00'),
27: Timestamp('2000-02-10 00:00:00'), 28: Timestamp('2000-02-11 00:00:00'),
29: Timestamp('2000-02-14 00:00:00'), 30: Timestamp('2000-02-15 00:00:00'),
31: Timestamp('2000-02-16 00:00:00'), 32: Timestamp('2000-02-17 00:00:00'),
33: Timestamp('2000-02-18 00:00:00'), 34: Timestamp('2000-02-21 00:00:00'),
35: Timestamp('2000-02-22 00:00:00'), 36: Timestamp('2000-02-23 00:00:00'),
37: Timestamp('2000-02-24 00:00:00'), 38: Timestamp('2000-02-25 00:00:00'),
39: Timestamp('2000-02-28 00:00:00'), 40: Timestamp('2000-02-29 00:00:00'),
41: Timestamp('2000-03-01 00:00:00'), 42: Timestamp('2000-03-02 00:00:00'),
43: Timestamp('2000-03-03 00:00:00'), 44: Timestamp('2000-03-08 00:00:00'),
45: Timestamp('2000-03-09 00:00:00'), 46: Timestamp('2000-03-10 00:00:00'),
47: Timestamp('2000-03-13 00:00:00'), 48: Timestamp('2000-03-14 00:00:00'),
49: Timestamp('2000-03-15 00:00:00'), 50: Timestamp('2000-03-16 00:00:00'),
51: Timestamp('2000-03-17 00:00:00'), 52: Timestamp('2000-03-20 00:00:00'),
53: Timestamp('2000-03-21 00:00:00'), 54: Timestamp('2000-03-22 00:00:00'),
55: Timestamp('2000-03-23 00:00:00'), 56: Timestamp('2000-03-24 00:00:00'),
57: Timestamp('2000-03-27 00:00:00'), 58: Timestamp('2000-03-28 00:00:00'),
59: Timestamp('2000-03-29 00:00:00'), 60: Timestamp('2000-03-30 00:00:00'),
61: Timestamp('2000-03-31 00:00:00')}})
print example.set_index('date').resample('SM').last()
print example.set_index('date').resample('M').last()
print example.set_index('date').resample('W').last()
```
#### Problem description
When resampling with 'SM' the values in the 'aux' column have ocurred after or at the resampling date, whereas with 'M' or 'W' (or 'Q', or 'A'...) the values in the 'aux' columns have ocurred before or at the resampling date.
#### Expected Output
The expected output form the 'SM' resample would be:
```
aux
date
2000-01-15 2000-01-14
2000-01-31 2000-01-31
2000-02-15 2000-02-15
2000-02-29 2000-02-29
2000-03-15 2000-03-15
2000-03-31 2000-03-31
```
#### Output of ``pd.show_versions()``
<details>
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 23 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.7
pip: 9.0.1
setuptools: 20.3
Cython: 0.22.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.6.1
xarray: None
IPython: 4.0.0
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: 1.0.0
tables: 3.2.0
numexpr: 2.5.2
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: 1.0.8
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: None
</details>
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MDU6SXNzdWUxOTU5MTA0NTY=
| 14,889 |
Documentation about HDFStore.select() with stop argument is not clear
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| 3 | 2016-12-15T20:40:23Z | 2016-12-16T11:14:35Z | null |
NONE
| null |
After finding a strange behavior in my code, I found out that when using the `stop` parameter in `pandas.HDFStore.select()` of in `pandas.read_hdf()`, the where conditions are applied AFTER the limitation of the output and not before, as I would have expected.
I think the documentation of these functions could be a little more clear about the order of operations:
- first, data query (file+path in store) with optional limitation by start and stop
- then, application of the where argument
The documentation could contain something
> 'The row limitation with start and stop limit the result of the table query, before the where arguments is applied to the dataframe'
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| 3 | 2016-12-15T21:40:07Z | 2016-12-16T11:11:05Z | 2016-12-16T11:11:01Z |
MEMBER
| null |
1) Minor spelling and command errors.
2) Add that we also use AppVeyor for testing.
3) With the introduction of `cpplint`, document it and how to use.
Follow-up to #14814.
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Add new optional "separator" argument to json_normalize
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[] | null | 3 | 2016-12-16T00:23:59Z | 2016-12-22T00:06:35Z | 2016-12-22T00:06:35Z |
NONE
| null |
- [x] closes #14883
- [x] tests added / passed (added test `test_shallow_nested_with_separator`)
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry (v0.20.0)
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| 14,892 |
DOC: fixed typo
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| 2 | 2016-12-16T02:17:03Z | 2016-12-16T11:25:47Z | 2016-12-16T09:18:26Z |
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
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TST: to_json keeps column info with empty dataframe (#7445)
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| 5 | 2016-12-16T05:21:07Z | 2017-12-20T01:58:29Z | 2016-12-16T23:21:07Z |
MEMBER
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- [x] closes #7445
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
Closed by #9805
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Improve the performance of instantiating a Series object with dictionary data and a datetimeindex
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| 0 | 2016-12-16T06:28:25Z | 2016-12-17T23:32:00Z | 2016-12-17T23:32:00Z |
CONTRIBUTOR
| null |
The current code path always results in an exception on:
https://github.com/pandas-dev/pandas/blob/3ac41ab2d7ae446fa04f47ec911003cd722dbd65/pandas/core/series.py#L191
which is then caught. Only a slight performance advantage is seen but hopefully the code change makes it less confusing for newcomers like me.
#### Code Sample, a copy-pastable example if possible
```python
dr = pd.date_range(
start=datetime(2015, 10, 26),
end=datetime(2016, 1, 1),
freq='10s'
)
data = {d: v for d, v in zip(dr, range(len(dr)))}
s = Series(data=data, index=dr)
```
#### Problem description
The current code path always results in an exception on:
https://github.com/pandas-dev/pandas/blob/3ac41ab2d7ae446fa04f47ec911003cd722dbd65/pandas/core/series.py#L191
which is then caught. Only a slight performance advantage is seen but hopefully the code change makes it less confusing for newcomers like me.
ASV output of new benchmark
Running 2 total benchmarks (2 commits * 1 environments * 1 benchmarks)
[ 0.00%] · For pandas commit hash 5f05fdcf:
[ 0.00%] ·· Building for conda-py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt.................................
[ 0.00%] ·· Benchmarking conda-py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[ 50.00%] ··· Running ...x.time_series_constructor_no_data_datetime_index 3.26s
[ 50.00%] · For pandas commit hash 3ba2cff9:
[ 50.00%] ·· Building for conda-py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt...
[ 50.00%] ·· Benchmarking conda-py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[100.00%] ··· Running ...x.time_series_constructor_no_data_datetime_index 3.77s before after ratio
[3ba2cff9] [5f05fdcf]
- 3.77s 3.26s 0.87 series_methods.series_constructor_dict_data_datetime_index.time_series_constructor_no_data_datetime_index
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Clean up construction of Series with dictionary and datetime index
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| 5 | 2016-12-16T06:31:57Z | 2016-12-17T23:32:06Z | 2016-12-17T23:32:00Z |
CONTRIBUTOR
| null |
- [X] closes #14894
- [X] tests added / passed
- [X] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
Fix usage of fast_multiget with index which was always throwing an exception that was then caught; add ASV that show slight improvement
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MDU6SXNzdWUxOTYwMjk2MjE=
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ENH: add errors=ignore|raise kw to DataFrame.rename
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| 2 | 2016-12-16T10:40:18Z | 2016-12-16T13:49:10Z | 2016-12-16T13:49:01Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas
df = pandas.DataFrame({'a': [1,2], 'b': [3,4]})
df.rename({'C':'D'})
```
#### Problem description
I expected the method to throw an error, complaining that the key passed via
the dictionary isnt a valid column name in the DataFrame. Instead, nothing happens.
#### Expected Output
some sort of error.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 16.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.0
nose: None
pip: 9.0.1
setuptools: 23.1.0
Cython: None
numpy: 1.10.4
scipy: None
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: None
patsy: None
dateutil: 2.5.2
pytz: 2016.3
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: 2.8
boto: None
pandas_datareader: None
</details>
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MDU6SXNzdWUxOTYwMzQyNjI=
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DOC : `build` isnt a valid argument to doc/make.py
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| 1 | 2016-12-16T11:03:22Z | 2016-12-16T11:11:32Z | 2016-12-16T11:11:22Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```
python make.py build
```
#### Problem description
the docs - http://pandas.pydata.org/pandas-docs/stable/contributing.html#building-the-documentation mention that in order to do a full clean build, we will have to do
```
python make.py clean
python.make.py build
```
but the `build` option doesn't seem to be supported, because running the command
raises the error
`Do not know how to handle build; valid args are ['all', 'latex', 'zip_html', 'latex_forced', 'upload_stable', 'build_pandas', 'upload_dev', 'upload_stable_pdf', 'upload_dev_pdf', 'auto_debug', 'html', 'clean', 'auto_dev']`
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 16.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.0
nose: None
pip: 9.0.1
setuptools: 23.1.0
Cython: None
numpy: 1.10.4
scipy: None
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: 1.4.1
patsy: None
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
None
</details>
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DOC : setting dialect on pandas.read_table overrides a few other kwds
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| 5 | 2016-12-16T11:54:04Z | 2016-12-31T15:55:21Z | 2016-12-31T15:55:21Z |
NONE
| null |
#### Problem description
Correct me if I'm wrong but [TextFileReader](https://github.com/pandas-dev/pandas/blob/master/pandas/io/parsers.py#L691-L700) overwrites the `delimiter`, `quoting` etc kwds if the `dialect` kwarg is passed. But this isn't highlighted in the `pandas.read_table` documentation.
I don't know if this was implicit. Sorry for the noise if this is a known issue or if it isn't actually one.
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MDExOlB1bGxSZXF1ZXN0OTgzMzU1MzU=
| 14,899 |
BLD: swap 3.6-dev and 3.4 builds, reorg build order
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| 0 | 2016-12-16T13:35:42Z | 2016-12-16T13:35:47Z | 2016-12-16T13:35:47Z |
CONTRIBUTOR
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Wrong slicing behavior for DataFrame loc
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| 5 | 2016-12-17T00:04:14Z | 2016-12-17T00:43:43Z | 2016-12-17T00:10:25Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> df
0 1 2 3
0 1 21 51 61
1 2 22 52 62
2 3 23 53 63
>>> df[0:0]
Empty DataFrame
Columns: [0, 1, 2, 3]
Index: []
>>> df.loc[0:0]
0 1 2 3
0 1 21 51 61
```
#### For loc, slicing is incorrect
The behavior exhibited by slicing of loc is incosistent with python array slicing.
For [0:0] it should have returned empty, but it is returning a row.
#### Expected Output
Similar to code show below for python array, pd.DataFrame.loc slicing should produce empty
E.g. Following code slices empty for [0:0]
```python
>>> l = [0,1,2]
>>> l[0:0]
[]
```
#### Output of ``pd.show_versions()``
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 25.2.0
Cython: None
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.2
pytz: 2016.3
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: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
</details>
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BUG: Categorical Series breaks when re-serializing with msgpack.
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| 1 | 2016-12-17T15:00:12Z | 2016-12-21T11:28:03Z | 2016-12-21T11:28:03Z |
CONTRIBUTOR
| null |
xref #8732
```
df=pd.DataFrame([[1,'John P. Doe'],[2,'Jane Dove'],[1,'John P. Doe']])
df[1]=pd.Categorical(df[1])
```
DataFrames work fine
```
In [9]: pd.read_msgpack(df.to_msgpack()).dtypes
Out[9]:
0 int64
1 category
dtype: object
```
Series are breaking on re-serialization
```
In [10]: pd.read_msgpack(df[1].to_msgpack())
ValueError: cannot specify a dtype with a Categorical unless dtype='category'
```
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Catch warning introduced by GH14432 in test case
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| 4 | 2016-12-17T15:47:33Z | 2016-12-17T23:04:24Z | 2016-12-17T23:03:34Z |
CONTRIBUTOR
| null |
Follow on to #14432 to catch the newly introduced `FutureWarning` in the `test_groupby_multi_categorical_as_index` test case.
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MDU6SXNzdWUxOTYyMzQ3NDM=
| 14,903 |
DataFrame.to_xarray() is sorting dimensional coordinates
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| 3 | 2016-12-17T18:07:18Z | 2016-12-17T23:33:30Z | 2016-12-17T23:33:19Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
rd = np.zeros((4,4), dtype=np.float64)
lag = np.arange(4)[::-1]
# lag = np.random.randn(4)
lev = np.arange(4)
iterables = [lag, lev]
index = pd.MultiIndex.from_product(iterables, names=['lag','lev'])
solution = pd.DataFrame(data=rd.reshape(rd.size, 1),index=index)
solution.columns = ['tmp']
solution.to_xarray()
```
Output:
```
<xarray.Dataset>
Dimensions: (lag: 4, lev: 4)
Coordinates:
* lag (lag) int64 0 1 2 3
* lev (lev) int64 0 1 2 3
Data variables:
tmp (lag, lev) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
```
#### Problem description
The dimensions of the generated `xarray.Dataset` is sorted in ascending order for `lag`.
This is true for any entry (like the commented out `np.random.randn(4)` line)
#### Expected Output
```
<xarray.Dataset>
Dimensions: (lag: 4, lev: 4)
Coordinates:
* lag (lag) int64 3 2 1 0
* lev (lev) int64 0 1 2 3
Data variables:
tmp (lag, lev) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.5.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-327.22.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 30.2.0
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: 0.8.2
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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ENH: Added to_json_schema
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| 77 | 2016-12-17T18:20:08Z | 2017-04-05T02:07:22Z | 2017-03-04T11:50:05Z |
CONTRIBUTOR
| null |
Lays the groundwork for (but doesn't close) https://github.com/pandas-dev/pandas/issues/14386
This handles the schema part of the request there. We'll still need to
do the work to publish the data to the frontend, but that can be done
as a followup.
Usage:
```python
In [4]: df = pd.DataFrame(
...: {'A': [1, 2, 3],
...: 'B': ['a', 'b', 'c'],
...: 'C': pd.date_range('2016-01-01', freq='d', periods=3),
...: }, index=pd.Index(range(3), name='idx'))
...: df
...:
Out[4]:
A B C
idx
0 1 a 2016-01-01
1 2 b 2016-01-02
2 3 c 2016-01-03
In [5]:
In [5]: pd.to_json_schema(df)
Out[5]:
{'fields': [{'name': 'idx', 'type': 'integer'},
{'name': 'A', 'type': 'integer'},
{'name': 'B', 'type': 'string'},
{'name': 'C', 'type': 'date'}],
'primary_key': 'idx'}
```
---
I think this is useful enough on its own to be part of the public API, so I've documented as such.
I've included a placeholder `publish_tableschema` that will *not* be included in the final commit.
It's just to make @rgbkrk's life easier for prototyping the nteract frontend. I think the proper solution for publishing the schema + data will have to wait on https://github.com/ipython/ipython/issues/10090
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| 14,905 |
PR for Pandas issue #14872 / fillna() TZ datetime64 rounded
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| 7 | 2016-12-17T20:43:16Z | 2016-12-18T19:42:38Z | 2016-12-18T19:42:28Z |
NONE
| null |
Hi Jeff,
The change adds a "datetime64[ns, UTC]" object to the _DATELIKE_DTYPES set in pandas/types/common.py.
No such object existed in this set and that is the reason the wrong method was executed on the inputs.
Object is created with "DatetimeTZDtype.__new__"
Thanks,
Rodolfo
- [X] closes #14872
- [ X] tests added / passed
- [ X] passes ``git diff upstream/master | flake8 --diff`` (returns the same number of skips, errors and failures before changes)
- [ ] whatsnew entry
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TST: Test timedelta arithmetic (#9396)
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| 2 | 2016-12-18T05:12:17Z | 2016-12-18T20:22:30Z | 2016-12-18T20:22:25Z |
MEMBER
| null |
- [x] closes #9396
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
Original issue was with using `div` with `datetime.timedelta`. Doesn't appear this was fixed recently.
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| 196,262,094 |
MDExOlB1bGxSZXF1ZXN0OTg0NjY4NTY=
| 14,907 |
TST: Groupby/transform with grouped NaN (#9941)
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| 2 | 2016-12-18T07:12:07Z | 2016-12-18T20:24:15Z | 2016-12-18T20:24:12Z |
MEMBER
| null |
- [x] closes #9941
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
The original issue works on 0.19.1 as expected (resulting value is `nan`). Doesn't look like this was fixed in a PR for 0.19.2 or 0.20
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