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MDExOlB1bGxSZXF1ZXN0MTExMTkzOTIy
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TST: remove rest of yield warnings
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| 2 | 2017-03-17T00:41:09Z | 2017-03-17T15:03:20Z | 2017-03-17T14:08:49Z |
CONTRIBUTOR
| null |
xref https://github.com/pandas-dev/pandas/issues/15341
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TST: Reorganize package_check and skip_if_no_package
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| 2 | 2017-03-17T00:51:16Z | 2017-03-17T13:11:46Z | 2017-03-17T12:50:27Z |
MEMBER
| null |
`skip_if_no_package` literally just calls `package_check` with no additional decorations and with confusing `*args` and `**kwargs`. However, `skip_if_no_package` is a better name than `package_check`.
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MDExOlB1bGxSZXF1ZXN0MTExMTk0ODk2
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MAINT: Drop take_last kwarg from method signatures
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| 7 | 2017-03-17T00:52:22Z | 2017-03-18T01:27:14Z | 2017-03-18T00:13:21Z |
MEMBER
| null |
Affected methods:
1) `nlargest`
2) `nsmallest`
3) `duplicated`
4) `drop_duplicates`
xref #10236, #10792, #10920.
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BUG: TZ-aware Series.where() appropriately handles default other=nan (#15701)
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| 1 | 2017-03-17T04:07:18Z | 2017-03-17T13:23:43Z | 2017-03-17T13:11:05Z |
CONTRIBUTOR
| null |
- [x] closes #15701
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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| 214,911,451 |
MDExOlB1bGxSZXF1ZXN0MTExMjE2MzU2
| 15,712 |
DOC: Add gotcha about flake8-ing diff
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| 1 | 2017-03-17T05:29:23Z | 2017-03-17T13:11:44Z | 2017-03-17T12:51:41Z |
MEMBER
| null |
The `flake8`-ing the diff will not catch any import style errors. I put an alternative check that is more comprehensive but will take longer to run since you will be checking entire files instead of the diff.
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| 214,955,698 |
MDU6SXNzdWUyMTQ5NTU2OTg=
| 15,713 |
BUG: Series.asof fails when series is all nans
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| 3 | 2017-03-17T09:53:56Z | 2017-03-29T09:27:17Z | 2017-03-29T00:46:47Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> import numpy as np
>>> pd.Series([np.nan]).asof([0])
Traceback (most recent call last):
File "<ipython-input-268-2b0f8084a393>", line 1, in <module>
pd.Series([np.nan]).asof([0])
File "~/anaconda3/lib/python3.5/site-packages/pandas/core/generic.py", line 3985, in asof
locs = self.index.asof_locs(where, ~(nulls.values))
File "~/anaconda3/lib/python3.5/site-packages/pandas/indexes/base.py", line 1837, in asof_locs
result = np.arange(len(self))[mask].take(locs)
IndexError: cannot do a non-empty take from an empty axes.
```
#### Problem description
When calling `Series.asof()`, if the series happens to contain all nans, an error is raised. See #10343.
#### Expected Output
```python
0 NaN
dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.8-100.fc24.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: C
LOCALE: None.None
pandas: 0.19.0+579.g4ce9c0c
pytest: 3.0.5
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
xarray: 0.9.1
IPython: 4.2.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.2
feather: None
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: 0.999
sqlalchemy: 1.1.5
pymysql: None
psycopg2: None
jinja2: 2.9.4
s3fs: None
pandas_gbq: None
pandas_datareader: None
</details>
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| 214,961,131 |
MDU6SXNzdWUyMTQ5NjExMzE=
| 15,714 |
Mode should be defined if the Series consist of only one object
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| 2 | 2017-03-17T10:17:02Z | 2017-04-12T10:29:20Z | 2017-04-12T10:29:20Z |
CONTRIBUTOR
| null |
#### Code Sample
```python
>>> import pandas.core.algorithms as algos
>>> algos.mode([5])
Series([], dtype: int64)
```
#### Problem description
Currently the `mode` of a series is not defined if the series consist of only one object. This limits the use of the mode as an aggregation method.
Compare this with the behaviour of other libraries, e.g. `scipy`:
```python
>>> from scipy import stats
>>> scipy.stats.mode([5])
(array([ 5.]), array([ 1.]))
```
#### Expected Output
```python
0 5
dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 34.3.1
Cython: None
numpy: 1.8.0rc1
scipy: 0.13.0b1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.4.8
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
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xlrd: 1.0.0
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xlsxwriter: 0.9.3
lxml: 3.7.3
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.6
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.9.5
boto: 2.42.0
pandas_datareader: None
</details>
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MDU6SXNzdWUyMTQ5Njg4NDY=
| 15,715 |
json encoding for python 2
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| 4 | 2017-03-17T10:50:27Z | 2018-04-04T13:41:48Z | 2018-04-04T13:41:47Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# not working
pd.read_json(path, encoding='cp1251')
# that works
import json
with open(path, 'r') as f:
js = json.load(f, encoding='cp1251')
pd.DataFrame(js)
```
#### Problem description
It is not mentioned explicitly in docstring that `encoding` option used in py3 only.
Currently `pd.read_json` mostly ignores `encoding=` option in python2.
Function `pd.common._get_handle` warns about using encoding with compression, but silently continues without actually using `encoding` otherwise.
It looks like subtasks are split in unfavourable way to pass encoding up to `json.loads` call.
#### Expected Output
One might expect pandas use encoding, to get life easier (as pandas usually do ;) ).
Or at least properly warn that option is ignored.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 28.8.0.post20161110
Cython: 0.24.1
numpy: 1.12.0
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: 1.0.0
tables: 3.2.2
numexpr: 2.6.2
matplotlib: 2.0.0
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.4
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: 0.7.6.None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
</details>
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| 214,974,927 |
MDU6SXNzdWUyMTQ5NzQ5Mjc=
| 15,716 |
to_json float precision bug
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| 4 | 2017-03-17T11:18:26Z | 2020-06-30T13:16:17Z | 2017-04-03T12:43:26Z |
NONE
| null |
xref #15864 for more tests
#### Code Sample, a copy-pastable example if possible
```python
>>> pd.DataFrame([dict(a_float=0.99999999999999944)]).to_json(double_precision=14)
'{"a_float":{"0":1.0}}'
>>> pd.DataFrame([dict(a_float=0.99999999999999944)]).to_json(double_precision=15)
'{"a_float":{"0":0.1}}'
```
#### Problem description
...
#### Expected Output
```python
'{"a_float":{"0":1.0}}'
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-66-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 34.3.2
Cython: 0.24.1
numpy: 1.12.0
scipy: 0.18.1
statsmodelsdouble_precision: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
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: 0.10.3
apiclient: 1.5.3
sqlalchemy: 1.1.2
pymysql: None
psycopg2: None
jinja2: 2.7.3
boto: 2.42.0
pandas_datareader: None
</details>
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MDU6SXNzdWUyMTUwMjA3ODM=
| 15,717 |
`date_range` with CustomBusinessDay receives TypeError
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| 6 | 2017-03-17T14:33:00Z | 2017-03-17T17:20:12Z | 2017-03-17T14:38:47Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import datetime
from pandas.tseries.offsets import CustomBusinessDay
from pandas.tseries.holiday import AbstractHolidayCalendar, Holiday, \
USMartinLutherKingJr, USPresidentsDay, USMemorialDay, USLaborDay, \
USThanksgivingDay, GoodFriday, nearest_workday
import pandas as pd
from pandas.tseries.offsets import BDay
class TradingCalendar(AbstractHolidayCalendar):
"""Calendar of trading holidays in the US"""
rules = [
Holiday('New Years Day', month=1, day=1, observance=nearest_workday),
USMartinLutherKingJr,
USPresidentsDay,
USMemorialDay,
Holiday('July 4th', month=7, day=4, observance=nearest_workday),
USLaborDay,
USThanksgivingDay,
GoodFriday,
Holiday('Christmas', month=12, day=25, observance=nearest_workday)
]
class TradingDay(CustomBusinessDay):
def __init__(self, n=1):
super(TradingDay, self).__init__(n, calendar=TradingCalendar())
r = pd.date_range(datetime.datetime(2017, 12, 26), datetime.datetime(2017, 1, 6), TradingDay(1))
len(r) == 9
```
#### Problem description
If you use a standard `BDay()` in the `date_range()` call, everything works as expected. However, if you use a `CustomBusinessDay`, it ends with a TypeError:
```
Traceback (most recent call last):
File "/Users/bspeice/Development/datemath.py", line 102, in trading_range
return pd.date_range(start, end, freq=TradingDay(skip))
File "/Users/bspeice/venvs/scientific/lib/python2.7/site-packages/pandas/tseries/index.py", line 2023, in date_range
closed=closed, **kwargs)
File "/Users/bspeice/venvs/scientific/lib/python2.7/site-packages/pandas/util/decorators.py", line 91, in wrapper
return func(*args, **kwargs)
File "/Users/bspeice/venvs/scientific/lib/python2.7/site-packages/pandas/tseries/index.py", line 301, in __new__
ambiguous=ambiguous)
File "/Users/bspeice/venvs/scientific/lib/python2.7/site-packages/pandas/tseries/index.py", line 506, in _generate
index = _generate_regular_range(start, end, periods, offset)
File "/Users/bspeice/venvs/scientific/lib/python2.7/site-packages/pandas/tseries/index.py", line 1976, in _generate_regular_range
dates = list(xdr)
File "/Users/bspeice/venvs/scientific/lib/python2.7/site-packages/pandas/tseries/offsets.py", line 2881, in generate_range
start = offset.rollforward(start)
File "/Users/bspeice/venvs/scientific/lib/python2.7/site-packages/pandas/tseries/offsets.py", line 432, in rollforward
dt = dt + self.__class__(1, normalize=self.normalize, **self.kwds)
TypeError: __init__() got an unexpected keyword argument 'normalize'
```
This error happens with every release from 0.18.0 - 0.19.2, I haven't tested outside this range.
#### Expected Output
True
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Darwin
OS-release: 16.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 34.3.0
Cython: None
numpy: 1.12.0
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.2.2
sphinx: 1.5.2
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 2.0.0
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.3
html5lib: 0.9999999
httplib2: 0.10.3
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.5
boto: None
pandas_datareader: None
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| 215,028,213 |
MDExOlB1bGxSZXF1ZXN0MTExMjk4NjEx
| 15,718 |
TST: only catch deprecation warnings for top-level module imports
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| 5 | 2017-03-17T14:58:28Z | 2017-03-21T00:18:42Z | 2017-03-17T14:58:35Z |
CONTRIBUTOR
| null |
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MDU6SXNzdWUyMTUwMzc3NzA=
| 15,719 |
Segmentation fault when ast and tables are imported before pandas
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[] | null | 10 | 2017-03-17T15:30:10Z | 2017-03-20T17:36:29Z | 2017-03-20T17:34:34Z |
NONE
| null |
```python
import tables
import ast
import pandas
```
This results in a segmentation fault using Anaconda 64 bit under Python 2 and Python 3.
* Pandas version 0.19.2 as obtained from conda-forge (np112py27_1).
* Pytables version 3.3.0 as obtained from conda0forge (np112py27_0).
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DOC: Update broken link in cookbook.rst #15605
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| 1 | 2017-03-17T15:39:33Z | 2018-12-12T21:40:25Z | 2017-03-17T15:42:26Z |
CONTRIBUTOR
| null |
- [ ] closes #15605
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
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groupby ignores rows containing the number -9223372036854775808
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| 2 | 2017-03-17T17:07:42Z | 2017-03-17T17:21:08Z | 2017-03-17T17:20:49Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: np.iinfo(np.int64).min == -9223372036854775808
True
In [4]: data = []
In [5]: data.append(-9223372036854775808)
In [6]: data.append(-9223372036854775808+1)
In [7]: data.append(0)
In [8]: df = pd.DataFrame( data, columns=["x"] ); df
Out[8]:
x
0 -9223372036854775808
1 -9223372036854775807
2 0
In [9]: df.groupby("x").groups
Out[9]:
{-9223372036854775807: Int64Index([1], dtype='int64'),
0: Int64Index([2], dtype='int64')}
In [10]: df.groupby("x")["x"].count()
x
-9223372036854775807 1
0 1
Name: x, dtype: int64
```
#### Problem description
The groupby operation ignores the row containing -9223372036854775808.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.8.0-41-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 34.3.1
Cython: None
numpy: 1.11.1rc1
scipy: None
statsmodels: None
xarray: None
IPython: 5.3.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2014.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.1
html5lib: 0.999999999
httplib2: 0.9.1
apiclient: None
sqlalchemy: 1.0.14
pymysql: None
psycopg2: None
jinja2: 2.9.5
boto: None
pandas_datareader: None
</details>
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CI: remove caching for miniconda itself
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| 0 | 2017-03-17T17:10:53Z | 2017-03-17T17:11:56Z | 2017-03-17T17:11:56Z |
CONTRIBUTOR
| null |
well got this to work, and it *does* cache, but the cache file is so huge that its just not worth it. going back to clean installs for miniconda itself.
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ERR: validate encoding on to_stata
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| 15 | 2017-03-17T17:48:10Z | 2017-09-05T12:34:28Z | 2017-03-21T21:54:22Z |
NONE
| null |
It seems ``pandas`` in ``python3.5`` causes issues due to encoding. For example the following generates a corrupt output file
```python
import pandas as pd
df1 = pd.DataFrame(np.array([1,2,3,4]), columns=['var1'])
df1.to_stata('corrupt.dta', write_index=False, encoding='utf8')
```
while
```python
df1.to_stata('not-corrupt.dta', write_index=False)
```
generates a correct file. I imagine this may be due to use of encoding and the difference in the treatment between ``python 2`` and ``python 3``, which breaks compatibility of scripts across ``python`` versions. I guess it would be nice if it does not take this option into account on ``python 3``, unless the error is caused by something else.
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MDU6SXNzdWUyMTUwNzk0OTc=
| 15,724 |
to_hdf5 copies data, causes MemoryError
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| 1 | 2017-03-17T18:00:59Z | 2017-03-17T18:03:23Z | 2017-03-17T18:02:41Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
result.to_hdf(op.join(args.output_dir, "merged.hd5"), "data", mode='w')
```
#### Problem description
Even though my data fits in memory, I run out of memory when trying to save it to disk apparently because pandas makes copies:
```
Traceback (most recent call last):
File "source/prof/merge_prof.py", line 144, in <module>
main()
File "source/prof/merge_prof.py", line 131, in main
result.to_hdf(op.join(args.output_dir, "merged.hd5"), "data", mode='w')
File "/home/jgarvin/.local/lib/python3.4/site-packages/pandas/core/generic.py", line 1138, in to_hdf
return pytables.to_hdf(path_or_buf, key, self, **kwargs)
File "/home/jgarvin/.local/lib/python3.4/site-packages/pandas/io/pytables.py", line 270, in to_hdf
f(store)
File "/home/jgarvin/.local/lib/python3.4/site-packages/pandas/io/pytables.py", line 264, in <lambda>
f = lambda store: store.put(key, value, **kwargs)
File "/home/jgarvin/.local/lib/python3.4/site-packages/pandas/io/pytables.py", line 873, in put
self._write_to_group(key, value, append=append, **kwargs)
File "/home/jgarvin/.local/lib/python3.4/site-packages/pandas/io/pytables.py", line 1315, in _write_to_group
s.write(obj=value, append=append, complib=complib, **kwargs)
File "/home/jgarvin/.local/lib/python3.4/site-packages/pandas/io/pytables.py", line 2877, in write
self.write_array('block%d_values' % i, blk.values, items=blk_items)
File "/home/jgarvin/.local/lib/python3.4/site-packages/pandas/io/pytables.py", line 2670, in write_array
self._handle.create_array(self.group, key, value)
File "/home/jgarvin/.local/lib/python3.4/site-packages/tables/file.py", line 1145, in create_array
obj=obj, title=title, byteorder=byteorder)
File "/home/jgarvin/.local/lib/python3.4/site-packages/tables/array.py", line 187, in __init__
byteorder, _log)
File "/home/jgarvin/.local/lib/python3.4/site-packages/tables/leaf.py", line 270, in __init__
super(Leaf, self).__init__(parentnode, name, _log)
File "/home/jgarvin/.local/lib/python3.4/site-packages/tables/node.py", line 266, in __init__
self._v_objectid = self._g_create()
File "/home/jgarvin/.local/lib/python3.4/site-packages/tables/array.py", line 196, in _g_create
nparr = array_as_internal(self._obj, flavor)
File "/home/jgarvin/.local/lib/python3.4/site-packages/tables/flavor.py", line 180, in array_as_internal
return array_of_flavor2(array, src_flavor, internal_flavor)
File "/home/jgarvin/.local/lib/python3.4/site-packages/tables/flavor.py", line 133, in array_of_flavor2
return convfunc(array)
File "/home/jgarvin/.local/lib/python3.4/site-packages/tables/flavor.py", line 373, in conv_to_numpy
nparr = nparr.copy() # copying the array makes it contiguous
MemoryError
```
#### Expected Output
no exception ;)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-85-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: None
pip: 1.5.4
setuptools: 3.3
Cython: None
numpy: 1.12.0
scipy: 0.18.1
statsmodels: 0.8.0
xarray: None
IPython: None
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.2
matplotlib: 2.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.5
boto: None
pandas_datareader: None
</details>
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MDU6SXNzdWUyMTUxMjIzNDQ=
| 15,725 |
BUG: HDFStore fixed format not decoding meta-data in PY3
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| 3 | 2017-03-17T21:06:22Z | 2021-05-08T01:30:56Z | null |
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
In Python 2:
```python
df = pd.DataFrame([[1, 2], [3, 4]])
df.index.name = "date"
df.to_hdf('/tmp/test.hdf', 'test')
```
In Python 3:
```python
In [2]: pd.read_hdf('/tmp/test.hdf', 'test')
Out[2]:
0 1
b'date'
0 1 2
1 3 4
```
#### Problem description
``b'date'`` should actually be just ``date``
#### Expected Output
``` python
Out[2]:
0 1
date
0 1 2
1 3 4
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.3.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+603.g2cad4dd0b
pytest: 3.0.6
pip: 9.0.1
setuptools: 33.1.1
Cython: 0.25.2
numpy: 1.12.0
scipy: 0.18.1
xarray: 0.9.1
IPython: 5.1.0.dev
sphinx: 1.4.9
patsy: 0.3.0-dev
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
feather: 0.3.1
matplotlib: 2.0.0
openpyxl: 2.3.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.6
lxml: 3.7.1
bs4: 4.5.3
html5lib: 0.999999999
sqlalchemy: 1.0.15
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
pandas_gbq: None
pandas_datareader: 0.2.1
</details>
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MDU6SXNzdWUyMTUxMzE4NjA=
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Groupby on date component results in corrupt DataFrame
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| 6 | 2017-03-17T21:54:34Z | 2017-03-18T10:59:38Z | 2017-03-17T22:01:26Z |
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [3]: df1 = pd.DataFrame({c : np.random.random(4) for c in ['a', 'b']})
In [4]: date = pd.DataFrame({'day' : 15, 'month' : df1.index + 1, 'year' : 2009})
In [5]: df1['date'] = pd.to_datetime(date)
In [6]: df1['month'] = df1['date'].dt.month
In [7]: df2 = df1.groupby('month').max()
In [8]: df2.transpose().mean()
Out[8]:
Series([], dtype: float64)
```
#### Problem description
``df2`` had strictly positive dimensions, its mean cannot be an empty ``Series``.
#### Expected Output
The same as
``` python
In [9]: df2.mean(axis=1)
Out[9]:
month
1 0.649630
2 0.392446
3 0.693638
12 0.381830
dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.3.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+603.g2cad4dd0b
pytest: 3.0.6
pip: 9.0.1
setuptools: 33.1.1
Cython: 0.25.2
numpy: 1.12.0
scipy: 0.18.1
xarray: 0.9.1
IPython: 5.1.0.dev
sphinx: 1.4.9
patsy: 0.3.0-dev
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
feather: 0.3.1
matplotlib: 2.0.0
openpyxl: 2.3.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.6
lxml: 3.7.1
bs4: 4.5.3
html5lib: 0.999999999
sqlalchemy: 1.0.15
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
pandas_gbq: None
pandas_datareader: 0.2.1
</details>
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data input column name
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| 2 | 2017-03-17T23:00:29Z | 2017-03-18T10:24:48Z | 2017-03-18T00:10:23Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```
a = df[(df.Gold>=1)&(df.Gold.1>=1)]
```
#### Problem description
when I input a data series, it automatically name the variable name to 'Gold' and 'Gold 1' because the input has two 'Gold' column.
which leads to an issue when I want to use indexing. The dot is recognized as "Invalid Syntax".
My solution is to rename the column but I wonder if I miss anything or actually we can automatically name them as 'Gold_1' instead of 'Gold.1' when this situation occur.
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CI: install nomkl to speed building
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| 0 | 2017-03-18T02:10:56Z | 2017-03-18T02:51:36Z | 2017-03-18T02:51:36Z |
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| 1 | 2017-03-18T03:29:38Z | 2017-03-18T04:00:32Z | 2017-03-18T03:32:47Z |
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| 0 | 2017-03-18T15:45:35Z | 2017-03-18T16:02:10Z | 2017-03-18T16:02:10Z |
CONTRIBUTOR
| null |
not really necessary & just makes the building take longer
|
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MDExOlB1bGxSZXF1ZXN0MTExNDE0NjIx
| 15,731 |
TST: move conftest.py to top-level
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| 7 | 2017-03-18T15:46:43Z | 2017-03-21T00:14:28Z | 2017-03-18T16:01:20Z |
CONTRIBUTOR
| null |
xref #15341
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MDU6SXNzdWUyMTUyMDI4ODY=
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OverflowError converting str to int in dataframe on win32
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| 3 | 2017-03-18T15:56:47Z | 2017-03-18T16:22:04Z | 2017-03-18T16:04:38Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame()
df.loc[1, 'timestamp'] = '1489687200000'
df.timestamp.astype(int)
```
#### Problem description
Results in error:
```
OverflowError Traceback (most recent call last)
<ipython-input-51-aa072084e764> in <module>()
1 df = pd.DataFrame()
2 df.loc[1, 'timestamp'] = '1489687200000'
----> 3 df.timestamp.astype(int)
C:\Users\Marco\anaconda\lib\site-packages\pandas\core\generic.py in astype(self, dtype, copy, raise_on_error, **kwargs)
3052 # else, only a single dtype is given
3053 new_data = self._data.astype(dtype=dtype, copy=copy,
-> 3054 raise_on_error=raise_on_error, **kwargs)
3055 return self._constructor(new_data).__finalize__(self)
3056
C:\Users\Marco\anaconda\lib\site-packages\pandas\core\internals.py in astype(self, dtype, **kwargs)
3187
3188 def astype(self, dtype, **kwargs):
-> 3189 return self.apply('astype', dtype=dtype, **kwargs)
3190
3191 def convert(self, **kwargs):
C:\Users\Marco\anaconda\lib\site-packages\pandas\core\internals.py in apply(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)
3054
3055 kwargs['mgr'] = self
-> 3056 applied = getattr(b, f)(**kwargs)
3057 result_blocks = _extend_blocks(applied, result_blocks)
3058
C:\Users\Marco\anaconda\lib\site-packages\pandas\core\internals.py in astype(self, dtype, copy, raise_on_error, values, **kwargs)
459 **kwargs):
460 return self._astype(dtype, copy=copy, raise_on_error=raise_on_error,
--> 461 values=values, **kwargs)
462
463 def _astype(self, dtype, copy=False, raise_on_error=True, values=None,
C:\Users\Marco\anaconda\lib\site-packages\pandas\core\internals.py in _astype(self, dtype, copy, raise_on_error, values, klass, mgr, **kwargs)
502
503 # _astype_nansafe works fine with 1-d only
--> 504 values = _astype_nansafe(values.ravel(), dtype, copy=True)
505 values = values.reshape(self.shape)
506
C:\Users\Marco\anaconda\lib\site-packages\pandas\types\cast.py in _astype_nansafe(arr, dtype, copy)
532 elif arr.dtype == np.object_ and np.issubdtype(dtype.type, np.integer):
533 # work around NumPy brokenness, #1987
--> 534 return lib.astype_intsafe(arr.ravel(), dtype).reshape(arr.shape)
535
536 if copy:
pandas\lib.pyx in pandas.lib.astype_intsafe (pandas\lib.c:17409)()
pandas\src\util.pxd in util.set_value_at_unsafe (pandas\lib.c:72777)()
OverflowError: Python int too large to convert to C long
```
#### Expected Output
On macos this works fine, but unfortunately I have to migrate a project to windows and there it does not work. I'm running the latest anaconda for win32 on windows 7.
int('1489687200000') works fine, also using df.timestamp.astype(np.int64) instead of astype(int) works. That's also my workaround right now.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 70 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.5
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: 2.45.0
pandas_datareader: 0.2.1
</details>
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MDExOlB1bGxSZXF1ZXN0MTExNDE2Njkw
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CI: turn on cache for osx
|
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TST: clean up build testing
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MAINT: Drop order and sort from pandas objects
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| 2 | 2017-03-19T01:16:36Z | 2017-03-19T02:08:50Z | 2017-03-19T02:02:21Z |
MEMBER
| null |
Affected classes:
1) `Index`
2) `Series`
2) `DataFrame`
xref #10726
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Value returns ndarray for dataframes with a single column with datetime64 tz-aware
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[] | null | 6 | 2017-03-19T20:08:37Z | 2017-07-26T23:57:54Z | 2017-07-26T23:57:53Z |
CONTRIBUTOR
| null |
- [x] closes #14052
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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BUG: concat with sparse not propogating default_fill_value
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| 2 | 2017-03-20T00:26:46Z | 2019-09-18T14:02:15Z | 2019-09-18T14:02:15Z |
CONTRIBUTOR
| null |
from https://github.com/pandas-dev/pandas/issues/12174#issuecomment-287594124
```
In [1]: df = pd.SparseDataFrame(np.random.choice([0., 1.], size=(5,5)), default_fill_value=0.)
...:
In [2]: df
Out[2]:
0 1 2 3 4
0 0.0 1.0 1.0 0.0 0.0
1 1.0 1.0 1.0 0.0 1.0
2 0.0 1.0 0.0 1.0 0.0
3 1.0 1.0 1.0 0.0 1.0
4 1.0 0.0 0.0 1.0 1.0
In [3]: df.default_fill_value
Out[3]: 0.0
In [4]: pd.concat((df, df), axis=1)
Out[4]:
0 1 2 3 4 0 1 2 3 4
0 0.0 1.0 1.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0
1 1.0 1.0 1.0 0.0 1.0 1.0 1.0 1.0 0.0 1.0
2 0.0 1.0 0.0 1.0 0.0 0.0 1.0 0.0 1.0 0.0
3 1.0 1.0 1.0 0.0 1.0 1.0 1.0 1.0 0.0 1.0
4 1.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 1.0 1.0
In [5]: pd.concat((df, df), axis=1).default_fill_value
Out[5]: nan
```
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PERF: Improve drop_duplicates for bool columns (#12963)
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| 14 | 2017-03-20T02:05:03Z | 2017-12-20T02:00:38Z | 2017-03-20T22:47:22Z |
MEMBER
| null |
- [x] closes #12963
- [x] tests passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
Converts `bool` columns to `int` when calling `drop_duplicates()` so the factorization does not go down the `object` path.
```
before after ratio
[9ab57dc5] [4b05fccb]
- 10.2±0.01ms 4.68±0.01ms 0.46 reindex.Duplicates.time_frame_drop_dups_bool
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
```
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MDExOlB1bGxSZXF1ZXN0MTExNDkyMjEy
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DOC: Fix typo in docstring param name
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| 1 | 2017-03-20T05:45:44Z | 2017-03-20T08:14:00Z | 2017-03-20T08:13:53Z |
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| 4 | 2017-03-20T06:20:20Z | 2017-12-20T02:00:47Z | 2017-03-20T17:51:28Z |
MEMBER
| null |
- [x] closes #13303
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
Previously, calling a date/time attribute with Timestamp that's tz aware (e.g. `Timestamp('...', tz='...').dayofyear`) would return the attribute in UTC instead of the local tz.
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[] | null | 0 | 2017-03-20T14:17:19Z | 2017-03-20T14:17:36Z | 2017-03-20T14:17:36Z |
CONTRIBUTOR
| null |
closes #12747
supersedes 12780
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| 1 | 2017-03-20T14:22:06Z | 2017-03-20T17:22:51Z | 2017-03-20T16:25:54Z |
CONTRIBUTOR
| null |
closes #12747
Author: sinhrks <[email protected]>
This patch had conflicts when merged, resolved by
Committer: Jeff Reback <[email protected]>
closes #15742
Closes #12780 from sinhrks/replace_type and squashes the following commits:
f9154e8 [sinhrks] remove unnecessary comments
279fdf6 [sinhrks] remove import failure
de44877 [sinhrks] BUG: replace coerces incorrect dtype
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| 215,439,278 |
MDU6SXNzdWUyMTU0MzkyNzg=
| 15,743 |
BUG: replace of numeric by string / dtype coversion
|
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| 2 | 2017-03-20T14:33:55Z | 2017-03-28T18:27:00Z | 2017-03-28T18:27:00Z |
CONTRIBUTOR
| null |
xref #12747
This was an item not closed by #12780 (but listed in #12747)
```
In [2]: pd.Series([1,2,3]).replace('1', np.nan)
Out[2]:
0 NaN
1 2.0
2 3.0
dtype: float64
```
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MDExOlB1bGxSZXF1ZXN0MTExNTg2NTUz
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Return mode even if single value (#15714)
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| 6 | 2017-03-20T15:59:20Z | 2017-03-29T23:54:14Z | 2017-03-29T23:53:57Z |
CONTRIBUTOR
| null |
- [X] Please accept this PR which closes #15714
- [X] Tests added — `test_mode_single`
- [X] Passes ``git diff upstream/master | flake8 --diff`` (however: it complains about some non-changed rows in hashtable_func_helper.pxi.in)
|
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CLN: define .mode in terms of value_counts
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| 6 | 2017-03-20T16:42:47Z | 2021-05-08T01:31:16Z | null |
CONTRIBUTOR
| null |
we have quite a lot of code to support .mode (internally we use pretty much the same algos as ``.value_counts()``)
but unless I am not understanding isn't.
``x.mode() == x.value_counts(sort=True).index[0]`` ?
if ties, then return a slice of the result
e.g.
```
In [17]: Series([1, 1, 1, 2, 2, 2, 3]).value_counts()
Out[17]:
2 3
1 3
3 1
dtype: int64
In [18]: Series([1, 1, 1, 2, 2, 2, 3]).mode()
Out[18]:
0 1
1 2
dtype: int64
```
cc @TomAugspurger
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CLN: make _Indexer.obj a weak ref
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| 9 | 2017-03-20T17:22:09Z | 2017-10-27T20:33:42Z | 2017-10-27T20:33:42Z |
CONTRIBUTOR
| null |
This will allow pandas objects to be collected on the first generation of the gc rather than wait for it to break cycles. Practically I am not sure this will have much of a user change.
https://github.com/dask/distributed/issues/956#issuecomment-287783490
The idea is to change this code [here](https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexing.py#L93)
from
```
class _NDFrameIndexer(object):
_valid_types = None
_exception = KeyError
axis = None
def __init__(self, obj, name):
self.obj = obj
self.ndim = obj.ndim
self.name = name
```
to
```
class _NDFrameIndexer(object):
_valid_types = None
_exception = KeyError
axis = None
def __init__(self, obj, name):
self.obj = weakref.ref(obj)
self.ndim = obj.ndim
self.name = name
```
and corresponding ``self.obj`` to ``self.obj()``
it 'works' in that gc collection happens immedately upon object deletion (IOW ``del df``). but a few fails on caching / chaining. In particular tests like: https://github.com/pandas-dev/pandas/blob/master/pandas/tests/indexing/test_chaining_and_caching.py#L31 I think were relying upon the reference NOT being collected (so that they can check it).
So this would require some internal reworking to remove / fix this. I suspect we will still achieve the same user effects (meaning of detection of chaining etc).
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Proposal to change behaviour with .loc and missing keys
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| 31 | 2017-03-20T18:25:07Z | 2020-05-12T16:39:37Z | 2017-10-03T10:55:51Z |
MEMBER
| null |
```python
In [2]: pd.Series([1, 2, 3]).loc[[2,3]]
Out[2]:
2 3.0
3 NaN
dtype: float64
In [3]: pd.Series([1, 2, 3]).loc[[3]]
[...]
KeyError: 'None of [[3]] are in the [index]'
```
#### Problem description
Although coherent (except for some unfortunate side-effects - some of them below) with the [docs](http://pandas.pydata.org/pandas-docs/stable/indexing.html#selection-by-label) where they say "At least 1 of the labels for which you ask, must be in the index or a ``KeyError`` will be raised!", the current behavior is - I claim - a terrible choice for both developers and users.
There are (at least) three ways to behave with missing labels:
1. you raise an error if requested at least one missing label
2. you raise an error if requested _only_ missing labels
2a ... while if at least one label is present, missing labels become ``NaN`` __(current)__
2b. ... while if at least one label is present, missing labels are silently dropped
3. you never raise an error for missing labels
3a ... and they become ``NaN``
3b. ... and they are silently dropped
#### For developers
Options 1. and 3. are both much easier to implement, because in both cases you can reduce the question "am I going to get an error?" in smaller pieces - e.g. when indexing a ``MultiIndex``, you will get an error if you get an error on any of its levels. Option 2. is instead more complicated (and computationally expensive), because you need to first aggregate in some way across levels/axes, and only then can you decide whether to raise an error or not. Several incoherences came out as a consequence of this choice, some of them still unsolved, such as #15452, [this](https://github.com/pandas-dev/pandas/pull/15615#issuecomment-287826997), the fact that ``pd.Series(range(2)).loc[[]]`` does not raise, and the fact that ``pd.DataFrame.ix[[missing_label]]`` doesn't either.
#### Other consequences of 2.
Additionally, it was decided that the behavior with missing labels would be to introduce ``NaN``s (rather than to drop them), and I think this was also not a good choice (and indeed partial indexing ``MultiIndex``es does not behave this way - it couldn't). I think it is also undocumented.
And finally, since the above wouldn't always tell you what to do when there are missing labels in a ``MultiIndex``, it was decided that ``.loc`` would rather behave as ``.reindex`` when there are missing _and incomplete_ labels, which is totally unexpected and, I think, undocumented.
Notice that these further issues (and more in general, the question "what to do when some labels are missing and you are not raising an error") would partially still hold with 3, but could be dealt with, I think, more elegantly.
#### For users
I think the current behavior is annoying to users not just because of those "__Other consequences__", but also because it is more complicated to describe in terms of set operation on labels/indices. For instance, with options 1. and 3.
``pd.concat([chunk.loc[something] for chunk in chunks])``
and
``pd.concat(chunks).loc[something]``
both return the same result (or raise). Instead with 2. it actually depends on how missing labels are distributed across chunks.
#### (Why this?)
It is worth understanding why 2. was picked in the first place, and I think the answer is "to be coherent with intervals". But I think it's not worth the damage - after all, an iterable and an interval are different objects. And moreover, introducing ``NaN``s for missing labels is anyway incoherent with intervals.
#### Backward incompatibility
Option 1. is, I think, the best, because it is also coherent with numpy's behavior with out-of-bounds indices (e.g. ``np.array([[1,2], [3,4]])[0,[1,3]]`` raises an ``IndexError``).
But while clearly both 1. and 3. could break some existing code, 3. would be better from this point of view, in the sense that it would break only code _assuming_ that an error is raised. Although one might even claim that 1., by breaking code which looks for missing labels, can help discover bugs in user code (not a great argument, I know).
So overall I am not sure about what we should pick between 1. and 3. But I really think we should leave 2., and that the later it is done, the worse. @jreback , @jorisvandenbossche if you want to tell me your thoughts about this, I can elaborate on what we could do with the "__Other consequences__" in the desired option.
Then if you approve the change, I'm willing to help in implementing it.
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MDExOlB1bGxSZXF1ZXN0MTExNjE5ODcz
| 15,748 |
MAINT: Remove Long and WidePanel
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| 6 | 2017-03-20T18:26:53Z | 2017-03-21T00:39:17Z | 2017-03-20T19:47:49Z |
MEMBER
| null |
Deprecated since 0.17.0.
xref #10892
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DOC: Patch new flake8 command grep
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| 1 | 2017-03-20T19:01:50Z | 2017-03-21T00:39:14Z | 2017-03-20T19:39:25Z |
MEMBER
| null |
The grep was initially matching to "pandas," which is incorrect because that was also matching files containing "pandas" in the name but that were not in the main `pandas` directory (e.g. performance test code). This change enforces that we match to any Python files in the main `pandas` directory.
Also picked up compatibility issue with OSX, in which the `-r` flag does not exist. However, `xargs` terminates if the argument list is empty, which was the whole point of passing in `-r` in the first place.
Follow-up to #15712
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API: Series.values with tz-aware should return object array of Timestamps
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| 7 | 2017-03-20T19:28:20Z | 2019-01-05T14:51:12Z | 2019-01-05T14:51:12Z |
CONTRIBUTOR
| null |
xref #14052
discussed at bit in the original PR: https://github.com/pandas-dev/pandas/pull/10477
I think was a mistake to return a ``datetime64[ns]`` in UTC for a tz-aware Series. we should
simply return an object array of ``Timestamps``, as it round-trips correctly. IOW, you don't lose the timezones.
```
tz-naive
In [9]: Series(pd.date_range('20130101',periods=3)).values
Out[9]:
array(['2013-01-01T00:00:00.000000000', '2013-01-02T00:00:00.000000000',
'2013-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
tz-aware - currently
In [10]: Series(pd.date_range('20130101',periods=3, tz='US/Eastern')).values
Out[10]:
array(['2013-01-01T05:00:00.000000000', '2013-01-02T05:00:00.000000000',
'2013-01-03T05:00:00.000000000'], dtype='datetime64[ns]')
```
what we should do
```
In [15]: np.array(Series(pd.date_range('20130101',periods=3, tz='US/Eastern')).tolist())
Out[15]:
array([Timestamp('2013-01-01 00:00:00-0500', tz='US/Eastern'),
Timestamp('2013-01-02 00:00:00-0500', tz='US/Eastern'),
Timestamp('2013-01-03 00:00:00-0500', tz='US/Eastern')], dtype=object)
```
round trips are plainly wrong (current)
```
In [5]: arr = Series(pd.date_range('20130101',periods=3, tz='US/Eastern'))
In [6]: Series(arr.values)
Out[6]:
0 2013-01-01 05:00:00
1 2013-01-02 05:00:00
2 2013-01-03 05:00:00
dtype: datetime64[ns]
```
round-trips are preserved (proposed)
```
In [3]: arr = np.array(Series(pd.date_range('20130101',periods=3, tz='US/Eastern')).tolist())
In [4]: Series(arr)
Out[4]:
0 2013-01-01 00:00:00-05:00
1 2013-01-02 00:00:00-05:00
2 2013-01-03 00:00:00-05:00
dtype: datetime64[ns, US/Eastern]
```
I don't think there is any way to transition to this and we simply have to change it. `
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PERF: BoolHashTable
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| 1 | 2017-03-20T22:52:19Z | 2021-05-08T01:31:34Z | null |
CONTRIBUTOR
| null |
xref https://github.com/pandas-dev/pandas/pull/15738#issuecomment-287843016
Currently bool data is passed to the generic python object hashtable for the following methods, making them all very slow.
- [ ] `value_counts`
- [ ] `rank`
- [ ] `unique`
Linked PR casts to int for `factorize`, `duplicated`, `drop_duplicates`
We could skip the hashing altogether and take advantage of the fixed set of values, e.g. below is a fastpath for unique.
```python
%%cython
from numpy cimport uint8_t
cimport cython
import numpy as np
@cython.boundscheck(False)
@cython.wraparound(False)
def unique(uint8_t[:] data):
cdef:
bint seen_true = False
bint seen_false = False
Py_ssize_t i, N = len(data)
uint8_t val
with nogil:
for i in range(N):
val = data[i]
if val == 0:
seen_false = True
elif val == 1:
seen_true = True
if seen_true and seen_false:
break
if seen_true and seen_false:
return np.array([True, False])
elif seen_true:
return np.array([True])
elif seen_false:
return np.array([False])
else:
return np.array([], dtype=bool)
In [35]: bools = np.array([False, True] * 100000)
In [36]: %timeit pd.unique(bools.view('uint8')).astype(bool)
1000 loops, best of 3: 1.74 ms per loop
In [37]: %timeit unique(bools.view('uint8'))
100000 loops, best of 3: 3.47 µs per loop
```
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| 15,752 |
TST: full dtype tests for .drop_duplicates and .duplicated
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| 1 | 2017-03-20T23:08:42Z | 2021-05-08T01:31:58Z | null |
CONTRIBUTOR
| null |
https://github.com/pandas-dev/pandas/pull/15738#issuecomment-287925070
xref #15738
we exercise tests for ``.drop_duplicates`` & ``.duplicated`` in various places
- for series -> ``pandas/tests/series/test_analytics.py``
- for dataframe -> ``pandas/test/frame/test_analytics.py``
- for index -> ``pandas/tests/test_base.py``
- ``datetimes`` -> ``pandas/tests//indexes/datetimes/test_ops.py``
- ``period`` -> ``pandas/tests//indexes/period/test_ops.py``
- ``categorical`` -> ``pandas/tests//test_categorical.py``
- ``timedelta`` -> ``pandas/tests//indexes/timedeltas/test_ops.py``
I think we are a little light on the ``categorical`` ones (esp need to exercise with categories of int, datetime, timedelta, bool)
``bool`` and a battery that covers all ``int`` & ``uint`` & ``float`` would be nice as well.
could certainly take multiple PR's to close this issue (if multiple people want to do this).
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MDU6SXNzdWUyMTU1ODk3MzU=
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BUG: PandasAutoDateLocator num_days calculation is wrong
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| 7 | 2017-03-20T23:51:57Z | 2017-03-21T15:49:03Z | 2017-03-21T08:53:20Z |
NONE
| null |
This line in PandasAutoDateLocator looks like it's missing some parentheses.
https://github.com/pandas-dev/pandas/blob/master/pandas/tseries/converter.py#L264
num_days = ((delta.years * 12.0) + delta.months * 31.0) + delta.days
I stepped through here and with a delta of 5 years, num_days was 60! It's easy enough to fix:
num_days = (((delta.years * 12.0) + delta.months) * 31.0) + delta.days
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| 2 | 2017-03-21T00:13:25Z | 2017-03-21T16:50:08Z | null |
NONE
| null |
```python
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
pd.show_versions()
index = np.array([
'2011-10-01 09:00',
'2016-10-01 09:00',
], dtype='datetime64[ns]')
values = np.array([
-0.26,
-0.01,
])
data_utc = pd.Series(values, index)
data_utc.index = data_utc.index.tz_localize('UTC')
data_eastern = pd.Series(values, index)
data_eastern.index = data_eastern.index.tz_localize('UTC').tz_convert('US/Eastern')
data_utc.plot(title='UTC')
plt.figure()
data_eastern.plot(title='US/Eastern')
plt.figure()
```
With the Eastern time zone, the x-axis labels are off by a year.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-229.4.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: C
LOCALE: None.None
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 27.2.0
Cython: None
numpy: 1.12.0
scipy: None
statsmodels: None
xarray: None
IPython: 5.3.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 2.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.5
boto: None
pandas_datareader: None
</details>
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| 215,594,653 |
MDU6SXNzdWUyMTU1OTQ2NTM=
| 15,755 |
nrows incompatible with chunksize in read_csv
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| 0 | 2017-03-21T00:25:11Z | 2017-03-21T18:06:01Z | 2017-03-21T18:06:01Z |
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [2]: pd.read_csv('/tmp/boh.csv', nrows=10, chunksize=2)
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-2-c652c48282ad> in <module>()
----> 1 pd.read_csv('/tmp/boh.csv', nrows=10, chunksize=2)
/home/nobackup/repo/pandas/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, 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, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)
651 skip_blank_lines=skip_blank_lines)
652
--> 653 return _read(filepath_or_buffer, kwds)
654
655 parser_f.__name__ = name
/home/nobackup/repo/pandas/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
391
392 if (nrows is not None) and (chunksize is not None):
--> 393 raise NotImplementedError("'nrows' and 'chunksize' cannot be used"
394 " together yet.")
395 elif nrows is not None:
NotImplementedError: 'nrows' and 'chunksize' cannot be used together yet.
```
(as introduced by #6774 )
#### Problem description
Supporting them together is not complicated - PR on its way.
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ENH: support "nrows" and "chunksize" together
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| 12 | 2017-03-21T00:41:13Z | 2017-03-21T18:54:48Z | 2017-03-21T18:06:01Z |
MEMBER
| null |
- [x] closes #15755
- [x] tests added / passed
- [x] passes ``git diff master | flake8 --diff`` (except for ``whatsnew/v0.20.0.txt``, I don't know why)
- [x] whatsnew entry
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MAINT: Drop convert_objects from NDFrame
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MEMBER
| null |
Deprecated since 0.17.0
xref #11173
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| 15,758 |
BUG: Series.asof fails for all NaN Series (GH15713)
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| 6 | 2017-03-21T03:05:24Z | 2017-03-26T04:01:09Z | 2017-03-26T02:31:15Z |
CONTRIBUTOR
| null |
- [x] closes bug #15713
- [x] 1 tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
Added the test if the series is all nans
Added the code that check if that's the case: if yes, return the expected output
As this is my first contribution, please comment if I did it right :)
Thanks!
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MDU6SXNzdWUyMTU2MzQ3ODE=
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Pandas ticker to ohlc
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| 1 | 2017-03-21T05:41:06Z | 2017-03-21T10:19:05Z | 2017-03-21T10:19:05Z |
NONE
| null |
pandas (0.19.2)
rows is a list of dict from mysql.
rows example
[{'date': datetime.datetime(2017, 3, 21, 13, 27, 20), 'tid': 648605515L, 'price': Decimal('1080.04000000'), 'type': 1, 'amount': Decimal('10.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 20), 'tid': 648605549L, 'price': Decimal('1081.55000000'), 'type': 1, 'amount': Decimal('16.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 20), 'tid': 648605547L, 'price': Decimal('1081.33000000'), 'type': 1, 'amount': Decimal('20.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 20), 'tid': 648605545L, 'price': Decimal('1081.30000000'), 'type': 1, 'amount': Decimal('16.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 20), 'tid': 648605543L, 'price': Decimal('1081.29000000'), 'type': 1, 'amount': Decimal('20.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 20), 'tid': 648605541L, 'price': Decimal('1080.46000000'), 'type': 1, 'amount': Decimal('26.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 20), 'tid': 648605517L, 'price': Decimal('1080.04000000'), 'type': 1, 'amount': Decimal('8.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 22), 'tid': 648605601L, 'price': Decimal('1079.69000000'), 'type': -1, 'amount': Decimal('70.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 25), 'tid': 648605686L, 'price': Decimal('1079.72000000'), 'type': -1, 'amount': Decimal('4.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 26), 'tid': 648605765L, 'price': Decimal('1079.45000000'), 'type': 1, 'amount': Decimal('6.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 26), 'tid': 648605753L, 'price': Decimal('1079.60000000'), 'type': -1, 'amount': Decimal('106.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 26), 'tid': 648605751L, 'price': Decimal('1079.60000000'), 'type': -1, 'amount': Decimal('80.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 26), 'tid': 648605749L, 'price': Decimal('1079.67000000'), 'type': -1, 'amount': Decimal('430.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 26), 'tid': 648605747L, 'price': Decimal('1079.70000000'), 'type': -1, 'amount': Decimal('66.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 26), 'tid': 648605745L, 'price': Decimal('1079.74000000'), 'type': -1, 'amount': Decimal('12.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 27), 'tid': 648605785L, 'price': Decimal('1079.45000000'), 'type': 1, 'amount': Decimal('2.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 27), 'tid': 648605774L, 'price': Decimal('1079.45000000'), 'type': 1, 'amount': Decimal('6.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 27), 'tid': 648605771L, 'price': Decimal('1079.45000000'), 'type': 1, 'amount': Decimal('14.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 28), 'tid': 648605827L, 'price': Decimal('1079.45000000'), 'type': 1, 'amount': Decimal('42.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 28), 'tid': 648605842L, 'price': Decimal('1079.45000000'), 'type': 1, 'amount': Decimal('10.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 32), 'tid': 648605973L, 'price': Decimal('1079.45000000'), 'type': 1, 'amount': Decimal('2.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 37), 'tid': 648606114L, 'price': Decimal('1079.44000000'), 'type': 1, 'amount': Decimal('24.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 37), 'tid': 648606116L, 'price': Decimal('1079.45000000'), 'type': 1, 'amount': Decimal('40.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 42), 'tid': 648606258L, 'price': Decimal('1079.45000000'), 'type': 1, 'amount': Decimal('56.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 45), 'tid': 648606345L, 'price': Decimal('1079.46000000'), 'type': -1, 'amount': Decimal('10.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 46), 'tid': 648606392L, 'price': Decimal('1079.69000000'), 'type': 1, 'amount': Decimal('44.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 48), 'tid': 648606418L, 'price': Decimal('1079.60000000'), 'type': -1, 'amount': Decimal('40.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 48), 'tid': 648606420L, 'price': Decimal('1079.46000000'), 'type': -1, 'amount': Decimal('36.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 48), 'tid': 648606422L, 'price': Decimal('1079.46000000'), 'type': -1, 'amount': Decimal('94.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 50), 'tid': 648606499L, 'price': Decimal('1079.31000000'), 'type': 1, 'amount': Decimal('80.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 50), 'tid': 648606478L, 'price': Decimal('1079.31000000'), 'type': -1, 'amount': Decimal('6.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 50), 'tid': 648606476L, 'price': Decimal('1079.31000000'), 'type': -1, 'amount': Decimal('34.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 50), 'tid': 648606474L, 'price': Decimal('1079.55000000'), 'type': -1, 'amount': Decimal('8.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 55), 'tid': 648606666L, 'price': Decimal('1079.31000000'), 'type': 1, 'amount': Decimal('44.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 55), 'tid': 648606650L, 'price': Decimal('1079.17000000'), 'type': 1, 'amount': Decimal('8.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 27, 55), 'tid': 648606648L, 'price': Decimal('1079.17000000'), 'type': 1, 'amount': Decimal('8.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 1), 'tid': 648606820L, 'price': Decimal('1079.03000000'), 'type': -1, 'amount': Decimal('28.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 2), 'tid': 648606825L, 'price': Decimal('1079.03000000'), 'type': 1, 'amount': Decimal('30.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 2), 'tid': 648606836L, 'price': Decimal('1079.02000000'), 'type': -1, 'amount': Decimal('22.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 5), 'tid': 648606945L, 'price': Decimal('1078.58000000'), 'type': -1, 'amount': Decimal('2.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 5), 'tid': 648606943L, 'price': Decimal('1078.61000000'), 'type': -1, 'amount': Decimal('2.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 5), 'tid': 648606941L, 'price': Decimal('1078.63000000'), 'type': -1, 'amount': Decimal('2.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 5), 'tid': 648606939L, 'price': Decimal('1078.88000000'), 'type': -1, 'amount': Decimal('2.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 5), 'tid': 648606926L, 'price': Decimal('1078.88000000'), 'type': -1, 'amount': Decimal('428.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 6), 'tid': 648606984L, 'price': Decimal('1078.58000000'), 'type': 1, 'amount': Decimal('2.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 6), 'tid': 648606982L, 'price': Decimal('1078.05000000'), 'type': -1, 'amount': Decimal('10.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 6), 'tid': 648606971L, 'price': Decimal('1078.58000000'), 'type': 1, 'amount': Decimal('2.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 6), 'tid': 648606957L, 'price': Decimal('1078.05000000'), 'type': -1, 'amount': Decimal('74.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 6), 'tid': 648606955L, 'price': Decimal('1078.15000000'), 'type': -1, 'amount': Decimal('6.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 6), 'tid': 648606953L, 'price': Decimal('1078.15000000'), 'type': -1, 'amount': Decimal('14.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 6), 'tid': 648606951L, 'price': Decimal('1078.42000000'), 'type': -1, 'amount': Decimal('16.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 7), 'tid': 648606992L, 'price': Decimal('1078.05000000'), 'type': -1, 'amount': Decimal('2.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 7), 'tid': 648606995L, 'price': Decimal('1078.58000000'), 'type': 1, 'amount': Decimal('2.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 7), 'tid': 648607023L, 'price': Decimal('1078.06000000'), 'type': -1, 'amount': Decimal('4.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 8), 'tid': 648607047L, 'price': Decimal('1078.86000000'), 'type': 1, 'amount': Decimal('2.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 10), 'tid': 648607113L, 'price': Decimal('1078.06000000'), 'type': -1, 'amount': Decimal('2.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 10), 'tid': 648607115L, 'price': Decimal('1078.03000000'), 'type': -1, 'amount': Decimal('148.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 12), 'tid': 648607192L, 'price': Decimal('1079.00000000'), 'type': -1, 'amount': Decimal('10.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 13), 'tid': 648607218L, 'price': Decimal('1078.99000000'), 'type': 1, 'amount': Decimal('98.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 13), 'tid': 648607220L, 'price': Decimal('1079.00000000'), 'type': 1, 'amount': Decimal('42.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 13), 'tid': 648607222L, 'price': Decimal('1079.03000000'), 'type': 1, 'amount': Decimal('342.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 13), 'tid': 648607224L, 'price': Decimal('1079.16000000'), 'type': 1, 'amount': Decimal('512.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 14), 'tid': 648607250L, 'price': Decimal('1078.98000000'), 'type': 1, 'amount': Decimal('44.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 14), 'tid': 648607252L, 'price': Decimal('1078.98000000'), 'type': 1, 'amount': Decimal('12.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 14), 'tid': 648607254L, 'price': Decimal('1079.00000000'), 'type': 1, 'amount': Decimal('106.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 14), 'tid': 648607256L, 'price': Decimal('1079.16000000'), 'type': 1, 'amount': Decimal('40.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 20), 'tid': 648607431L, 'price': Decimal('1079.16000000'), 'type': 1, 'amount': Decimal('28.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 20), 'tid': 648607429L, 'price': Decimal('1079.01000000'), 'type': 1, 'amount': Decimal('2.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 20), 'tid': 648607427L, 'price': Decimal('1079.01000000'), 'type': 1, 'amount': Decimal('2.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 23), 'tid': 648607518L, 'price': Decimal('1079.16000000'), 'type': 1, 'amount': Decimal('8.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 24), 'tid': 648607544L, 'price': Decimal('1079.16000000'), 'type': 1, 'amount': Decimal('344.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 25), 'tid': 648607593L, 'price': Decimal('1078.79000000'), 'type': -1, 'amount': Decimal('6.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 26), 'tid': 648607631L, 'price': Decimal('1079.16000000'), 'type': 1, 'amount': Decimal('430.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 26), 'tid': 648607623L, 'price': Decimal('1079.16000000'), 'type': 1, 'amount': Decimal('18.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 26), 'tid': 648607621L, 'price': Decimal('1078.79000000'), 'type': 1, 'amount': Decimal('14.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 29), 'tid': 648607695L, 'price': Decimal('1079.16000000'), 'type': 1, 'amount': Decimal('776.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 32), 'tid': 648607803L, 'price': Decimal('1079.16000000'), 'type': 1, 'amount': Decimal('2.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 32), 'tid': 648607805L, 'price': Decimal('1079.16000000'), 'type': 1, 'amount': Decimal('10.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 36), 'tid': 648607905L, 'price': Decimal('1079.16000000'), 'type': 1, 'amount': Decimal('2.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 37), 'tid': 648607940L, 'price': Decimal('1079.31000000'), 'type': 1, 'amount': Decimal('2.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 42), 'tid': 648608110L, 'price': Decimal('1079.46000000'), 'type': -1, 'amount': Decimal('12.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 46), 'tid': 648608211L, 'price': Decimal('1079.88000000'), 'type': -1, 'amount': Decimal('12.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 46), 'tid': 648608213L, 'price': Decimal('1079.88000000'), 'type': -1, 'amount': Decimal('6.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 57), 'tid': 648608534L, 'price': Decimal('1080.29000000'), 'type': 1, 'amount': Decimal('14.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 28, 57), 'tid': 648608536L, 'price': Decimal('1080.30000000'), 'type': 1, 'amount': Decimal('2.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 29, 2), 'tid': 648608683L, 'price': Decimal('1080.59000000'), 'type': 1, 'amount': Decimal('40.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 29, 3), 'tid': 648608733L, 'price': Decimal('1080.59000000'), 'type': 1, 'amount': Decimal('360.00000000')}, {'date': datetime.datetime(2017, 3, 21, 13, 29, 7), 'tid': 648608838L, 'price': Decimal('1080.90000000'), 'type': 1, 'amount': Decimal('82.00000000')}]
if I didn't use set_index ,it will have an `TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'RangeIndex'`
if rows:
df = pd.DataFrame(rows)
print df.head()
# TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'RangeIndex'
df = df.set_index("date")
print df.head()
resample_data = df.resample("1min", how={"price": "ohlc", "amount": "sum"})
print resample_data
Result :
Connected to pydev debugger (build 162.1967.10)
amount date price tid type
0 2.00000000 2017-03-21 11:15:12 1075.83000000 648370156 -1
1 10.00000000 2017-03-21 11:15:15 1076.00000000 648370241 -1
2 10.00000000 2017-03-21 11:15:17 1075.83000000 648370297 -1
3 10.00000000 2017-03-21 11:15:17 1075.83000000 648370311 1
4 8.00000000 2017-03-21 11:15:19 1076.13000000 648370370 1
amount price tid type
date
2017-03-21 11:15:12 2.00000000 1075.83000000 648370156 -1
2017-03-21 11:15:15 10.00000000 1076.00000000 648370241 -1
2017-03-21 11:15:17 10.00000000 1075.83000000 648370297 -1
2017-03-21 11:15:17 10.00000000 1075.83000000 648370311 1
2017-03-21 11:15:19 8.00000000 1076.13000000 648370370 1
/Users/wyx/bitcoin_workspace/fibo-strategy/ticker.py:45: FutureWarning: how in .resample() is deprecated
the new syntax is .resample(...)..apply(<func>)
resample_data = df.resample("1min", how={"price": "ohlc", "amount": "sum"})
Traceback (most recent call last):
File "/Applications/PyCharm.app/Contents/helpers/pydev/pydevd.py", line 1580, in <module>
globals = debugger.run(setup['file'], None, None, is_module)
File "/Applications/PyCharm.app/Contents/helpers/pydev/pydevd.py", line 964, in run
pydev_imports.execfile(file, globals, locals) # execute the script
File "/Users/wyx/bitcoin_workspace/fibo-strategy/ticker.py", line 45, in <module>
resample_data = df.resample("1min", how={"price": "ohlc", "amount": "sum"})
File "/Users/wyx/bitcoin_workspace/fibo-strategy/.env/lib/python2.7/site-packages/pandas/core/generic.py", line 4216, in resample
limit=limit)
File "/Users/wyx/bitcoin_workspace/fibo-strategy/.env/lib/python2.7/site-packages/pandas/tseries/resample.py", line 582, in _maybe_process_deprecations
r = r.aggregate(how)
File "/Users/wyx/bitcoin_workspace/fibo-strategy/.env/lib/python2.7/site-packages/pandas/tseries/resample.py", line 320, in aggregate
result, how = self._aggregate(arg, *args, **kwargs)
File "/Users/wyx/bitcoin_workspace/fibo-strategy/.env/lib/python2.7/site-packages/pandas/core/base.py", line 549, in _aggregate
result = _agg(arg, _agg_1dim)
File "/Users/wyx/bitcoin_workspace/fibo-strategy/.env/lib/python2.7/site-packages/pandas/core/base.py", line 500, in _agg
result[fname] = func(fname, agg_how)
File "/Users/wyx/bitcoin_workspace/fibo-strategy/.env/lib/python2.7/site-packages/pandas/core/base.py", line 483, in _agg_1dim
return colg.aggregate(how, _level=(_level or 0) + 1)
File "/Users/wyx/bitcoin_workspace/fibo-strategy/.env/lib/python2.7/site-packages/pandas/core/groupby.py", line 2652, in aggregate
return getattr(self, func_or_funcs)(*args, **kwargs)
File "/Users/wyx/bitcoin_workspace/fibo-strategy/.env/lib/python2.7/site-packages/pandas/core/groupby.py", line 1128, in ohlc
lambda x: x._cython_agg_general('ohlc'))
File "/Users/wyx/bitcoin_workspace/fibo-strategy/.env/lib/python2.7/site-packages/pandas/core/groupby.py", line 3103, in _apply_to_column_groupbys
return func(self)
File "/Users/wyx/bitcoin_workspace/fibo-strategy/.env/lib/python2.7/site-packages/pandas/core/groupby.py", line 1128, in <lambda>
lambda x: x._cython_agg_general('ohlc'))
File "/Users/wyx/bitcoin_workspace/fibo-strategy/.env/lib/python2.7/site-packages/pandas/core/groupby.py", line 808, in _cython_agg_general
raise DataError('No numeric types to aggregate')
pandas.core.base.DataError: No numeric types to aggregate
Process finished with exit code 1
I am a rookie for pandas.
1. How solve the error?
2. And if I want to use the last close price to fill the NaN of next
min ohlc. How to do that?
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MDU6SXNzdWUyMTU2MzkwODU=
| 15,760 |
BUG: to_datetime with utc=True for Series
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| 1 | 2017-03-21T06:15:42Z | 2017-03-21T21:28:05Z | 2017-03-21T08:49:03Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```javascript
In [2]: ts = 1.4884427244831782e+18
In [3]: pd.to_datetime(ts, utc=True)
Out[3]: Timestamp('2017-03-02 08:18:44.483178240+0000', tz='UTC')
In [4]: pd.to_datetime([ts], utc=True)
Out[4]: DatetimeIndex(['2017-03-02 08:18:44.483178240+00:00'], dtype='datetime64[ns, UTC]', freq=None)
In [5]: pd.to_datetime(pd.Series([ts]), utc=True)
Out[5]:
0 2017-03-02 08:18:44.483178240
dtype: datetime64[ns]
```
#### Problem description
The argument ``utc=True`` in function ``to_datetime`` is not working when passing a ``Series``, differently from other case, e.g. when passing a list or a scalar Unix timestamp.
#### Expected Output
The expected output is the same as the one given by
```javascript
In [6]: pd.to_datetime(pd.Series([ts])).dt.tz_localize('UTC')
Out[6]:
0 2017-03-02 08:18:44.483178240+00:00
dtype: datetime64[ns, UTC]
```
#### Another example: `tz_convert`
It is straightforward to convert to another time zone from a Unix timestamp or list:
```javascript
In [7]: pd.to_datetime(ts, utc=True).tz_convert('Europe/Paris')
Out[7]: Timestamp('2017-03-02 09:18:44.483178240+0100', tz='Europe/Paris')
In [8]: pd.to_datetime([ts], utc=True).tz_convert('Europe/Paris')
Out[8]: DatetimeIndex(['2017-03-02 09:18:44.483178240+01:00'], dtype='datetime64[ns, Europe/Paris]', freq=None)
```
whereas for a `Series`:
```javascript
In [9]: pd.to_datetime(pd.Series([ts])).dt.tz_localize('UTC').dt.tz_convert('Europe/Paris')
Out[9]:
0 2017-03-02 09:18:44.483178240+01:00
dtype: datetime64[ns, Europe/Paris]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-113-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: ca_ES.UTF-8
LOCALE: ca_ES.UTF-8
pandas: 0.19.2
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0.post20161106
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: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: 0.2.1
</details>
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| 215,641,423 |
MDU6SXNzdWUyMTU2NDE0MjM=
| 15,761 |
groupby aggregate function with a dictionary argument mishandles datetime64 columns with TZ info
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| 3 | 2017-03-21T06:32:20Z | 2017-03-21T09:56:46Z | 2017-03-21T09:25:30Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
from datetime import datetime
import pandas as pd
import pytz
tz = pytz.timezone('Asia/Hong_Kong')
datetimes = [datetime(2017, 3, 21, 1, 0, 0, 0, tz), datetime(2017, 3, 21, 2, 0, 0, 0, tz),
datetime(2017, 3, 21, 3, 0, 0, 0, tz), datetime(2017, 3, 21, 4, 0, 0, 0, tz)]
df = pd.DataFrame.from_dict({'groups': ['a', 'a', 'b', 'b'], 'dt': datetimes})
print('Expected output:')
print(df.groupby('groups').aggregate('max'))
print('Observed output:')
print(df.groupby('groups').aggregate({'dt': 'max'}))
```
#### Problem description
#### Expected Output
`df.groupby('some_column').aggregate({'some_datetime64_column': 'some_aggregate_function'})` and `df.groupby('some_column').aggregate('some_aggregate_function')` should return the same result for `some_datetime64_column` of type `datetime64` with specified timezones
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.103
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.1
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.3.0
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: 1.5.1
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: 0.7.9.None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.42.0
pandas_datareader: 0.2.1
</details>
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MDU6SXNzdWUyMTU2NjY2Mzg=
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different view when index a panel with multiindex
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| 1 | 2017-03-21T08:51:41Z | 2018-07-06T20:21:16Z | 2017-03-21T12:58:37Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
#### Problem description
I find a bug when using panel.apply(), and with some digging, I find an interesting indexing problem caused by multiindex in Panel Object. It is demonstrated below:
```python
#
import pandas as pd
pn1 = pd.Panel(pd.np.random.random(size=(3,4,5)),items=['AAA','BBB','CCC'])
print pn1.loc[:,:,1]
pn2 = pn1.copy()
pn2.major_axis = pd.MultiIndex.from_product([list('AB'),list('ab')])
print pn2.loc[:,:,1]
```
#### Output:
```
AAA BBB CCC
0 0.182982 0.774812 0.894589
1 0.654716 0.695453 0.790078
2 0.340826 0.941283 0.938544
3 0.351851 0.971896 0.362317
A B
a b a b
AAA 0.182982 0.654716 0.340826 0.351851
BBB 0.774812 0.695453 0.941283 0.971896
CCC 0.894589 0.790078 0.938544 0.362317
```
With a multiindex as the major_axis of Panel object, the indexing result is transposed. And because of that, when using ``` pn.apply(func, axis=('items','major'))```, it give you the wrong results when the major_axis is a multiindex.
Is this a bug? if not, then what is the purpose that it is designed like this?
Thanks for help.
</details>
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| 215,688,657 |
MDU6SXNzdWUyMTU2ODg2NTc=
| 15,763 |
Call save，Prompt list index out of range
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| 2 | 2017-03-21T10:20:56Z | 2017-03-22T01:51:55Z | 2017-03-21T12:07:56Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
import openpyxl
path = r'C:\Users\hcb\Desktop\test.xlsx'
wb = openpyxl.load_workbook(path)
wb.save('test.xlsx')
#### Problem description
Call save，Prompt list index out of range
#### Expected Output
Traceback (most recent call last):
File "D:/python3/test1/myPackge/test2.py", line 4, in <module>
wb.save('test.xlsx')
File "C:\Python36\lib\site-packages\openpyxl\workbook\workbook.py", line 342, in save
save_workbook(self, filename)
File "C:\Python36\lib\site-packages\openpyxl\writer\excel.py", line 267, in save_workbook
writer.save(filename)
File "C:\Python36\lib\site-packages\openpyxl\writer\excel.py", line 248, in save
self.write_data()
File "C:\Python36\lib\site-packages\openpyxl\writer\excel.py", line 81, in write_data
self._write_worksheets()
File "C:\Python36\lib\site-packages\openpyxl\writer\excel.py", line 197, in _write_worksheets
xml = ws._write()
File "C:\Python36\lib\site-packages\openpyxl\worksheet\worksheet.py", line 870, in _write
return write_worksheet(self)
File "C:\Python36\lib\site-packages\openpyxl\writer\worksheet.py", line 99, in write_worksheet
cols = ws.column_dimensions.to_tree()
File "C:\Python36\lib\site-packages\openpyxl\worksheet\dimensions.py", line 230, in to_tree
obj = col.to_tree()
File "C:\Python36\lib\site-packages\openpyxl\worksheet\dimensions.py", line 182, in to_tree
attrs = dict(self)
File "C:\Python36\lib\site-packages\openpyxl\worksheet\dimensions.py", line 51, in __iter__
value = getattr(self, key, None)
File "C:\Python36\lib\site-packages\openpyxl\styles\styleable.py", line 95, in __get__
return coll.names[idx]
IndexError: list index out of range
<details>
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| 15,764 |
CLN/INT: rename *possibly* -> *maybe* functions
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| 4 | 2017-03-21T12:50:34Z | 2017-03-22T11:57:12Z | 2017-03-22T11:57:12Z |
CONTRIBUTOR
| null |
we have a mixed bag on naming this. Let's change ``possibly`` -> ``maybe``. Keep everything else the same. (if you do the same search for ``maybe`` you will fine it to be far more common). Note that the *usage* of these functions will have to be renamed as well :>
```
(pandas) bash-3.2$ grep --include '*.py' -R possibly pandas |grep def
pandas/computation/expr.py: def _possibly_transform_eq_ne(self, node, left=None, right=None):
pandas/computation/expr.py: def _possibly_downcast_constants(self, left, right):
pandas/computation/expr.py: def _possibly_eval(self, binop, eval_in_python):
pandas/computation/expr.py: def _possibly_evaluate_binop(self, op, op_class, lhs, rhs,
pandas/core/internals.py:def _possibly_compare(a, b, op):
pandas/indexes/base.py: def _possibly_promote(self, other):
pandas/tests/types/test_cast.py: def test_possibly_convert_objects_copy(self):
pandas/tseries/index.py: def _possibly_promote(self, other):
pandas/tseries/tdi.py: def _possibly_promote(self, other):
pandas/types/cast.py:def _possibly_convert_platform(values):
pandas/types/cast.py:def _possibly_downcast_to_dtype(result, dtype):
pandas/types/cast.py:def _possibly_cast_item(obj, item, dtype):
pandas/types/cast.py:def _possibly_convert_objects(values, convert_dates=True, convert_numeric=True,
pandas/types/cast.py:def _possibly_castable(arr):
pandas/types/cast.py:def _possibly_infer_to_datetimelike(value, convert_dates=False):
pandas/types/cast.py:def _possibly_cast_to_datetime(value, dtype, errors='raise'):
```
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MDExOlB1bGxSZXF1ZXN0MTExNzc5MDE4
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CLN: replace _interleave_dtype with _find_common_type
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| 2 | 2017-03-21T13:35:18Z | 2017-03-21T14:53:08Z | 2017-03-21T14:50:13Z |
CONTRIBUTOR
| null |
xref #15736
xref #12780
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BUG: various 32bit compat issues
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| 1 | 2017-03-21T14:43:53Z | 2017-03-21T17:41:58Z | 2017-03-21T17:41:03Z |
CONTRIBUTOR
| null |
closes #14866
xref #14183
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Proper checks on nrows and chunksize for read_csv
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| 0 | 2017-03-21T16:33:14Z | 2017-03-22T08:03:51Z | 2017-03-22T08:03:32Z |
MEMBER
| null |
From #15755 (e.g. [this comment](https://github.com/pandas-dev/pandas/pull/15756#issuecomment-288108170)).
Current status:
- there is a check on ``nrows``, which ensure floats are converted to int with no loss of precision, but not that the resulting/passed value is non-negative
- there is no check on ``chunksize``, so that a ``pd.concat`` based on ``chunksize=0`` will enter an infinite loop
I guess we want to ensure ``chunksize > 0`` and ``nrows >= 0``.
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MDExOlB1bGxSZXF1ZXN0MTExODMyNDQ3
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BUG: Enforce correct encoding in stata
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| 6 | 2017-03-21T17:14:27Z | 2018-04-22T21:12:04Z | 2017-03-21T21:54:22Z |
CONTRIBUTOR
| null |
Ensure StataReader and StataWriter have the correct encoding.
Standardized default encoding to 'latin-1'
closes #15723
- [x] closes #15723
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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DOC: Ensure basic flake8 diff checks only Python
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| 1 | 2017-03-21T17:56:49Z | 2017-03-21T18:00:58Z | 2017-03-21T18:00:11Z |
MEMBER
| null |
Let's make sure all of the `flake8` commands in our documentation check only Python files (will be applicable to majority of contributions and PR's).
Follow-up to #15749
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Writing custom series to HDF store
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| 5 | 2017-03-21T18:14:01Z | 2017-03-22T10:28:10Z | 2017-03-21T19:09:09Z |
NONE
| null |
How do you write custom objects to an HDF file store?
Is there any guidelines for writing custom series to an HDF store? If so where?
Thank you
Andrew
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CLN/INT: Rename _possibly to _maybe (GH15764)
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| 6 | 2017-03-21T19:42:54Z | 2017-03-22T12:24:35Z | 2017-03-22T11:57:12Z |
CONTRIBUTOR
| null |
Also rename "private" functions in pandas.type.cast
- [x] closes #15764
- [x] tests passed
- [x] passes ``git diff upstream/master --name-only -- '*.py' | flake8 --diff``
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skipna added to groupby numeric ops
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| 26 | 2017-03-21T20:30:36Z | 2021-05-09T15:32:20Z | 2017-10-28T00:26:40Z |
NONE
| null |
- [x] closes #15675
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| 1 | 2017-03-21T20:46:35Z | 2017-03-21T23:23:16Z | 2017-03-21T23:20:44Z |
CONTRIBUTOR
| null |
- fixes ``.isin`` on 32-bit (hopefully)
- perf about 30% better (but this is a small number anyhow) & releases GIL
- more generic framework
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| 5 | 2017-03-21T22:29:22Z | 2017-03-22T08:33:23Z | 2017-03-22T08:03:33Z |
MEMBER
| null |
- [x] closes #15767
- [x] tests added / passed
- [x] passes ``git diff master --name-only -- '*.py' | flake8 --diff``
- [x] whatsnew entry
|
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CLN: separate out groupby algos
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| 1 | 2017-03-21T22:34:24Z | 2017-03-22T12:08:16Z | 2017-03-22T12:07:06Z |
CONTRIBUTOR
| null |
- separate out groupby algorithms to separate lib
- release GIL on median
- release GIL on is_lexsorted / fix memory leak
- release GIL on nancorr
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COMPAT: 32-bit skips
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| 1 | 2017-03-22T00:33:54Z | 2017-03-22T11:54:43Z | 2017-03-22T11:53:46Z |
CONTRIBUTOR
| null |
closes #14183
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Bug: Timestamp removes timezone localization
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| 7 | 2017-03-22T05:44:04Z | 2017-04-08T21:59:13Z | 2017-04-08T21:59:13Z |
MEMBER
| null |
```python
In [2]: tz = pd.DatetimeIndex(['2013-01-01 06:00'], tz='US/Pacific').tz
In [3]: tz
Out[3]: <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>
In [4]: pd.Timestamp('2013-01-01 06:00', tz=tz).tz
Out[4]: <DstTzInfo 'US/Pacific' PST-1 day, 16:00:00 STD>
```
#### Problem description
Localization of the timezone should be maintained when creating a Timestamp. I also believe this is the root issue of #13238 since `resample()` calls `groupby()` which (I think) reconstructs the index with Timestamps.
```python
In [10]: s = pd.Series(index=pd.DatetimeIndex(['2013-01-01 06:00', '2013-01-01 07:00', '2013-01-02 06:00'],
tz='America/Los_Angeles'),
data=[1, 2, 3])
In [13]: s.index.tz
Out[13]: <DstTzInfo 'America/Los_Angeles' LMT-1 day, 16:07:00 STD>
In [14]: s.resample('D').max().index.tz
Out[14]: <DstTzInfo 'America/Los_Angeles' PST-1 day, 16:00:00 STD>
```
#### Expected Output
```python
<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: 92239f5dcfb02f97b5b1eed651895fe70dfd7eb1
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-45-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.0+644.g92239f5.dirty
pytest: 3.0.6
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.2
scipy: None
xarray: None
IPython: 5.1.0
sphinx: 1.4.8
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
pandas_gbq: None
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTExOTc0MDQ5
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Fix scalar iloc
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[] | null | 12 | 2017-03-22T10:22:51Z | 2017-08-07T22:26:15Z | 2017-08-01T22:56:27Z |
MEMBER
| null |
- [x] closes #15686
- [x] tests added / passed
- [x] passes ``git diff master --name-only -- '*.py' | flake8 --diff``
- [x] whatsnew entry
Unless I'm missing anything, all code for indexers (inside ``pandas/core/indexing.py``) resorts to one of the following for setting values:
- if we can work directly on ``BlockManager.setitem`` (simple case, e.g. no multiple dtypes involved), do so
- otherwise, rely on methods provided by the object (mainly ``__setitem__``), _which are label based_
This makes it impossible, in the "non-simple case", to index positionally in the correct way with duplicated labels (by the way, it's also plain ugly).
I can see three (four) possible solutions:
1. temporarily strip an object from (some of) its labels - what this PR does. Arguably the simplest approach (doesn't require changing objects/``BlockManager`` code), arguably not very elegant (although we could already expect some efficiency gains compared to current master, since this removes some equality checks on labels)
2. add methods to the different objects that do position-based setting. Not trivial as it sounds, I'm afraid - such methods should talk directly the the ``BlockManager``s, hence for instance taking care of dtype changes
3. seriously improving ``BlockManager.setitem`` so that it is able to take care of multiple dtypes. Probably the best approach (would allow to enormously simplify indexing code), and the one requiring most work
4. (completely rewrite all setting code in ``indexing.py`` currently relying on ``self.obj.__setitem__`` so it directly talks to ``BlockManager.setitem``)
Notice that ``BlockManager.setitem`` must be fixed in order to fix #12991, but the fix for that bug is significantly simpler than 3. (it is just a matter of shifting indices, not of working with different dtypes).
The current PR... works, and does clean a bit the ``_setter`` interface (i.e. concerning the difference between ``iloc`` and ``loc``), which might be a good thing even in case we later decide to drop the less elegant ``InfoCleaner``. My preferred approach would be to apply it, then start working on 3., then come back to the indexing code and simplify it by profiting of 3. But if you prefer to start directly with 2. or 3., I can have a try in that direction. I don't think I'm willing to try 4. because current indexing code is already too complicated.
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PERF: cythonize groupby-rank
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| 2 | 2017-03-22T12:17:55Z | 2018-02-10T16:05:52Z | 2018-02-10T16:05:52Z |
CONTRIBUTOR
| null |
This dispatches to each group individually. Better to have a combined group_rank to do this. It is a bit of code and ideally would share some with the actual rank algos.
```
In [7]: ngroups = 1000
In [8]: N = 100000
In [9]: np.random.seed(1234)
In [10]: df = DataFrame({'key': np.random.randint(0, ngroups, size=N), 'value': np.arange(N)})
In [11]: %timeit df.groupby('key').rank()
1 loop, best of 3: 392 ms per loop
# comparision with group_shift_indexer, a transforming operator
In [13]: %timeit df.groupby('key').shift()
100 loops, best of 3: 3.15 ms per loop
```
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travis deduping on prs
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| 4 | 2017-03-22T12:52:37Z | 2017-03-22T15:38:39Z | 2017-03-22T13:58:05Z |
CONTRIBUTOR
| null |
closes #12438
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Return value of boolean datetime fields of NaT: False or NaN ?
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| 1 | 2017-03-22T14:29:18Z | 2017-03-27T19:22:23Z | 2017-03-27T19:22:23Z |
MEMBER
| null |
From discussion in https://github.com/pandas-dev/pandas/pull/15589, the handling of NaT is different for different datetime fields.
On a scalar value, `is_leap_year` returns False, the others are not defined:
```
In [1]: pd.Timestamp('NaT').is_month_start
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-1-654cc4e613aa> in <module>()
----> 1 pd.Timestamp('NaT').is_month_start
AttributeError: 'NaTType' object has no attribute 'is_month_start'
In [2]: pd.Timestamp('NaT').is_leap_year
Out[2]: False
```
On an index (or series `.dt` accessor), `is_leap_year` returns False, the others propagate NaN (with the consequence it is no longer a boolean array/series):
```
In [3]: pd.DatetimeIndex(['2012-01-01', 'NaT']).is_month_start
Out[3]: array([ 1., nan])
In [4]: pd.DatetimeIndex(['2012-01-01', 'NaT']).is_leap_year
Out[4]: array([ True, False], dtype=bool)
```
So when `is_leap_year` was introduced in #13739, this was done on purpose, citing @sinhrks "pd.NaT.is_leap_year results in False, as I think users want bool array.".
This seems indeed the more logical thing to return (certainly for arrays/series), so you can eg use it for indexing. For scalars it is a bit less clear what is the preferable option (return False or NaN), but probably best to be consistent.
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COMPAT: NaT accessors
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| 3 | 2017-03-22T14:47:23Z | 2017-03-27T19:23:35Z | 2017-03-27T19:22:23Z |
CONTRIBUTOR
| null |
closes #15781
PR
```
In [1]: idx = DatetimeIndex(['1/1/2000', None, None, '1/4/2000'])
In [2]: idx.is_month_start
Out[2]: array([ True, False, False, False], dtype=bool)
In [3]: idx.is_leap_year
Out[3]: array([ True, False, False, True], dtype=bool)
```
0.19.2
```
In [1]: idx = DatetimeIndex(['1/1/2000', None, None, '1/4/2000'])
In [3]: idx.is_month_start
Out[3]: array([ 1., nan, nan, 0.])
In [2]: idx.is_leap_year
Out[2]: array([ True, False, False, True], dtype=bool)
```
mimics ``is_leap_year`` to return a boolean (inlcuding for ``NaT``).
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MDExOlB1bGxSZXF1ZXN0MTEyMDk0NTA1
| 15,783 |
CI: remove travis dedupe as enabled auto-cancellation
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| 0 | 2017-03-22T19:43:40Z | 2017-03-23T19:07:23Z | 2017-03-23T19:07:23Z |
CONTRIBUTOR
| null |
xref https://github.com/pandas-dev/pandas/commit/79581ffe6fb73089dfa8394c2f4e44677acfe1ce
of course Travis just announced auto-cancellation / it looks good when I enabled it. so removing this :<
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Update testing.py
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| 1 | 2017-03-22T21:12:48Z | 2017-03-22T21:36:56Z | 2017-03-22T21:36:54Z |
CONTRIBUTOR
| null |
Tiny docs typo fix
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BUG: melt changes type of tz-aware columns
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| 1 | 2017-03-23T13:43:32Z | 2018-03-13T10:31:10Z | 2018-03-13T10:31:10Z |
NONE
| null |
#### Code Samples
```python
import pandas as pd
frame = pd.DataFrame({'klass':range(5), 'ts': [pd.Timestamp('2017-03-23 08:22:42.173378+01'), pd.Timestamp('2017-03-23 08:22:42.178578+01'), pd.Timestamp('2017-03-23 08:22:42.173578+01'), pd.Timestamp('2017-03-23 08:22:42.178378+01'), pd.Timestamp('2017-03-23 08:22:42.163378+01')], 'attribute':['att1', 'att2', 'att3', 'att4', 'att5'], 'value': ['a', 'b', 'c', 'd', 'd']})
# At this point, frame.ts is of dtype datetime64[ns, pytz.FixedOffset(60)]
frame.set_index(['ts', 'klass'], inplace=True)
queried_index = frame.query('value=="d"').index
pivoted_frame = frame.reset_index().pivot_table(index=['klass', 'ts'], columns='attribute', values='value', aggfunc='first')
melted_frame = pd.melt(pivoted_frame.reset_index(), id_vars=['klass', 'ts'], var_name='attribute', value_name='value')
# At this point, melted_frame.ts is of dtype datetime64[ns]
queried_after_melted_index = melted_frame.query('value=="d"').set_index(['ts', 'klass']).index
frame.loc[queried_index] # Works
frame.loc[queried_index] = 'test' # Works
frame.loc[queried_after_melted_index] # Works
frame.loc[queried_after_melted_index] = 'test' # Breaks
```
The last statement gives:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.5/dist-packages/pandas/core/indexing.py", line 140, in __setitem__
indexer = self._get_setitem_indexer(key)
File "/usr/local/lib/python3.5/dist-packages/pandas/core/indexing.py", line 127, in _get_setitem_indexer
return self._convert_to_indexer(key, is_setter=True)
File "/usr/local/lib/python3.5/dist-packages/pandas/core/indexing.py", line 1230, in _convert_to_indexer
raise KeyError('%s not in index' % objarr[mask])
KeyError: "MultiIndex(levels=[[2017-03-23 07:22:42.163378, 2017-03-23 07:22:42.173378, 2017-03-23 07:22:42.173578, 2017-03-23 07:22:42.178378, 2017-03-23 07:22:42.178578], [0, 1, 2, 3, 4]],\n labels=[[3, 0], [3, 4]],\n names=['ts', 'klass']) not in index"
```
#### Problem description
- It is counter-intuitive that any operation (which does not explicitly mention in its docs that it does) alters the type of any column.
- Also counter-intuitive is that ```frame.loc``` has different behavior in a statement than it has in an assignment.
#### Expected Output
- ```melted_frame.ts``` and ```frame.ts``` have the same dtype.
- ```DataFrame.loc``` fails in both cases, not just in an assignment, or succeeds in both.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-66-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 20.7.0
Cython: None
numpy: 1.12.0
scipy: None
statsmodels: None
xarray: None
IPython: 5.3.0
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: 0.7.3
lxml: 3.5.0
bs4: 4.4.1
html5lib: 0.999
httplib2: 0.9.1
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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| 216,453,969 |
MDU6SXNzdWUyMTY0NTM5Njk=
| 15,786 |
Values with the same indexes as x,y from np.array and pd.DataFrame aren't equal.
|
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| 1 | 2017-03-23T14:45:37Z | 2017-03-23T15:16:19Z | 2017-03-23T15:16:05Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
arr = np.array([[1,2,3],[4,5,6]])
df = pd.DataFrame(arr)
```
#### Problem description
the expected output is arr[0][1] equals df[0][1],but the real thing is that two elems aren't equal in value.I have seen the source code,a code line as following
```python
#other code
value = value.T
/#other code
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: zh_CN.UTF-8
LOCALE: None.None
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 29.0.1
Cython: 0.24
numpy: 1.12.1
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
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MDU6SXNzdWUyMTY0Nzc1NDg=
| 15,787 |
BUG: concat of MultiIndex with names passed
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| 18 | 2017-03-23T15:51:42Z | 2017-04-10T12:10:24Z | 2017-04-10T12:10:24Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
res = []
for _ in range(2):
res1 = []
# Only occurs when dataframe is used with measure
data = np.zeros((30, 21))
idx = np.random.randint(0, 5, 30)
df = pd.DataFrame(data, index=idx).loc[3]
#df = pd.DataFrame(data[::5, :]) # Uncomment for example of correct behavior
res1.append(pd.DataFrame(sum(data.dot(df.T))))
tmp = pd.concat(res1, keys=[1], names=['level1'])
res.append(tmp)
final = pd.concat(res, keys=[i for i in range(2)], names=['level2'])
print(final)
```
#### Problem description
In python, datatypes generally don't matter. A dataframe is a dataframe, but as shown in the example code concat'ing dataframes with an index does not have the same behavior as dataframes without an index. The label for a level of the index is dropped. This is a small bug. Run it several times (10-12 seems to do it) and you will see a much more worrisome issue: on occasion, the label is not dropped. Yes, the output of concat is random.
#### Expected Output
```
level2 level1
0 1 0 0.0
1 0.0
2 0.0
3 0.0
4 0.0
5 0.0
1 1 0 0.0
1 0.0
2 0.0
3 0.0
4 0.0
5 0.0
```
#### Output of ``pd.show_versions()``
```
level2
0 1 0 0.0
1 0.0
2 0.0
3 0.0
4 0.0
5 0.0
1 1 0 0.0
1 0.0
2 0.0
3 0.0
````
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MDU6SXNzdWUyMTY0OTEyMjc=
| 15,788 |
DataFrame.apply() returns DataFrame unexpectedly when length of list stored in cell matches DF-dimensions
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| 1 | 2017-03-23T16:30:19Z | 2017-03-23T18:31:04Z | 2017-03-23T18:30:49Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
# list length does NOT match DataFrame dimensions, works as expected:
df = pd.DataFrame()
df['A'] = [[1, 2], [1, 2], [1, 2]]
df['B'] = [[3, 4], [3, 4], [3, 4]]
df['C'] = [[4, 6], [4, 6], [4, 6]]
print(df)
# A B C
# 0 [1, 2] [3, 4] [4, 6]
# 1 [1, 2] [3, 4] [4, 6]
# 2 [1, 2] [3, 4] [4, 6]
D = df.apply(lambda row: [a+b for a, b in zip(row.A, row.B)], axis=1)
print(D)
# 0 [4, 6]
# 1 [4, 6]
# 2 [4, 6]
# dtype: object
df['D'] = D
print(df)
# A B C D
# 0 [1, 2] [3, 4] [4, 6] [4, 6]
# 1 [1, 2] [3, 4] [4, 6] [4, 6]
# 2 [1, 2] [3, 4] [4, 6] [4, 6]
# list length DOES match DataFrame dimensions, does not work as expected:
df = pd.DataFrame()
df['A'] = [[1, 2, 3], [1, 2, 3], [1, 2, 3]]
df['B'] = [[3, 4, 5], [3, 4, 5], [3, 4, 5]]
df['C'] = [[4, 6, 8], [4, 6, 8], [4, 6, 8]]
print(df)
# A B C
# 0 [1, 2, 3] [3, 4, 5] [4, 6, 8]
# 1 [1, 2, 3] [3, 4, 5] [4, 6, 8]
# 2 [1, 2, 3] [3, 4, 5] [4, 6, 8]
# unwanted result:
D = df.apply(lambda row: [a+b for a, b in zip(row.A, row.B)], axis=1)
print(D)
# A B C
# 0 4 6 8
# 1 4 6 8
# 2 4 6 8
# correct:
df['D'] = [[a+b for a, b in zip(row.A, row.B)] for row in df.itertuples()]
print(df)
# A B C D
# 0 [1, 2, 3] [3, 4, 5] [4, 6, 8] [4, 6, 8]
# 1 [1, 2, 3] [3, 4, 5] [4, 6, 8] [4, 6, 8]
# 2 [1, 2, 3] [3, 4, 5] [4, 6, 8] [4, 6, 8]
```
#### Problem description
This behavior came up in a bigger project, when the length of a stored list incidentally met the DataFrame's dimension and resulted in an exception raised. I didn't expect apply() to return a DataFrame at all. I tried to find a parameter to change this behavior, but didn't have any luck there.
May be related to https://github.com/pandas-dev/pandas/issues/5299
#### Expected Output
see above
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.11-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 34.3.2
Cython: None
numpy: 1.12.1
scipy: 0.19.0
statsmodels: 0.8.0
xarray: None
IPython: 5.3.0
sphinx: 1.5.3
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 2.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.7.1 (dt dec pq3 ext lo64)
jinja2: 2.9.5
boto: None
pandas_datareader: None
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DOC: .groupby() aligns Series, accepts ndarray
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| 3 | 2017-03-23T17:46:49Z | 2017-03-23T19:10:45Z | 2017-03-23T19:10:25Z |
CONTRIBUTOR
| null |
- [x] closes https://github.com/pandas-dev/pandas/issues/15244
- [ ] tests added / passed
- [x] passes ``git diff upstream/master --name-only -- '*.py' | flake8 --diff``
- [ ] whatsnew entry
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COMPAT: 3.6.1 compat for change in PySlice_GetIndices_Ex
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| 1 | 2017-03-23T18:06:49Z | 2017-03-23T19:13:08Z | 2017-03-23T19:11:33Z |
CONTRIBUTOR
| null |
This doesn't actually matter to any tests except for some internal consistency ones.
Bonus is that it eliminates a warning :<
note that we aren't actually testing this (yet) on Travis as our 3.6 build uses conda-forge and 3.6.1 is not there as of yet. Its in defaults though (and shows up on appveyor build).
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| 216,553,658 |
MDU6SXNzdWUyMTY1NTM2NTg=
| 15,791 |
jupyter notebook kernel crashes when doing groupby, having one row with NaN
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[] | null | 10 | 2017-03-23T19:57:20Z | 2021-05-08T01:34:36Z | 2021-05-08T01:34:36Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
df.groupby('City').count()['Bib'].sort_values(ascending=False).head(20)
```
#### Problem description
Data set has 26630 rows and one row of df["City"] has a NaN. The jupyter notebook kernel crashes.
#### Expected Output
A list of the top 20 cities is expected here.
df[df['City'].notnull()].groupby('City').count()['Bib'].sort_values(ascending=False).head(20)
produces the expected output.
https://github.com/rojour/boston_results
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.5
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: 2.45.0
pandas_datareader: 0.2.1
</details>
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CI: fix coverage file location
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| 1 | 2017-03-23T21:18:45Z | 2017-03-25T15:57:32Z | 2017-03-25T15:56:49Z |
CONTRIBUTOR
| null |
CI: clean up some unused env variables
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CLN/DEPR: html.border option -> display.html.border
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| 1 | 2017-03-23T21:27:50Z | 2017-07-16T15:32:45Z | 2017-07-16T15:32:45Z |
CONTRIBUTOR
| null |
xref #11784
```
html.border : int
A ``border=value`` attribute is inserted in the ``<table>`` tag
for the DataFrame HTML repr.
[default: 1] [currently: 1]
```
should migrate this (by deprecation) and moving to a new option ``display.html.border`` to keep in-line with our namespacing.
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Add .nunique method to DataFrame and .groupby
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| 3 | 2017-03-23T21:37:03Z | 2017-03-24T06:36:34Z | 2017-03-23T21:40:21Z |
CONTRIBUTOR
| null |
#### Currently, pd.DataFrame does not have a ``.nunique`` method
``DataFrames`` have various aggregation methods that they share with ``series``, examples being ``.sum``, ``.count`` etc. Likewise with ``DataFrame.groupby``-objects.
In many cases, a ``.nunique`` would be useful as on ``DataFrame`` and ``DataFrame.groupby``-objects, for example when you're trying to assess the diversity of data in a DataFrame.
#### Expected Output
When you're using ``DataFrame.nunique``, with ``n`` columns, you should get back a ``series`` where the index values are the dataframe.columns values, and the series values are the count of unique values, for each column.
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Rolling window endpoints inclusion
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| 26 | 2017-03-23T22:29:19Z | 2017-04-13T11:39:51Z | 2017-04-13T11:39:15Z |
CONTRIBUTOR
| null |
- [x] closes #13965
- [x] time-based window tests added / passed
- [x] fixed window tests added / passed
- [x] passes ``git diff upstream/master --name-only -- '*.py' | flake8 --diff``
- [x] whatsnew entry
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ERR: better error message for trying to concat non-pandas objects
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| 4 | 2017-03-23T23:03:41Z | 2017-10-18T10:27:57Z | 2017-10-18T10:27:57Z |
CONTRIBUTOR
| null |
```
In [3]: pd.concat([Series(), 'foo'])
TypeError: cannot concatenate a non-NDFrame object
```
trivially ``non-NDFrame`` -> ``non-pandas``. but I think even better would be something like
```
In [4]: objs = [Series(), 'foo']
In [7]: "cannot concatenate [{pandas}] with non-pandas objects of type [{non_pandas}]".format(
...: pandas=','.join(set([str(o.__class__.__name__) for o in objs if isinstance(o, pandas.core.generic.NDFrame)])),
...: non_pandas=','.join(set([str(type(o)) for o in objs if not isinstance(o, pandas.core.generic.NDFrame)])))
Out[7]: "cannot concatenate [Series] with non-pandas objects of type [<class 'str'>]"
```
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BUG/API: degenerate monotonic but not lexsorted MultiIndexes
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| 1 | 2017-03-23T23:55:09Z | 2017-04-22T02:58:17Z | 2017-04-22T02:58:17Z |
CONTRIBUTOR
| null |
after #15694. There are still some degenerate cases where even after ``.sort_index()`` a MI is not lexsorted (nor monotonic).
xref https://github.com/pandas-dev/pandas/pull/15694#issuecomment-288894362
We have a bunch of tests which assert this in ``test_multilevel.py``
```
In [1]: idx = MultiIndex([['A', 'B', 'C'], ['c', 'b', 'a']],
...: [[0,1,2,0,1,2], [0,2,1,1,0,2]])
...:
...: df = DataFrame({'col': range(len(idx))}, index=idx)
...:
In [2]: df
Out[2]:
col
A c 0
B a 1
C b 2
A b 3
B c 4
C a 5
In [3]: df.sort_index()
Out[3]:
col
A b 3
c 0
B a 1
c 4
C a 5
b 2
In [4]: df.sort_index().index.is_lexsorted()
Out[4]: False
In [5]: df.sort_index().index.is_monotonic
Out[5]: True
```
This can be fixed by the diff above. In essence, rather than ``.sort_index()`` *just* computing indexers off of _reconstructed index, we actually _reconnstruct the index on the returned object.
But this breaks a few things, namely some tests involving ``.stack()`` as that does ``.sort_index()`` internally. So would need to compensate. (and of course this subtley *changes* the actual returned values, in that they are actually sorted :>)
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MDExOlB1bGxSZXF1ZXN0MTEyNDEyODMz
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MAINT: Enforce string type for where parameter
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| 5 | 2017-03-24T09:05:39Z | 2017-03-25T20:49:21Z | 2017-03-25T15:59:49Z |
MEMBER
| null |
Deprecated in 0.11.0.
xref #12027.
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read_csv arguments: can we have skipcols and userows?
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[] | null | 4 | 2017-03-24T15:04:59Z | 2017-03-27T11:59:25Z | 2017-03-27T11:02:44Z |
NONE
| null |
Is there a reason why `read_csv` has a `usecols` and `skiprows` as arguments, but not `skipcols` and `userows`? Is this to avoid parameter checks or something more fundamental than that?
It would be nice to have all four options to avoid clunky inversions of the type `usecols = columns.remove(unwanted_col)`.
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ERR: need a better error message when trying to compare incompat types on merge
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| 0 | 2017-03-24T17:23:16Z | 2018-02-03T20:30:30Z | 2018-02-03T20:30:30Z |
CONTRIBUTOR
| null |
xref https://github.com/pandas-dev/pandas/pull/11410#issuecomment-289083481
These are *both* datetimes, but *not* compatible.
```
In [8]: df = DataFrame({'A':pd.date_range('20130101',periods=3),'B':pd.date_range('20130101',periods=3,tz='US/Eastern')})
In [9]: pd.merge(df, df, left_on='A', right_on='B')
TypeError: Argument 'values' has incorrect type (expected numpy.ndarray, got Index)
```
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| 0 | 2017-03-25T17:13:15Z | 2021-05-08T01:36:08Z | null |
CONTRIBUTOR
| null |
xfaile this test: https://github.com/pandas-dev/pandas/pull/15601/commits/4dc2ef8b0af84e60e8ec1dcc070dc9e478defdf8
in ``pandas/tests/io/test_pytables.py``
```
@pytest.mark.xfail(run=False,
reason="append_to_multiple_dropna_false "
"is not raising as failed")
def test_append_to_multiple_dropna_false(self):
df1 = tm.makeTimeDataFrame()
df2 = tm.makeTimeDataFrame().rename(columns=lambda x: "%s_2" % x)
df1.iloc[1, df1.columns.get_indexer(['A', 'B'])] = np.nan
df = concat([df1, df2], axis=1)
with ensure_clean_store(self.path) as store:
# dropna=False shouldn't synchronize row indexes
store.append_to_multiple(
{'df1a': ['A', 'B'], 'df2a': None}, df, selector='df1a',
dropna=False)
with pytest.raises(ValueError):
store.select_as_multiple(['df1a', 'df2a'])
assert not store.select('df1a').index.equals(
store.select('df2a').index)
```
This sometimes succeed and sometimes fails. Needs some investigation.
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| 217,012,386 |
MDExOlB1bGxSZXF1ZXN0MTEyNjA2ODg5
| 15,802 |
Revert "MAINT: Remove Long and WidePanel (#15748)"
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| 4 | 2017-03-25T23:11:14Z | 2017-03-26T02:17:11Z | 2017-03-26T02:16:32Z |
CONTRIBUTOR
| null |
This reverts commit bff47f2302a0be4dcbf7e5055e525d5652e08fb5.
xref #15748
we will have to push this to a later pandas major version as statsmodels not fully compat: https://github.com/statsmodels/statsmodels/issues/3580
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CI: cleanup / linting
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| 1 | 2017-03-25T23:28:34Z | 2017-03-26T02:16:47Z | 2017-03-26T02:16:11Z |
CONTRIBUTOR
| null |
CI: typo in .travis.yml for print_skipped
CI: linted .travis.yml
CI: removed CLIPBOARD env variables as not used
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DOC: Explain differences further for sep parameter
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| 1 | 2017-03-26T01:25:12Z | 2017-03-26T20:02:53Z | 2017-03-26T17:02:46Z |
MEMBER
| null |
Per comment in <a href="https://github.com/pandas-dev/pandas/issues/12686#issuecomment-288843261">#12686</a>. This is the only documentation issue that I found amongst the differences that still exist between the C and Python engines in `parsers.py`
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MAINT: Remove combineAdd and combineMult
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| 2 | 2017-03-26T01:36:12Z | 2017-03-26T20:02:49Z | 2017-03-26T16:58:59Z |
MEMBER
| null |
Deprecated in 0.17.0.
xref #10735
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Drop support for NaN categories in Categorical
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| 4 | 2017-03-26T01:48:11Z | 2017-03-27T19:16:59Z | 2017-03-27T14:47:02Z |
MEMBER
| null |
Deprecated in 0.17.0.
xref #10748
xref #13648
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CI: simplify ci setup a bit
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| 2 | 2017-03-26T13:54:43Z | 2017-03-27T10:48:44Z | 2017-03-26T15:12:20Z |
CONTRIBUTOR
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