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- [x] I think we could fully remove the `bench` sub-dir, taking care if we don't have asv's for anything there (I think we do but need to check). (PR https://github.com/pandas-dev/pandas/pull/16984)
- [x] `scripts` dir, we can blow windows_builder (PR #16984)
- [x] `scripts` dir, we can blow away most of these with the exception of: `api_rst_coverage.py`, `find_undoc_args.py`, `merge-py.py`, `find_commits_touching_func.py`, again checking for any benchmark scripts and make sure we have asv.
- [ ] add README.md for `scripts` that documents purpose of these scripts
- [x] ~~move `pandas/build_dist.sh` and `pandas/release_stats.sh` to `scripts` (need to updat e commit checklist though and slightly modify).~~
- [x] ~~remove `pandas/test_perf.sh` and `pandas/Makefile`~~
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} | 3 | 2016-07-19T12:23:27Z | 2016-07-20T21:23:30Z | 2016-07-20T21:23:30Z | MEMBER | null | - [x] closes #12817
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
```
# asv
- 33.75ms 3.22ms 0.10 period.period_algorithm.time_period_series_drop_duplicates
```
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} | 9 | 2016-07-19T12:34:43Z | 2016-07-20T22:05:22Z | 2016-07-20T21:11:50Z | MEMBER | null | - [x] closes #736
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Updated cookbook attaching SO link of original issue.
BTW, `pivot_annual` has a similar functionality but it looks to be replaced by `pivot_table` and datetime property access. Because the function is not on top `pd` namespace, shouldn't deprecate it?
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> Download zipped PWT data and calculate 5yr averages
link is bronken in the cookbook section of the wiki
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| null | 4 | 2016-07-19T14:16:55Z | 2018-11-20T11:05:05Z | 2016-07-19T14:20:36Z | CONTRIBUTOR | null | Hi,
I believe `.equals()` is incorrectly returning `False` on dataframes that are identical except for ordering of indexes. Think this may relate to closed issues #9330 and #9745.
#### Example
``` python
In [1]: import pandas as pd
In [2]: df1 = pd.DataFrame({'a':[1,2,3]}, index=['x','y','z'])
In [3]: df2 = pd.DataFrame({'a':[1,3,2]}, index=['x','z','y'])
In [4]: df1.equals(df2)
Out[4]: False #expected True
In [5]: df1.equals(df2.sort_index())
Out[5]: True
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: x86
processor: x86 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.0
nose: 1.3.7
pip: 7.1.2
setuptools: 18.5
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
xarray: None
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.0
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
```
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https://api.github.com/repos/pandas-dev/pandas/issues/13709 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13709/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13709/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13709/events | https://github.com/pandas-dev/pandas/issues/13709 | 166,345,390 | MDU6SXNzdWUxNjYzNDUzOTA= | 13,709 | pd.merge_asof() matches out of tolerance when timestamps are duplicated | {
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} | 3 | 2016-07-19T14:27:58Z | 2016-08-01T20:56:54Z | 2016-08-01T20:56:54Z | CONTRIBUTOR | null | This is a continuation of #13695.
Starting with the original DataFrames from that issue:
```
df1 = pd.DataFrame({'time':pd.to_datetime(['2016-07-15 13:30:00.030']), 'username':['bob']})
df2 = pd.DataFrame({'time':pd.to_datetime(['2016-07-15 13:30:00.000', '2016-07-15 13:30:00.030']), 'version':[1, 2]})
```
I now get the null:
```
In [82]: pd.merge_asof(df1, df2, on='time', allow_exact_matches=False, tolerance=pd.Timedelta('10ms'))
Out[82]:
time username version
0 2016-07-15 13:30:00.030 bob NaN
```
However, if I change the first DataFrame to have duplicate timestamps:
```
df1 = pd.DataFrame({'time':pd.to_datetime(['2016-07-15 13:30:00.030', '2016-07-15 13:30:00.030']), 'username':['bob', 'charlie']})
```
then the bug reappears:
```
In [85]: pd.merge_asof(df1, df2, on='time', allow_exact_matches=False, tolerance=pd.Timedelta('10ms'))
Out[85]:
time username version
0 2016-07-15 13:30:00.030 bob 1
1 2016-07-15 13:30:00.030 charlie 1
```
This is in pandas version 0.18.0+418.gc46dcfa.
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} | 2 | 2016-07-19T17:14:07Z | 2016-07-21T10:59:37Z | 2016-07-21T10:59:35Z | CONTRIBUTOR | null | - [x] closes #12259
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Automatically select type %tc for datetime[ns] columns
Change ValueErrors to NotImplementedError for unsupported types
Add tests for select exceptions
closes #12259
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| null | 9 | 2016-07-19T20:08:33Z | 2016-07-26T13:10:47Z | 2016-07-26T10:43:28Z | NONE | null | Hi,
I noticed that `pd.read_table` raises EmptyDataError, but only when using a filename rather than StringIO and when `engine="c"`.
Possibly related to #13652?
#### Code Sample, a copy-pastable example if possible
``` python
import pandas as pd
from io import StringIO
s = 'a\tb\tc\n1\t2\t3\n'
with open('a.tsv', 'w') as fout:
fout.write(s)
for label in ('StringIO', 'filename'):
for engine in ('python', 'c'):
print('input={}, engine={}'.format(label, engine))
if label == 'StringIO':
data = StringIO(s)
else:
data = 'a.tsv'
print(pd.read_table(data, engine=engine))
```
Output:
```
input=StringIO, engine=python
a b c
0 1 2 3
input=StringIO, engine=c
a b c
0 1 2 3
input=filename, engine=python
a b c
0 1 2 3
input=filename, engine=c
---------------------------------------------------------------------------
EmptyDataError Traceback (most recent call last)
...
pandas/parser.pyx in pandas.parser.TextReader.__cinit__ (pandas/parser.c:5214)()
EmptyDataError: No columns to parse from file
```
#### Expected Output
```
input=StringIO, engine=python
a b c
0 1 2 3
input=StringIO, engine=c
a b c
0 1 2 3
input=filename, engine=python
a b c
0 1 2 3
input=filename, engine=c
a b c
0 1 2 3
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-24-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 5.0.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
```
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} | 3 | 2016-07-19T21:17:16Z | 2020-03-31T05:54:07Z | 2020-03-31T05:54:06Z | MEMBER | null | xref #17603 - can be simplified when this issue is fixed
To be compat with `DatetimeIndex` kwds. The impl should be pretty straightforward.
- http://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html
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} | 1 | 2016-07-20T02:02:26Z | 2016-07-20T17:29:44Z | 2016-07-20T13:50:59Z | NONE | null | # Description
Importing pandas makes plotting with matplotlib and standard datetime objects impossible.
A type error is raised in matplotlib because pandas import datetime and "override the global" one.
I would not expect pandas to override the default datetime. After importing pandas one cannot use datetimes for plotting for example.
PS: just reposting this from matplotlib issue 6796
#### Code Sample, a copy-pastable example if possible
``` python
import datetime
import matplotlib.pyplot as plt
# this works
x = [datetime.date(1677,1,1) + datetime.timedelta(days=i) for i in range(10)]
plt.plot(x,range(10))
import pandas as pd
# do not work anymore
x = [datetime.date(1677,1,1) + datetime.timedelta(days=i) for i in range(10)]
plt.plot(x,range(10))
```
#### Expected Output
datetime would not be "change".
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_AU.utf-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 21.0.0
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: 0.7.2
IPython: 4.2.0
sphinx: 1.3.5
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: 2.3.3
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
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} | 5 | 2016-07-20T03:04:59Z | 2016-07-21T10:44:50Z | 2016-07-21T10:44:50Z | CONTRIBUTOR | null | This is a follow up to #13514 (safe sort of mixed-int arrays).
After merging this commit, [`test_constructor_unsortable`](https://github.com/pydata/pandas/blob/b225cacb1d2a34e3c4041533a0590133098756fa/pandas/tests/test_categorical.py#L94) in test_categorical.py breaks.
According to the [code there](https://github.com/pydata/pandas/blob/b225cacb1d2a34e3c4041533a0590133098756fa/pandas/tests/test_categorical.py#L104-L112), `numpy.sort` should sort a mixed int-datetime array in python2 and numpy >= 1.10. But it doesn't.
``` python
In [3]: arr = np.array([1, 2, datetime.now(), 0, 3], dtype='O')
In [4]: np.sort(arr)
/home/users/piotr/workspace/pandas-pijucha/pandas_dev_python2/lib/python2.7/site-packages/numpy/core/fromnumeric.py:825: RuntimeWarning: tp_compare didn't return -1 or -2 for exception
a.sort(axis, kind, order)
Out[4]: array([1, 2, datetime.datetime(2016, 7, 19, 9, 49, 28, 214675), 0, 3], dtype=object)
In [6]: np.__version__
Out[6]: '1.11.0'
```
Ipython probably interferes here because in pure python2.7 I'm getting
```
TypeError: can't compare datetime.datetime to int
```
In the old code in [factorize](https://github.com/pydata/pandas/blob/006bd0b1c2f3ff183c1834a27305a1a3039011d8/pandas/core/algorithms.py#L221-L225), there was a list comprehension similar to this:
``` python
ordered = [np.sort(np.array([e for e in arr if f(e)], dtype=object))
for f in [lambda x: True, lambda x: False]]
```
I haven't caught it precisely but it looks as if it sometimes swallowed an exception. (New code in [`safe_sort`](https://github.com/pydata/pandas/blob/b225cacb1d2a34e3c4041533a0590133098756fa/pandas/core/algorithms.py#L211-L215) is simpler - sorts each of the two arrays separately, but still with np.sort.)
It looks to me that `Categorical.from_array(arr, ordered=True)` should always raise now. And maybe test_constructor_unsortable from test_categorical.py needs to be rewritten.
---
### Weird numpy behaviour
I tested numpy behaviour for several versions between 1.7 and 1.11 in python 2.7, both in a script and interactive python (not ipython).
#### Script
Running the following script:
``` python
from datetime import datetime
import numpy as np
import sys
print sys.version
print np.__version__
arr = np.array([1, 2, datetime.now(), 0, 3], dtype=object)
arr2 = np.sort(arr)
print arr2
```
gives for **numpy < 1.10**:
``` python
2.7.11 (default, Mar 30 2016, 15:33:06)
[GCC 5.3.0 20151204 (release)]
1.9.1
Traceback (most recent call last):
File "/home/users/piotr/workspace/pandas-tests/unsortable2.py", line 9, in <module>
arr2 = np.sort(arr)
File "/home/users/piotr/workspace/numpy/numpy_dev_py2/lib/python2.7/site-packages/numpy/core/fromnumeric.py", line 791, in sort
a.sort(axis, kind, order)
TypeError: can't compare datetime.datetime to int
```
and for **numpy >= 1.10**:
``` python
2.7.11 (default, Mar 30 2016, 15:33:06)
[GCC 5.3.0 20151204 (release)]
1.11.0
/home/users/piotr/workspace/numpy/numpy_dev_py2/lib/python2.7/site-packages/numpy/core/fromnumeric.py:825: RuntimeWarning: tp_compare didn't return -1 or -2 for exception
a.sort(axis, kind, order)
Traceback (most recent call last):
File "/home/users/piotr/workspace/pandas-tests/unsortable2.py", line 10, in <module>
print arr2
File "/home/users/piotr/workspace/numpy/numpy_dev_py2/lib/python2.7/site-packages/numpy/core/numeric.py", line 1869, in array_str
return array2string(a, max_line_width, precision, suppress_small, ' ', "", str)
File "/home/users/piotr/workspace/numpy/numpy_dev_py2/lib/python2.7/site-packages/numpy/core/arrayprint.py", line 442, in array2string
elif reduce(product, a.shape) == 0:
TypeError: can't compare datetime.datetime to int
```
The exception is raised in the line following `arr2 = np.sort(arr)`.
When I remove `print arr2` from the script, I'm getting "exception ignored":
``` python
2.7.11 (default, Mar 30 2016, 15:33:06)
[GCC 5.3.0 20151204 (release)]
1.11.0
/home/users/piotr/workspace/numpy/numpy_dev_py2/lib/python2.7/site-packages/numpy/core/fromnumeric.py:825: RuntimeWarning: tp_compare didn't return -1 or -2 for exception
a.sort(axis, kind, order)
Exception TypeError: "can't compare datetime.datetime to int" in <module 'threading' from '/usr/lib64/python2.7/threading.pyc'> ignored
```
#### Interactive mode
In the interactive mode (not Ipython):
For **numpy < 1.10**:
``` python
>>> sys.version
'2.7.11 (default, Mar 30 2016, 15:33:06) \n[GCC 5.3.0 20151204 (release)]'
>>> np.__version__
'1.9.1'
>>> arr = np.array([1, 2, datetime.now(), 0, 3], dtype=object)
>>> np.sort(arr)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/users/piotr/workspace/numpy/numpy_dev_py2/lib/python2.7/site-packages/numpy/core/fromnumeric.py", line 791, in sort
a.sort(axis, kind, order)
TypeError: can't compare datetime.datetime to int
>>> arr2 = np.sort(arr)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/users/piotr/workspace/numpy/numpy_dev_py2/lib/python2.7/site-packages/numpy/core/fromnumeric.py", line 791, in sort
a.sort(axis, kind, order)
TypeError: can't compare datetime.datetime to int
>>> order = [np.sort(np.array([e for e in arr if f(e)], dtype=object)) for f in [lambda x: True, lambda x: False]]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/users/piotr/workspace/numpy/numpy_dev_py2/lib/python2.7/site-packages/numpy/core/fromnumeric.py", line 791, in sort
a.sort(axis, kind, order)
TypeError: can't compare datetime.datetime to int
```
For **numpy >= 1.10**:
``` python
>>> sys.version
'2.7.11 (default, Mar 30 2016, 15:33:06) \n[GCC 5.3.0 20151204 (release)]
>>> np.__version__
'1.11.0'
>>> arr = np.array([1, 2, datetime.now(), 0, 3], dtype=object)
>>> np.sort(arr)
/home/users/piotr/workspace/numpy/numpy_dev_py2/lib/python2.7/site-packages/numpy/core/fromnumeric.py:825: RuntimeWarning: tp_compare didn't return -1 or -2 for exception
a.sort(axis, kind, order)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/users/piotr/workspace/numpy/numpy_dev_py2/lib/python2.7/site-packages/numpy/core/numeric.py", line 1807, in array_repr
', ', "array(")
File "/home/users/piotr/workspace/numpy/numpy_dev_py2/lib/python2.7/site-packages/numpy/core/arrayprint.py", line 442, in array2string
elif reduce(product, a.shape) == 0:
TypeError: can't compare datetime.datetime to int
>>> arr2 = np.sort(arr)
>>> arr2
TypeError: can't compare datetime.datetime to int
>>> print(arr2)
[1 2 datetime.datetime(2016, 7, 20, 0, 24, 6, 50903) 0 3]
>>> arr2
array([1, 2, datetime.datetime(2016, 7, 20, 0, 24, 6, 50903), 0, 3], dtype=object)
>>> order = [np.sort(np.array([e for e in arr if f(e)], dtype=object)) for f in [lambda x: True, lambda x: False]]
>>> order
[array([1, 2, datetime.datetime(2016, 7, 20, 0, 24, 6, 50903), 0, 3], dtype=object), array([], dtype=object)]
```
(I pasted literally from a console, line by line, adding only empty lines for clarity. Calls to `arr2` puzzle me.)
A behaviour for the above list comprehension may depend on whether `np.sort` raises or not on the second array in the list (here, empty).
### `safe_sort`
Calling `safe_sort` on `arr` always raises. (But I don't really know why.)
``` python
>>> np.__version__
'1.11.0'
>>> sys.version
'2.7.11 (default, Mar 30 2016, 15:33:06) \n[GCC 5.3.0 20151204 (release)]'
>>> pd.__version__
u'0.18.1+221.g8acfad3'
>>> pd.core.algorithms.safe_sort(arr)
/home/users/piotr/workspace/numpy/numpy_dev_py2/lib/python2.7/site-packages/pandas-0.18.1+221.g8acfad3-py2.7-linux-x86_64.egg/pandas/core/algorithms.py:223: RuntimeWarning: tp_compare didn't return -1 or -2 for exception
sorter = values.argsort()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/users/piotr/workspace/numpy/numpy_dev_py2/lib/python2.7/site-packages/pandas-0.18.1+221.g8acfad3-py2.7-linux-x86_64.egg/pandas/core/algorithms.py", line 227, in safe_sort
ordered = sort_mixed(values)
File "/home/users/piotr/workspace/numpy/numpy_dev_py2/lib/python2.7/site-packages/pandas-0.18.1+221.g8acfad3-py2.7-linux-x86_64.egg/pandas/core/algorithms.py", line 214, in sort_mixed
strs = np.sort(values[str_pos])
File "/home/users/piotr/workspace/numpy/numpy_dev_py2/lib/python2.7/site-packages/numpy/core/fromnumeric.py", line 824, in sort
a = asanyarray(a).copy(order="K")
TypeError: can't compare datetime.datetime to int
```
---
I didn't test much in ipython, but it also (at least sometimes) swallows an exception and returns a partially sorted `arr2`, but prints a warning (as above).
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} | 7 | 2016-07-20T10:33:07Z | 2019-07-29T16:12:43Z | 2019-07-29T16:12:43Z | CONTRIBUTOR | null | so if anyone is interested: https://github.com/matthew-brett/multibuild/tree/7abbd01f230be6067842f0510a98b06a6320ddf9
hosts the builder for the wheels, it does a 32-build on linux via a docker image. So we could just directly pull that same image and build (of master).
cc @matthew-brett
xref #13584
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} | 15 | 2016-07-20T11:59:15Z | 2016-07-26T10:52:16Z | 2016-07-26T10:51:50Z | MEMBER | null | - [x] closes #13399
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
not sure what the `setup.py` 's best practice be...
CC: @gfyoung
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} | 18 | 2016-07-20T12:50:45Z | 2016-07-24T14:01:53Z | 2016-07-21T12:08:26Z | NONE | null | This code segfaults:
``` python
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(3, 3), index='A B C'.split())
pd.DataFrame(np.empty_like(df.index))
```
## INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-91-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: 0.7.2
IPython: 5.0.0
sphinx: None
patsy: 0.4.1
dateutil: 2.3
pytz: 2016.4
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
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} | 2 | 2016-07-20T15:01:00Z | 2016-07-20T17:29:35Z | 2016-07-20T15:24:08Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
``` python
import datetime
import pandas as pd
df = pd.DataFrame()
df['string_date'] = ['2015-01-01', '']
df['timestamp'] = df.string_date.values.astype("datetime64[M]")
df.loc[df.timestamp.isnull(), 'timestamp'] = datetime.datetime.max
```
Error
```
OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 9999-12-31 23:59:59
```
#### Expected Output
No error
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.5.0-2-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.2
setuptools: 20.10.1
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 4.0.3
sphinx: 1.3.6
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.0
xlrd: 0.9.4
xlwt: 0.7.5
xlsxwriter: 0.7.3
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.999
httplib2: 0.9.1
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
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} | 3 | 2016-07-20T16:06:50Z | 2018-09-15T12:37:25Z | 2018-09-15T12:37:25Z | NONE | null | DataFrame.xs() returns a value error if a non-unique multi-index is used and the `level` kwarg is called. However, if the `level` karg is not called, or a tuple is passed the function behaves normally.
```
import sys
if sys.version[0] == '2':
from StringIO import StringIO
if sys.version[0] == '3':
from io import StringIO
import pandas as pd
sstring = """\
m,p,tstep,value,jday,normed_value,datetime
6,407,0,1,564.5,5.75,1964-07-18 12:00:00
6,407,0,1,564.5,5.75,1964-07-18 12:00:00
7,407,0,1,564.5,5.75,1964-07-18 12:00:00
8,408,0,1,564.5,6.75,1964-07-18 12:00:00
"""
subset = pd.read_csv(StringIO(sstring),
index_col=['m', 'p'],
parse_dates=['datetime'])
```
#### Example output of various use cases
##### Working example 1
```
subset.xs(6)
tstep value jday normed_value datetime
p
407 0 1 564.5 5.75 1964-07-18 12:00:00
407 0 1 564.5 5.75 1964-07-18 12:00:00
```
##### Working example 2
```
subset.xs((6,), level=['m'])
tstep value jday normed_value datetime
p
407 0 1 564.5 5.75 1964-07-18 12:00:00
407 0 1 564.5 5.75 1964-07-18 12:00:00
```
##### Working example 3
```
subset.xs((6, 407), level=['m', 'p'])
tstep value jday normed_value datetime
m p
6 407 0 1 564.5 5.75 1964-07-18 12:00:00
407 0 1 564.5 5.75 1964-07-18 12:00:00
```
##### Value error
I would expect this to return the same as example 1 or 2
```
subset.xs(6, level='m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-1-1d10441983d6> in <module>()
20 parse_dates=['datetime'])
21
---> 22 subset.xs(6, level='m')
C:\Anaconda3\lib\site-packages\pandas\core\generic.py in xs(self, key, axis, level, copy, drop_level)
1734
1735 result = self.ix[indexer]
-> 1736 setattr(result, result._get_axis_name(axis), new_ax)
1737 return result
1738
C:\Anaconda3\lib\site-packages\pandas\core\generic.py in __setattr__(self, name, value)
2683 try:
2684 object.__getattribute__(self, name)
-> 2685 return object.__setattr__(self, name, value)
2686 except AttributeError:
2687 pass
pandas\src\properties.pyx in pandas.lib.AxisProperty.__set__ (pandas\lib.c:44748)()
C:\Anaconda3\lib\site-packages\pandas\core\generic.py in _set_axis(self, axis, labels)
426
427 def _set_axis(self, axis, labels):
--> 428 self._data.set_axis(axis, labels)
429 self._clear_item_cache()
430
C:\Anaconda3\lib\site-packages\pandas\core\internals.py in set_axis(self, axis, new_labels)
2633 raise ValueError('Length mismatch: Expected axis has %d elements, '
2634 'new values have %d elements' %
-> 2635 (old_len, new_len))
2636
2637 self.axes[axis] = new_labels
ValueError: Length mismatch: Expected axis has 4 elements, new values have 2 elements
```
#### output of `pd.show_versions()`
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
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https://api.github.com/repos/pandas-dev/pandas/issues/13720 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13720/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13720/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13720/events | https://github.com/pandas-dev/pandas/issues/13720 | 166,621,622 | MDU6SXNzdWUxNjY2MjE2MjI= | 13,720 | Weird behaviour when creating `Series` with datetimes parsed from strings | {
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} | 5 | 2016-07-20T16:14:08Z | 2016-07-21T07:33:28Z | 2016-07-20T17:29:09Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
x = [u'2016-07-19T00:00:00.000Z',
u'2016-07-19T01:00:00.000Z',
u'2016-07-19T02:00:00.000Z',
u'2016-07-19T03:00:00.000Z',
u'2016-07-19T04:00:00.000Z',
u'2016-07-19T05:00:00.000Z',
u'2016-07-19T06:00:00.000Z',
u'2016-07-19T07:00:00.000Z',
u'2016-07-19T08:00:00.000Z',
u'2016-07-19T09:00:00.000Z',
u'2016-07-19T10:00:00.000Z',
u'2016-07-19T11:00:00.000Z',
u'2016-07-19T12:00:00.000Z',
u'2016-07-19T13:00:00.000Z',
u'2016-07-19T14:00:00.000Z',
u'2016-07-19T15:00:00.000Z',
u'2016-07-19T16:00:00.000Z',
u'2016-07-19T17:00:00.000Z',
u'2016-07-19T18:00:00.000Z',
u'2016-07-19T19:00:00.000Z',
u'2016-07-19T20:00:00.000Z',
u'2016-07-19T21:00:00.000Z',
u'2016-07-19T22:00:00.000Z',
u'2016-07-19T23:00:00.000Z']
print pd.Series([dateutil.parser.parse(y) for y in x])
print pd.Series([dateutil.parser.parse(y) for y in (x + x + x)])
```
#### Expected Output
```
0 2016-07-19 00:00:00+00:00
1 2016-07-19 01:00:00+00:00
2 2016-07-19 02:00:00+00:00
3 2016-07-19 03:00:00+00:00
4 2016-07-19 04:00:00+00:00
5 2016-07-19 05:00:00+00:00
6 2016-07-19 06:00:00+00:00
7 2016-07-19 07:00:00+00:00
8 2016-07-19 08:00:00+00:00
9 2016-07-19 09:00:00+00:00
10 2016-07-19 10:00:00+00:00
11 2016-07-19 11:00:00+00:00
12 2016-07-19 12:00:00+00:00
13 2016-07-19 13:00:00+00:00
14 2016-07-19 14:00:00+00:00
15 2016-07-19 15:00:00+00:00
16 2016-07-19 16:00:00+00:00
17 2016-07-19 17:00:00+00:00
18 2016-07-19 18:00:00+00:00
19 2016-07-19 19:00:00+00:00
20 2016-07-19 20:00:00+00:00
21 2016-07-19 21:00:00+00:00
22 2016-07-19 22:00:00+00:00
23 2016-07-19 23:00:00+00:00
dtype: datetime64[ns, tzlocal()]
0 2016-07-19 00:00:00
1 2016-07-19 01:00:00
2 2016-07-19 02:00:00
3 2016-07-19 03:00:00
4 2016-07-19 04:00:00
5 2016-07-19 05:00:00
6 2016-07-19 06:00:00
7 2016-07-19 07:00:00
8 2016-07-19 08:00:00
9 2016-07-19 09:00:00
10 2016-07-19 10:00:00
11 2016-07-19 11:00:00
12 2016-07-19 12:00:00
13 2016-07-19 13:00:00
14 2016-07-19 14:00:00
15 2016-07-19 15:00:00
16 2016-07-19 16:00:00
17 2016-07-19 17:00:00
18 2016-07-19 18:00:00
19 2016-07-19 19:00:00
20 2016-07-19 20:00:00
21 2016-07-19 21:00:00
22 2016-07-19 22:00:00
23 2016-07-19 23:00:00
24 2016-07-19 00:00:00
25 2016-07-19 01:00:00
26 2016-07-19 02:00:00
27 2016-07-19 03:00:00
28 2016-07-19 04:00:00
29 2016-07-19 05:00:00
...
42 2016-07-19 18:00:00
43 2016-07-19 19:00:00
44 2016-07-19 20:00:00
45 2016-07-19 21:00:00
46 2016-07-19 22:00:00
47 2016-07-19 23:00:00
48 2016-07-19 00:00:00
49 2016-07-19 01:00:00
50 2016-07-19 02:00:00
51 2016-07-19 03:00:00
52 2016-07-19 04:00:00
53 2016-07-19 05:00:00
54 2016-07-19 06:00:00
55 2016-07-19 07:00:00
56 2016-07-19 08:00:00
57 2016-07-19 09:00:00
58 2016-07-19 10:00:00
59 2016-07-19 11:00:00
60 2016-07-19 12:00:00
61 2016-07-19 13:00:00
62 2016-07-19 14:00:00
63 2016-07-19 15:00:00
64 2016-07-19 16:00:00
65 2016-07-19 17:00:00
66 2016-07-19 18:00:00
67 2016-07-19 19:00:00
68 2016-07-19 20:00:00
69 2016-07-19 21:00:00
70 2016-07-19 22:00:00
71 2016-07-19 23:00:00
dtype: datetime64[ns, tzlocal()]
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 15.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.1
setuptools: 21.2.1
Cython: None
numpy: 1.11.0
scipy: None
statsmodels: None
xarray: None
IPython: 3.1.0
sphinx: None
patsy: None
dateutil: 2.5.2
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.999
httplib2: 0.9.2
apiclient: 1.5.1
sqlalchemy: None
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.7.3
boto: None
pandas_datareader: None
```
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- [x] closed by #13736
```
======================================================================
ERROR: test_nanosecond_field (pandas.tseries.tests.test_timeseries.TestDatetime64)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\dev\pandas\pandas\tseries\tests\test_timeseries.py", line 3925, in test_nanosecond_field
dti = DatetimeIndex(np.arange(10))
File "C:\dev\pandas\pandas\util\decorators.py", line 91, in wrapper
return func(*args, **kwargs)
File "C:\dev\pandas\pandas\tseries\index.py", line 337, in __new__
if isinstance(subarr, DatetimeIndex):
UnboundLocalError: local variable 'subarr' referenced before assignment
```
- [x] closed by #13732
```
======================================================================
FAIL: test_next (pandas.io.tests.test_common.TestMMapWrapper)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\dev\pandas\pandas\io\tests\test_common.py", line 141, in test_next
self.assertEqual(next_line, line)
AssertionError: 'a,b,c\r\n' != 'a,b,c\n'
- a,b,c
? -
+ a,b,c
```
I think some @sinhrks changes for the first
not sure about the 2nd, IIRC @pijucha saw this on windows as well
@gfyoung any idea?d
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- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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} | 8 | 2016-07-20T17:00:53Z | 2016-07-24T16:03:34Z | 2016-07-24T16:00:24Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Before this commit pd.Timedelta raised ValueError
when None is passed.
This behavior is inconsistent because
- pd.Timestamp(None) and pd.Period(None) return NaT
- pd.Timedelta returns NaT if '', 'nat', 'NAT' or np.nan
is passed
This PR is related to https://github.com/pydata/pandas/pull/13687#issuecomment-233204955
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} | 6 | 2016-07-20T18:02:42Z | 2016-07-24T18:03:59Z | 2016-07-24T18:03:22Z | MEMBER | null | `io.data` and `io.web` have been deprecated since 0.17 and moved to the `pandas-datareader` project. I am not necessarily saying we should already remove the code (although we could, certainly given that the tests cause failures from time to time, but we can also wait until 0.20), but my proposal is to already remove the docs (and only include a link to the pandas-datareader docs).
Reason: some of the examples are failing anyways (due to issues with yahoo), our docs already take quite a while to build, and the exact same content is available in the pandas-datareader docs.
Any objections?
@jreback @davidastephens @sinhrks @femtotrader
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} | 2 | 2016-07-20T19:41:23Z | 2016-07-20T20:20:34Z | 2016-07-20T20:18:04Z | CONTRIBUTOR | null | I find it necessary to specify an explicit date parser when reading certain CSV files because `read_csv(..., error_bad_lines=True)` does not recognize errors that fall within the index column; it falls back to string-indexing instead. I've dealt with this by specifying `coerce=True`, but that method is deprecated since #10653 combined the `coerce` and `errors` keywords. Now, neither method appears to work for me.
Here is the test snippet:
``` python
import pandas as pd
try:
from StringIO import StringIO
except ImportError:
from io import StringIO
fakefile = """\
"TOA5","indoorair(1.2.3)","CR1000","47257","CR1000.Std.28.02","CPU:indoorair.cr1","37538","tsdata"
"TIMESTAMP","RECORD","dc1100_pm_small","dc1100_pm_large","li840a_CO2","li840a_H2O","li840a_cell_T","li840a_cell_P","li840a_dew_T","li840a_pwr_src","typeK_amb_T","logger_panel_T"
"TS","RN","counts/m^3","counts/m^3","ppmv","ppthv","degC","mbar","degC","Vdc","degC","degC"
"","","Smp","Smp","Avg","Avg","Avg","Avg","Avg","Avg","Avg","Avg"
"2016-04-06 13:20:00",5756,1373740,42377,1149.936,10.30014,51.20879,928.455,6.267447,24.1189,21.4,25.93
"2016-04-06 13:21:00",5757,1437306,56503,1147.533,10.30781,51.20538,928.4556,6.278251,24.1182,21.43,25.94
"2016-04-06 13:22:00",5758,1454964,49440,1146.63,10.31073,51.20726,928.449,6.282249,24.11856,21.43,25.95
"2016-04-06 13:23:00",5759,1433775,52972,1145.948,10.30542,51.2077,928.4606,6.274971,24.11839,21.4,25.94
"2016-04-06 13:24:00",5760,1483216,56503,1145.541,10.30182,51.20682,928.451,6.26975,24.11856,21.4,25.95"""
df = pd.read_csv(StringIO(fakefile),
index_col=0,
parse_dates=True,
date_parser=lambda ts: pd.to_datetime(ts, '%Y-%m-%d %H:%M:%S',
errors='coerce'),
header=1,
skiprows=[2,3],
na_values=['NAN', -7999, 7999],
keep_default_na=False)
```
#### Machine 1
Here's the traceback I received on my Linux Mint box (before reducing to MWE):
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-11-dca3f9d3edf1> in <module>()
----> 1 indoor_air = read_TOA5('pullman04/winter16/indoor-rack/indoor-air_tsdata.dat')
<ipython-input-10-406a7964b57c> in read_TOA5(fpath)
12 # https://s.campbellsci.com/documents/us/manuals/cr1000.pdf
13 ## HINT csv parser consumes the dbl-quotes around NANs
---> 14 na_values=['NAN', -7999, 7999], keep_default_na=False)
15 #if len(df.index.get_duplicates()): # if dup rows found
16 # df = df.groupby(level=0).first() # discard copies
/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)
560 skip_blank_lines=skip_blank_lines)
561
--> 562 return _read(filepath_or_buffer, kwds)
563
564 parser_f.__name__ = name
/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc in _read(filepath_or_buffer, kwds)
323 return parser
324
--> 325 return parser.read()
326
327 _parser_defaults = {
/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc in read(self, nrows)
813 raise ValueError('skip_footer not supported for iteration')
814
--> 815 ret = self._engine.read(nrows)
816
817 if self.options.get('as_recarray'):
/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc in read(self, nrows)
1385
1386 names, data = self._do_date_conversions(names, data)
-> 1387 index, names = self._make_index(data, alldata, names)
1388
1389 # maybe create a mi on the columns
/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc in _make_index(self, data, alldata, columns, indexnamerow)
1028 elif not self._has_complex_date_col:
1029 index = self._get_simple_index(alldata, columns)
-> 1030 index = self._agg_index(index)
1031
1032 elif self._has_complex_date_col:
/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc in _agg_index(self, index, try_parse_dates)
1109
1110 if (try_parse_dates and self._should_parse_dates(i)):
-> 1111 arr = self._date_conv(arr)
1112
1113 col_na_values = self.na_values
/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc in converter(*date_cols)
2286 errors='ignore')
2287 except Exception:
-> 2288 return generic_parser(date_parser, *date_cols)
2289
2290 return converter
/usr/local/lib/python2.7/dist-packages/pandas/io/date_converters.pyc in generic_parser(parse_func, *cols)
36 for i in range(N):
37 args = [c[i] for c in cols]
---> 38 results[i] = parse_func(*args)
39
40 return results
<ipython-input-10-406a7964b57c> in <lambda>(ts)
6 # explicit parser + coerce to prevent fallback to string-indexing
7 date_parser=lambda ts: pd.to_datetime(ts, '%Y-%m-%d %H:%M:%S',
----> 8 errors='coerce'),
9 header=1, # 1st line metadata 2nd headers
10 skiprows=[2,3], # 3rd units 4th agg type
/usr/local/lib/python2.7/dist-packages/pandas/util/decorators.pyc in wrapper(*args, **kwargs)
89 else:
90 kwargs[new_arg_name] = new_arg_value
---> 91 return func(*args, **kwargs)
92 return wrapper
93 return _deprecate_kwarg
TypeError: to_datetime() got multiple values for keyword argument 'errors'
```
using pandas 0.18.1 (freshly updated) with Python 2.7 (_Sorry, I don't have `pd.show_versions()` output..._)
#### Machine 2
When I run the same snippet on Windows 7 with pandas 0.18.1, Python 3.5.1:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py in converter(*date_cols)
2275 result = tools.to_datetime(
-> 2276 date_parser(*date_cols), errors='ignore')
2277 if isinstance(result, datetime.datetime):
<ipython-input-11-5805c9b0f012> in <lambda>(ts)
4 date_parser=lambda ts: pd.to_datetime(ts, '%Y-%m-%d %H:%M:%S',
----> 5 errors='coerce'),
6 header=1,
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\util\decorators.py in wrapper(*args, **kwargs)
90 kwargs[new_arg_name] = new_arg_value
---> 91 return func(*args, **kwargs)
92 return wrapper
TypeError: to_datetime() got multiple values for argument 'errors'
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py in converter(*date_cols)
2284 parser=date_parser,
-> 2285 dayfirst=dayfirst),
2286 errors='ignore')
pandas\src\inference.pyx in pandas.lib.try_parse_dates (pandas\lib.c:57884)()
pandas\src\inference.pyx in pandas.lib.try_parse_dates (pandas\lib.c:57802)()
<ipython-input-11-5805c9b0f012> in <lambda>(ts)
4 date_parser=lambda ts: pd.to_datetime(ts, '%Y-%m-%d %H:%M:%S',
----> 5 errors='coerce'),
6 header=1,
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\util\decorators.py in wrapper(*args, **kwargs)
90 kwargs[new_arg_name] = new_arg_value
---> 91 return func(*args, **kwargs)
92 return wrapper
TypeError: to_datetime() got multiple values for argument 'errors'
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-11-5805c9b0f012> in <module>()
7 skiprows=[2,3],
8 na_values=['NAN', -7999, 7999],
----> 9 keep_default_na=False)
10 df
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)
560 skip_blank_lines=skip_blank_lines)
561
--> 562 return _read(filepath_or_buffer, kwds)
563
564 parser_f.__name__ = name
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py in _read(filepath_or_buffer, kwds)
323 return parser
324
--> 325 return parser.read()
326
327 _parser_defaults = {
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py in read(self, nrows)
813 raise ValueError('skip_footer not supported for iteration')
814
--> 815 ret = self._engine.read(nrows)
816
817 if self.options.get('as_recarray'):
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py in read(self, nrows)
1385
1386 names, data = self._do_date_conversions(names, data)
-> 1387 index, names = self._make_index(data, alldata, names)
1388
1389 # maybe create a mi on the columns
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py in _make_index(self, data, alldata, columns, indexnamerow)
1028 elif not self._has_complex_date_col:
1029 index = self._get_simple_index(alldata, columns)
-> 1030 index = self._agg_index(index)
1031
1032 elif self._has_complex_date_col:
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py in _agg_index(self, index, try_parse_dates)
1109
1110 if (try_parse_dates and self._should_parse_dates(i)):
-> 1111 arr = self._date_conv(arr)
1112
1113 col_na_values = self.na_values
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py in converter(*date_cols)
2286 errors='ignore')
2287 except Exception:
-> 2288 return generic_parser(date_parser, *date_cols)
2289
2290 return converter
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\date_converters.py in generic_parser(parse_func, *cols)
36 for i in range(N):
37 args = [c[i] for c in cols]
---> 38 results[i] = parse_func(*args)
39
40 return results
<ipython-input-11-5805c9b0f012> in <lambda>(ts)
3 parse_dates=True,
4 date_parser=lambda ts: pd.to_datetime(ts, '%Y-%m-%d %H:%M:%S',
----> 5 errors='coerce'),
6 header=1,
7 skiprows=[2,3],
C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\util\decorators.py in wrapper(*args, **kwargs)
89 else:
90 kwargs[new_arg_name] = new_arg_value
---> 91 return func(*args, **kwargs)
92 return wrapper
93 return _deprecate_kwarg
TypeError: to_datetime() got multiple values for argument 'errors'
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 23 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
```
#### Machine 2 (again)
Strangely, I get the same error using a different (older) distribution on the same Win7 machine:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-1-4ac37d43a03f> in <module>()
23 skiprows=[2,3],
24 na_values=['NAN', -7999, 7999],
---> 25 keep_default_na=False)
C:\WinPython\python-2.7.9\lib\site-packages\pandas\io\parsers.pyc in parser_f(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, na_fvalues, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, float_precision, nrows, iterator, chunksize, verbose, encoding, squeeze, mangle_dupe_cols, tupleize_cols, infer_datetime_format, skip_blank_lines)
463 skip_blank_lines=skip_blank_lines)
464
--> 465 return _read(filepath_or_buffer, kwds)
466
467 parser_f.__name__ = name
C:\WinPython\python-2.7.9\lib\site-packages\pandas\io\parsers.pyc in _read(filepath_or_buffer, kwds)
249 return parser
250
--> 251 return parser.read()
252
253 _parser_defaults = {
C:\WinPython\python-2.7.9\lib\site-packages\pandas\io\parsers.pyc in read(self, nrows)
708 raise ValueError('skip_footer not supported for iteration')
709
--> 710 ret = self._engine.read(nrows)
711
712 if self.options.get('as_recarray'):
C:\WinPython\python-2.7.9\lib\site-packages\pandas\io\parsers.pyc in read(self, nrows)
1210
1211 names, data = self._do_date_conversions(names, data)
-> 1212 index, names = self._make_index(data, alldata, names)
1213
1214 # maybe create a mi on the columns
C:\WinPython\python-2.7.9\lib\site-packages\pandas\io\parsers.pyc in _make_index(self, data, alldata, columns, indexnamerow)
876 elif not self._has_complex_date_col:
877 index = self._get_simple_index(alldata, columns)
--> 878 index = self._agg_index(index)
879
880 elif self._has_complex_date_col:
C:\WinPython\python-2.7.9\lib\site-packages\pandas\io\parsers.pyc in _agg_index(self, index, try_parse_dates)
957
958 if (try_parse_dates and self._should_parse_dates(i)):
--> 959 arr = self._date_conv(arr)
960
961 col_na_values = self.na_values
C:\WinPython\python-2.7.9\lib\site-packages\pandas\io\parsers.pyc in converter(*date_cols)
2048 dayfirst=dayfirst)
2049 except Exception:
-> 2050 return generic_parser(date_parser, *date_cols)
2051
2052 return converter
C:\WinPython\python-2.7.9\lib\site-packages\pandas\io\date_converters.pyc in generic_parser(parse_func, *cols)
36 for i in range(N):
37 args = [c[i] for c in cols]
---> 38 results[i] = parse_func(*args)
39
40 return results
<ipython-input-1-4ac37d43a03f> in <lambda>(ts)
19 parse_dates=True,
20 date_parser=lambda ts: pd.to_datetime(ts, '%Y-%m-%d %H:%M:%S',
---> 21 errors='coerce'),
22 header=1,
23 skiprows=[2,3],
TypeError: to_datetime() got multiple values for keyword argument 'errors'
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 23 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.15.2
nose: 1.3.4
Cython: 0.21.1
numpy: 1.8.2
scipy: 0.14.0
statsmodels: 0.6.1
IPython: 2.3.1
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.3
pytz: 2014.10
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.2
openpyxl: None
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.6.4
lxml: 3.4.1
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: 2.5.2
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
```
#### Machine 3
Different physical hardware, same test snippet and same environment (Anaconda3-4.1.0-Windows-x86_64)...
...produced the same traceback.
#### The Hang-Up
I would have just ignored the deprecation warning and continued to use `coerce=True`, except now I can't get back into a working state...
Reverting to `coerce=True` does not halt the "multiple values for argument 'errors' traceback:
```
import pandas as pd
try:
from StringIO import StringIO
except ImportError:
from io import StringIO
fakefile = """\
"TOA5","indoorair(1.2.3)","CR1000","47257","CR1000.Std.28.02","CPU:indoorair.cr1","37538","tsdata"
"TIMESTAMP","RECORD","dc1100_pm_small","dc1100_pm_large","li840a_CO2","li840a_H2O","li840a_cell_T","li840a_cell_P","li840a_dew_T","li840a_pwr_src","typeK_amb_T","logger_panel_T"
"TS","RN","counts/m^3","counts/m^3","ppmv","ppthv","degC","mbar","degC","Vdc","degC","degC"
"","","Smp","Smp","Avg","Avg","Avg","Avg","Avg","Avg","Avg","Avg"
"2016-04-06 13:20:00",5756,1373740,42377,1149.936,10.30014,51.20879,928.455,6.267447,24.1189,21.4,25.93
"2016-04-06 13:21:00",5757,1437306,56503,1147.533,10.30781,51.20538,928.4556,6.278251,24.1182,21.43,25.94
"2016-04-06 13:22:00",5758,1454964,49440,1146.63,10.31073,51.20726,928.449,6.282249,24.11856,21.43,25.95
"2016-04-06 13:23:00",5759,1433775,52972,1145.948,10.30542,51.2077,928.4606,6.274971,24.11839,21.4,25.94
"2016-04-06 13:24:00",5760,1483216,56503,1145.541,10.30182,51.20682,928.451,6.26975,24.11856,21.4,25.95"""
df = pd.read_csv(StringIO(fakefile),
index_col=0,
parse_dates=True,
date_parser=lambda ts: pd.to_datetime(ts, '%Y-%m-%d %H:%M:%S',
coerce=True),
header=1,
skiprows=[2,3],
na_values=['NAN', -7999, 7999],
keep_default_na=False)
```
produces
```
K:\temp.py:24: FutureWarning: the coerce=True keyword is deprecated, use errors='coerce' instead
coerce=True),
Traceback (most recent call last):
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py", line 2276, in converter
date_parser(*date_cols), errors='ignore')
File "K:\temp.py", line 24, in <lambda>
coerce=True),
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\util\decorators.py", line 91, in wrapper
return func(*args, **kwargs)
TypeError: to_datetime() got multiple values for argument 'errors'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py", line 2285, in converter
dayfirst=dayfirst),
File "pandas\src\inference.pyx", line 841, in pandas.lib.try_parse_dates (pandas\lib.c:57884)
File "pandas\src\inference.pyx", line 838, in pandas.lib.try_parse_dates (pandas\lib.c:57802)
File "K:\temp.py", line 24, in <lambda>
coerce=True),
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\util\decorators.py", line 91, in wrapper
return func(*args, **kwargs)
TypeError: to_datetime() got multiple values for argument 'errors'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "K:\temp.py", line 28, in <module>
keep_default_na=False)
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py", line 562, in parser_f
return _read(filepath_or_buffer, kwds)
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py", line 325, in _read
return parser.read()
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py", line 815, in read
ret = self._engine.read(nrows)
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py", line 1387, in read
index, names = self._make_index(data, alldata, names)
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py", line 1030, in _make_index
index = self._agg_index(index)
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py", line 1111, in _agg_index
arr = self._date_conv(arr)
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py", line 2288, in converter
return generic_parser(date_parser, *date_cols)
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\date_converters.py", line 38, in generic_parser
results[i] = parse_func(*args)
File "K:\temp.py", line 24, in <lambda>
coerce=True),
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\util\decorators.py", line 91, in wrapper
return func(*args, **kwargs)
TypeError: to_datetime() got multiple values for argument 'errors'
```
Neither does removing _both_ `coerce` and `errors` arguments:
```
import pandas as pd
try:
from StringIO import StringIO
except ImportError:
from io import StringIO
fakefile = """\
"TOA5","indoorair(1.2.3)","CR1000","47257","CR1000.Std.28.02","CPU:indoorair.cr1","37538","tsdata"
"TIMESTAMP","RECORD","dc1100_pm_small","dc1100_pm_large","li840a_CO2","li840a_H2O","li840a_cell_T","li840a_cell_P","li840a_dew_T","li840a_pwr_src","typeK_amb_T","logger_panel_T"
"TS","RN","counts/m^3","counts/m^3","ppmv","ppthv","degC","mbar","degC","Vdc","degC","degC"
"","","Smp","Smp","Avg","Avg","Avg","Avg","Avg","Avg","Avg","Avg"
"2016-04-06 13:20:00",5756,1373740,42377,1149.936,10.30014,51.20879,928.455,6.267447,24.1189,21.4,25.93
"2016-04-06 13:21:00",5757,1437306,56503,1147.533,10.30781,51.20538,928.4556,6.278251,24.1182,21.43,25.94
"2016-04-06 13:22:00",5758,1454964,49440,1146.63,10.31073,51.20726,928.449,6.282249,24.11856,21.43,25.95
"2016-04-06 13:23:00",5759,1433775,52972,1145.948,10.30542,51.2077,928.4606,6.274971,24.11839,21.4,25.94
"2016-04-06 13:24:00",5760,1483216,56503,1145.541,10.30182,51.20682,928.451,6.26975,24.11856,21.4,25.95"""
df = pd.read_csv(StringIO(fakefile),
index_col=0,
parse_dates=True,
date_parser=lambda ts: pd.to_datetime(ts, '%Y-%m-%d %H:%M:%S'),
#errors='coerce'),
header=1,
skiprows=[2,3],
na_values=['NAN', -7999, 7999],
keep_default_na=False)
```
produces
```
Traceback (most recent call last):
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py", line 2276, in converter
date_parser(*date_cols), errors='ignore')
File "K:\temp.py", line 23, in <lambda>
date_parser=lambda ts: pd.to_datetime(ts, '%Y-%m-%d %H:%M:%S'),
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\util\decorators.py", line 91, in wrapper
return func(*args, **kwargs)
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\tseries\tools.py", line 291, in to_datetime
unit=unit, infer_datetime_format=infer_datetime_format)
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\tseries\tools.py", line 427, in _to_datetime
return _convert_listlike(arg, box, format)
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\tseries\tools.py", line 398, in _convert_listlike
require_iso8601=require_iso8601
File "pandas\tslib.pyx", line 2134, in pandas.tslib.array_to_datetime (pandas\tslib.c:41972)
File "pandas\tslib.pyx", line 2150, in pandas.tslib.array_to_datetime (pandas\tslib.c:38535)
AssertionError
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py", line 2285, in converter
dayfirst=dayfirst),
File "pandas\src\inference.pyx", line 841, in pandas.lib.try_parse_dates (pandas\lib.c:57884)
File "pandas\src\inference.pyx", line 838, in pandas.lib.try_parse_dates (pandas\lib.c:57802)
File "K:\temp.py", line 23, in <lambda>
date_parser=lambda ts: pd.to_datetime(ts, '%Y-%m-%d %H:%M:%S'),
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\util\decorators.py", line 91, in wrapper
return func(*args, **kwargs)
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\tseries\tools.py", line 291, in to_datetime
unit=unit, infer_datetime_format=infer_datetime_format)
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\tseries\tools.py", line 429, in _to_datetime
return _convert_listlike(np.array([arg]), box, format)[0]
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\tseries\tools.py", line 398, in _convert_listlike
require_iso8601=require_iso8601
File "pandas\tslib.pyx", line 2134, in pandas.tslib.array_to_datetime (pandas\tslib.c:41972)
File "pandas\tslib.pyx", line 2150, in pandas.tslib.array_to_datetime (pandas\tslib.c:38535)
AssertionError
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "K:\temp.py", line 28, in <module>
keep_default_na=False)
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py", line 562, in parser_f
return _read(filepath_or_buffer, kwds)
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py", line 325, in _read
return parser.read()
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py", line 815, in read
ret = self._engine.read(nrows)
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py", line 1387, in read
index, names = self._make_index(data, alldata, names)
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py", line 1030, in _make_index
index = self._agg_index(index)
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py", line 1111, in _agg_index
arr = self._date_conv(arr)
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\parsers.py", line 2288, in converter
return generic_parser(date_parser, *date_cols)
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\date_converters.py", line 38, in generic_parser
results[i] = parse_func(*args)
File "K:\temp.py", line 23, in <lambda>
date_parser=lambda ts: pd.to_datetime(ts, '%Y-%m-%d %H:%M:%S'),
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\util\decorators.py", line 91, in wrapper
return func(*args, **kwargs)
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\tseries\tools.py", line 291, in to_datetime
unit=unit, infer_datetime_format=infer_datetime_format)
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\tseries\tools.py", line 429, in _to_datetime
return _convert_listlike(np.array([arg]), box, format)[0]
File "C:\Users\pokeeffe\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\tseries\tools.py", line 398, in _convert_listlike
require_iso8601=require_iso8601
File "pandas\tslib.pyx", line 2134, in pandas.tslib.array_to_datetime (pandas\tslib.c:41972)
File "pandas\tslib.pyx", line 2150, in pandas.tslib.array_to_datetime (pandas\tslib.c:38535)
AssertionError
```
To be clear, I can still use `coerce=True` in older versions of pandas. For example, in 0.15.2, both of these work fine:
```
...
df = pd.read_csv(StringIO(fakefile),
index_col=0,
parse_dates=True,
date_parser=lambda ts: pd.to_datetime(ts, '%Y-%m-%d %H:%M:%S',
coerce=True),
header=1,
skiprows=[2,3],
na_values=['NAN', -7999, 7999],
keep_default_na=False)
```
```
...
df = pd.read_csv(StringIO(fakefile),
index_col=0,
parse_dates=True,
date_parser=lambda ts: pd.to_datetime(ts, '%Y-%m-%d %H:%M:%S'),
# coerce=True),
header=1,
skiprows=[2,3],
na_values=['NAN', -7999, 7999],
keep_default_na=False)
```
---
_Whew! I know that was a lot of scrolling sorry_
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} | 1 | 2016-07-20T20:57:53Z | 2016-11-07T20:52:08Z | 2016-11-07T20:52:08Z | CONTRIBUTOR | null | Wouldn't necessarily need to add everything that `read_table` accepts, but at minimum adding a couple common ones like `delim_whitespace` and `sep` would make it nicer for argument completion in interactive use.
https://github.com/pydata/pandas/blob/master/pandas/io/clipboard.py#L6
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} | 2 | 2016-07-20T21:40:47Z | 2016-07-25T11:40:10Z | 2016-07-25T11:40:10Z | CONTRIBUTOR | null | xref #13706
should deprecate `pandas.util.isleapyear`
and impement as an accessort to `DTI`, `Timestamp` and `.dt`
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} | 7 | 2016-07-20T22:10:30Z | 2016-07-20T22:27:17Z | 2016-07-20T22:25:22Z | CONTRIBUTOR | null | - [x] closes #13599
- [ ] tests added / passed
- [x] whatsnew entry
/cc @jreback
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- [x] tests added / passed
- [x] whatsnew entry
/cc @jreback
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} | 10 | 2016-07-20T23:20:27Z | 2016-08-08T14:11:31Z | 2016-08-08T14:11:31Z | NONE | null | #### Code Sample, a copy-pastable example if possible
``` python
>>> import pandas as pd
>>> pd.__version__
u'0.18.0'
>>> pd.period_range('2016-07-20', periods=4, freq='2H')
PeriodIndex(['2016-07-20 00:00', '2016-07-20 02:00', '2016-07-20 04:00',
'2016-07-20 06:00'],
dtype='int64', freq='2H')
>>> pd.period_range('2016-07-20', periods=4, freq='2H30min')
PeriodIndex(['1970-10-11 08:48', '1970-10-11 08:50', '1970-10-11 08:52',
'1970-10-11 08:54'],
dtype='int64', freq='150T')
>>> pd.period_range('2016-07-20', periods=4, freq='2H30T')
PeriodIndex(['1970-10-11 08:48', '1970-10-11 08:50', '1970-10-11 08:52',
'1970-10-11 08:54'],
dtype='int64', freq='150T')
>>> pd.period_range('2016-07-20', periods=2, freq='1D1H')
PeriodIndex(['1971-12-10 10:00', '1971-12-10 11:00'], dtype='int64', freq='25H')
```
`date_range` works fine:
```
>>> pd.date_range('2016-07-20', periods=4, freq='2H30T')
DatetimeIndex(['2016-07-20 00:00:00', '2016-07-20 02:30:00',
'2016-07-20 05:00:00', '2016-07-20 07:30:00'],
dtype='datetime64[ns]', freq='150T')
```
#### Expected Output
The `freq` is right, but I'd expect the PeriodIndex to start at 2016-07-20, not in 1970, and to display 2h30 increments, something like:
``` python
PeriodIndex(['2016-07-20 00:00', '2016-07-20 02:30', '2016-07-20 05:00',
'2016-07-20 07:30'],
dtype='int64', freq='150T')
```
Or, if `freq` that combine multiple offsets are note supported, to at least raise and error.
#### output of `pd.show_versions()`
INSTALLED VERSIONS
---
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.2
setuptools: 23.1.0
Cython: 0.24
numpy: 1.10.4
scipy: 0.17.1
statsmodels: 0.6.1
xarray: 0.7.2
IPython: 4.1.2
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.2
pytz: 2016.3
blosc: 1.2.8
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.3.4
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.999
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.12
pymysql: 0.6.7.None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
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} | 6 | 2016-07-20T23:49:21Z | 2016-07-23T15:38:02Z | 2016-07-23T15:37:53Z | CONTRIBUTOR | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Previously `df.groupby(0)[df.columns]` would fail if all column names
were integers (meaning `df.columns` was an `Int64Index`). This was
because the implementation of `__getitem__` in `SelectionMixin` was
checking for `ABCIndex` when it probably should have checked for
`ABCIndexClass`.
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} | 8 | 2016-07-21T02:45:27Z | 2016-07-24T18:47:01Z | 2016-07-24T15:44:52Z | MEMBER | null | Partially addresses #13721 by addressing the `MMapWrapper` test failure. The issue was we didn't have control over the newline character, so the test was re-written to give us that control.
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} | 2 | 2016-07-21T04:30:26Z | 2016-07-21T10:44:33Z | 2016-07-21T10:44:04Z | CONTRIBUTOR | null | - [x] closes #13714
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
- Change test for Categorical.from_array (`test_constructor_unsortable`)
- Add test for safe_sort
- Change 2.7 build to latest numpy
---
`safe_sort` always raises on a mixed int-datetime array. This is the expected behaviour. (But, for numpy >= 1.10 & python2, I still have some doubts whether it's not a manifestation of an undefined behaviour.)
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} | 9 | 2016-07-21T07:40:10Z | 2016-07-22T01:24:06Z | 2016-07-21T10:24:04Z | NONE | null | Hi,
I came across an unknown-to-me behaviour in the performance of `groupby` (I was suggested to put the question here after a thread on [stackoverflow](http://stackoverflow.com/questions/38496930/performance-of-groupby-when-dealing-with-category-type-in-pandas).
```
import pandas as pd
import numpy as np
a = np.random.choice(['a', 'A'], size=100000)
b = np.random.choice(range(10000), size=100000)
df = pd.DataFrame(data=dict(A=a, B=b))
```
The performance between the genuine `nunique` function and the 'lambda version' if it is totally different
```
%%timeit
df_ = df.groupby(by='B').A.nunique()
10 loops, best of 3: 60.9 ms per loop
```
```
%%timeit
df_ = df.groupby(by='B').agg(
dict(
A=lambda s: s.nunique()
)
)
1 loop, best of 3: 981 ms per loop
```
Is it an expected behaviour?
Best regards,
F.
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} | 9 | 2016-07-21T08:51:00Z | 2016-07-25T15:18:01Z | 2016-07-24T18:03:22Z | MEMBER | null | Closes #13724
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} | 0 | 2016-07-21T08:56:13Z | 2016-07-21T10:51:21Z | 2016-07-21T10:51:21Z | MEMBER | null | - [x] 1st issue of #13721
- [x] tests added / passed
- passed appveyor (2nd job failure is caused by the same test as #13714)
- https://ci.appveyor.com/project/sinhrks/pandas-2dhxb/build/1.0.375
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] no whatsnew, as it is a regression caused by #13692.
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} | 2 | 2016-07-21T09:03:42Z | 2016-07-21T11:00:34Z | 2016-07-21T10:54:49Z | MEMBER | null | - [x] follow-up of #12759
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
- because `Period(NaT)` had separate impl until #12579, there were some bugs related to `pd.isnull`. Now all the issues are fixed, thus added explicit tests and what's new.
- Also updated what's new to describe `Period(None)` (related to #13723).
**0.18.1:**
```
s = pd.Series([pd.Period('2011-01', freq='M'), pd.Period('NaT', freq='M')])
s.isnull()
#0 False
#1 False
# dtype: bool
s.fillna(pd.Period('2012-01', freq='M'))
#0 2011-01
#1 NaT
# dtype: object
s.dropna()
#0 2011-01
#1 NaT
# dtype: object
```
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} | 2 | 2016-07-21T09:17:55Z | 2016-07-25T15:14:32Z | 2016-07-25T15:14:32Z | CONTRIBUTOR | null | I've noticed the generated documentation for [to_csv](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_csv.html) strips backslashes in the newline character
> line_terminator : string, default ‘n’
I thought it was a case of slash stripping, but I checked [the code](https://github.com/pydata/pandas/blob/master/pandas/core/frame.py#L1346) and it's double backslashed there - so the doc generation is a bit aggressive. The [IO doc](http://pandas.pydata.org/pandas-docs/stable/io.html#writing-to-csv-format) is fine, [its code](https://github.com/pydata/pandas/blob/master/doc/source/io.rst) is the same - double backslashed.
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} | 7 | 2016-07-21T09:50:55Z | 2016-07-25T11:40:17Z | 2016-07-25T11:40:11Z | MEMBER | null | - [x] closes #13727
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
- found there is `tslib.isleapyear` also, but i don't think it is public (deprecated also).
- `pd.NaT.is_leap_year` results in `False`, as I think users want `bool` array.
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} | 11 | 2016-07-21T11:23:29Z | 2018-10-04T10:04:29Z | 2016-11-09T21:41:33Z | NONE | null | **Create MultiIndex DataFrame**
```
import pandas as pd
df = pd.DataFrame(
[
[5777, 100, 5385, 200, 5419, 4887, 100, 200],
[4849, 0, 4539, 0, 3381, 0, 0, ],
[4971, 0, 3824, 0, 4645, 3424, 0, 0, ],
[4827, 200, 3459, 300, 4552, 3153, 100, 200, ],
[5207, 0, 3670, 0, 4876, 3358, 0, 0, ],
],
index=pd.to_datetime(['2010-01-01',
'2010-01-02',
'2010-01-03',
'2010-01-04',
'2010-01-05']),
columns=pd.MultiIndex.from_tuples(
[('Portfolio A', 'GBP', 'amount'), ('Portfolio A', 'GBP', 'injection'),
('Portfolio B', 'EUR', 'amount'), ('Portfolio B', 'EUR', 'injection'),
('Portfolio A', 'USD', 'amount'), ('Portfolio A', 'USD', 'injection'),
('Portfolio B', 'JPY', 'amount'), ('Portfolio B', 'JPY', 'injection')])
).sortlevel(axis=1)
```
**Add a column at Level 2**
```
amount = df.loc[:, pd.IndexSlice[:, :, 'amount']]
inject = df.loc[:, pd.IndexSlice[:, :, 'injection']]
dav = amount - amount.shift() - inject.shift().values
dav.columns.set_levels(['daily_added_value'], level=2, inplace=True)
df = pd.concat([df, dav], axis=1).sortlevel(axis=1)
print(df.T) #transposed to fit only
```
I can repeat the _add a column at Level 2 code_ above multiple times, however if I try the following I get an error (possibly related to NAN's):
```
dav1 = df.loc[:, pd.IndexSlice[:, :, 'daily_added_value']]
amount = df.loc[:, pd.IndexSlice[:, :, 'amount']]
dr = dav1 * amount
dr.columns.set_levels(['daily_return'], level=2, inplace=True)
df = pd.concat([df, dr], axis=1).sortlevel(axis=1)
```
#### Expected Output - ( daily_returns is added to level 2 ( daily_added_value \* amount )
Note I have put zeros as expected values but the desired output is daily_added_value \* amount) I am also adding other measures at level 2 with division and shift() in the formulae. These fail with same error.
```
2010-01-01 2010-01-02 2010-01-03 \
Portfolio A GBP amount 5777.0 4849.0 4971.0
daily_added_value NaN -1028.0 122.0
injection 100.0 0.0 0.0
USD amount 5419.0 3381.0 4645.0
daily_added_value NaN -6925.0 1264.0
daily_returns 0 0 0
injection 4887.0 0.0 3424.0
Portfolio B EUR amount 5385.0 4539.0 3824.0
daily_added_value NaN -1046.0 -715.0
injection 200.0 0.0 0.0
JPY amount 100.0 0.0 0.0
daily_added_value NaN -300.0 NaN
daily_returns 0 0 0
injection 200.0 NaN 0.0
2010-01-04 2010-01-05
Portfolio A GBP amount 4827.0 5207.0
daily_added_value -144.0 180.0
injection 200.0 0.0
USD amount 4552.0 4876.0
daily_added_value -3517.0 -2829.0
daily_returns 0 0 0
injection 3153.0 3358.0
Portfolio B EUR amount 3459.0 3670.0
daily_added_value -365.0 -89.0
injection 300.0 0.0
JPY amount 100.0 0.0
daily_added_value 100.0 -300.0
daily_returns 0 0 0
injection 200.0 0.0
```
**Error**
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-56-7a5afb5f45f1> in <module>()
2 amount = df.loc[:, pd.IndexSlice[:, :, 'amount']]
3 dr = dav1 * amount
----> 4 dr.columns.set_levels(['daily_return'], level=2, inplace=True)
5 df = pd.concat([df, dr], axis=1).sortlevel(axis=1)
C:\Users\xxxxxxxx\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\indexes\multi.py in set_levels(self, levels, level, inplace, verify_integrity)
227 idx._reset_identity()
228 idx._set_levels(levels, level=level, validate=True,
--> 229 verify_integrity=verify_integrity)
230 if not inplace:
231 return idx
C:\Users\xxxxxxxx\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\indexes\multi.py in _set_levels(self, levels, level, copy, validate, verify_integrity)
164
165 if verify_integrity:
--> 166 self._verify_integrity()
167
168 def set_levels(self, levels, level=None, inplace=False,
C:\Users\xxxxxxxx\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\indexes\multi.py in _verify_integrity(self)
127 " level (%d). NOTE: this index is in an"
128 " inconsistent state" % (i, label.max(),
--> 129 len(level)))
130
131 def _get_levels(self):
ValueError: On level 2, label max (1) >= length of level (1). NOTE: this index is in an inconsistent state
```
**How do you make the index consistent?**
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 62 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: 0.2.1
```
This post is also raised as a [question](http://stackoverflow.com/questions/38491990/adding-a-column-derived-from-apply-to-multiindex-dataframe-using-tshift) and also related to this [question](http://stackoverflow.com/questions/38352742/pandas-design-considerations-for-multiindexed-dataframes)
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} | 2 | 2016-07-21T11:51:26Z | 2016-07-21T12:06:08Z | 2016-07-21T12:01:40Z | NONE | null | Thanks for all developers,
I think it's useful and natural behaviour
when `keys` parameter of concat() function is specified with Index or MultiIndex object, its `name` or `names` attribute is not taken over to the result DataFrame object.
``` python
>>> import pandas as pd
>>> a1 = pd.DataFrame([[110, 111], [120, 121]])
>>> a1.index.name = 'index_X'
>>> a2 = pd.DataFrame([[210, 211], [220, 221]])
>>> a2.index.name = 'index_X'
>>> b = pd.concat([a1, a2], keys=['a1', 'a2'], names=['index_Y'])
>>> b
0 1
index_Y index_X
a1 0 110 111
1 120 121
a2 0 210 211
1 220 221
>>> b_index = pd.Index(['a1', 'a2'], name='index_Y')
>>> b = pd.concat([a1, a2], keys=b_index)
>>> b # name of b_index is not taken over!
0 1
index_X
a1 0 110 111
1 120 121
a2 0 210 211
1 220 221
```
In such case, I think it's useful and natural behavior that the name of 1st level of the result indices is automatically set as `"index_Y"`.
Are there any probrems or difficulties if this change will be applied?
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-31-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
```
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} | 2 | 2016-07-21T17:51:57Z | 2016-09-02T22:08:10Z | 2016-09-02T22:08:10Z | NONE | null | When doing a `groupby` on more than one column, the resulting `MultiIndex` does not seem to preserve the original column dtypes. I noticed it when working with `Categorical` columns, expecting `CategoricalIndex` when grouping on them, but this is only the case when grouping on just one column.
I did see that the behaviour was [discussed in a PR](https://github.com/pydata/pandas/pull/13394#issuecomment-225072647), but it ultimately was not addressed.
#### Code Sample, a copy-pastable example if possible
``` python
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({
...: 'a': pd.Series(list('xyxxyz')).astype('category', categories=list('xyz')),
...: 'b': pd.Series(list('yzzyxz')).astype('category', categories=list('xyz')),
...: 'c': [1,2,3,4,5,6]
...: })
In [3]: df.groupby('a').sum().reset_index().dtypes
Out[3]:
a category
c int64
dtype: object
In [4]: df.groupby(['a', 'b']).sum().reset_index().dtypes
Out[4]:
a object
b object
c float64
dtype: object
```
#### Expected Output
``` python
In [4]: df.groupby(['a', 'b']).sum().reset_index().dtypes
Out[4]:
a category
b category
c int64
dtype: object
```
#### output of `pd.show_versions()`
``` python
In [5]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.13
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.18.1+240.gbb6b5e5
nose: None
pip: 8.1.2
setuptools: 19.4
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 5.0.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.9.3
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.14
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
```
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} | 1 | 2016-07-21T21:40:44Z | 2016-10-01T11:28:02Z | 2016-10-01T11:28:01Z | NONE | null | ### Code
``` python
>>> import numpy as np
>>> import pandas as pd
>>> df = DataFrame([1, 2, 3, np.nan], columns = ['x'])
>>> print df.x.quantile(0.5)
```
...outputs NaN, where would expect it to handle NaN values and return 2.0
### Versions
pandas: 0.18.1
numpy: 1.11.0
In [NumPy 1.10.1](http://docs.scipy.org/doc/numpy/release.html#numpy-1-10-0-release-notes), the behavior of numpy.median and numpy.percentile are changed to not handle NaN...
> > > Median warns and returns nan when invalid values are encountered... Similar to mean, median and percentile now emits a Runtime warning and returns NaN in slices where a NaN is present. To compute the median or percentile while ignoring invalid values use the new nanmedian or nanpercentile functions.
Since pandas.DataFrame uses numpy.percentile for .describe and .quantile, neither handle NaN values when paired with numpy >= 1.10.0.
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| null | 9 | 2016-07-22T00:11:04Z | 2016-12-02T15:39:18Z | null | CONTRIBUTOR | null | I think that the title says it all. The `pd.merge` function can be compute intensive and can benefit (I think) from parallel computing.
It does not appear to currently release the GIL. I can easily push my CPU to 100% but no higher when performing parallel joins.
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} | 4 | 2016-07-22T01:00:31Z | 2016-07-27T10:39:32Z | 2016-07-27T10:39:32Z | NONE | null | #### Code Sample, a copy-pastable example if possible
See for example http://stackoverflow.com/questions/18471859/pandas-read-csv-dtype-inference-issue
(the behavior didn't since 3 years ago, except a warning is now issued)
```
import pandas as pd
df = pd.DataFrame({'a':['1']*100000 + ['X']*100000 + ['1']*100000, 'b':['b']*300000})
df.to_csv('test', sep='\t', index=False, na_rep='NA') # warning issued
df2 = pd.read_csv('test', sep='\t')
print df2['a'].unique() # ['1' 'X' 1]
for a in df2['a'][262140:262150]:
print repr(a) # switching between '1' and 1
```
Similar examples: the column that contains '1' and 1 is inferred as string; one that contains 1 and '1' is inferred as numeric.
```
import io
df = pd.read_csv(io.StringIO('1, "1"\n"1",1'), header=None) # no warning
df.dtypes # int and str
```
Furthermore, `pd.to_numeric(df[1])` won't actually work in the above case.
While it's an understandable behavior, it is unexpected to many users and isn't even documented (beyond the general warnings that type inference isn't perfect). Given the number of people who use pandas for reading csv files, without knowing the history of the library or the mechanics of type inference, this results in a lot of wasted time identifying and fixing a problem that could have been prevented.
I suggest at least adding clear documentation on this, but preferably changing the _default_ behavior to either fail with an error stating that type inference failed and that dtypes should be explicitly provided (I don't think it's possible to use two passes since the input data may be a generator that is exhausted after the first read).
#### Expected Output
the column
#### output of `pd.show_versions()`
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} | 9 | 2016-07-22T03:39:34Z | 2016-11-18T22:43:00Z | 2016-11-18T22:42:37Z | CONTRIBUTOR | null | This is probably a known bug but I couldn't find a github issue.
There is a disabled test `test_clipboard.py` which fails with the following error
``` python
======================================================================
FAIL: test_round_trip_frame_sep (pandas.io.tests.test_clipboard.TestClipboard)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/users/piotr/workspace/pandas-pijucha/pandas/io/tests/test_clipboard.py", line 73, in test_round_trip_frame_sep
self.check_round_trip_frame(dt, sep=',')
File "/home/users/piotr/workspace/pandas-pijucha/pandas/io/tests/test_clipboard.py", line 69, in check_round_trip_frame
tm.assert_frame_equal(data, result, check_dtype=False)
File "/home/users/piotr/workspace/pandas-pijucha/pandas/util/testing.py", line 1276, in assert_frame_equal
right.columns))
File "/home/users/piotr/workspace/pandas-pijucha/pandas/util/testing.py", line 1022, in raise_assert_detail
raise AssertionError(msg)
AssertionError: DataFrame are different
DataFrame shape (number of columns) are different
[left]: 2, Index([u'en', u'es'], dtype='object')
[right]: 0, Index([], dtype='object')
```
#### Code Sample, a copy-pastable example if possible
More explicitly (the example from the above test):
``` python
nonascii = pd.DataFrame({'en': 'in English'.split(), 'es': 'en español'.split()})
nonascii.to_clipboard(sep=',')
read_clipboard(sep=',', index_col=0)
Out[154]:
Empty DataFrame
Columns: []
Index: [0 in en, 1 English español]
read_clipboard()
Out[155]:
en es
0 in en
1 English español
```
#### Expected Output
``` python
read_clipboard(sep=',', index_col=0)
Out[134]:
en es
0 in en
1 English español
read_clipboard()
Out[135]:
,en,es
0 0,in,en
1 1,English,español
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.1.20-1
machine: x86_64
processor: Intel(R)_Core(TM)_i5-2520M_CPU_@_2.50GHz
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.18.1+240.gbb6b5e5
nose: 1.3.7
pip: 8.1.2
setuptools: 21.2.0
Cython: 0.24.1
numpy: 1.11.0
```
---
There are probably 2 issues in the code.
1. `.encode('utf-8') [is called](https://github.com/pydata/pandas/blob/bb6b5e54edaf046389e8cce28e7cd27ee87f5fcc/pandas/util/clipboard.py#L172) on a py2 string, which raises if there is a non-ascii character in the string, and then
2. `to_clipboard` [falls back](https://github.com/pydata/pandas/blob/bb6b5e54edaf046389e8cce28e7cd27ee87f5fcc/pandas/io/clipboard.py#L87) to `to_string` method.
(In this case, fixing 1 solves the problem. But in general, if something else raises and we fall back here, a separator is ignored.)
I don't know what to do about 2, but 1 seems to be easy.
Part of the code in `util.clipboard.py` calls `subprocess.Popen.communicate()`, which operates on byte types (bytes in PY3 and strings in PY2). So, `encode`/`decode` are needed only in PY3.
I believe this https://github.com/pydata/pandas/commit/6d4fdb0e9e0c55caacd60db8989f6868bf6fea0a fixes the problem. But for now I tested only one pair of functions (in KDE) and couldn't possibly test it on OS X.
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} | 4 | 2016-07-22T05:43:12Z | 2016-07-22T10:10:31Z | 2016-07-22T10:10:09Z | NONE | null | #### Code Sample
``` python
import pandas as pd
import numpy as np
dates = pd.date_range('1/1/2000', periods=8)
data1 = {'A': np.arange(8),
'B': np.random.randn(8)
}
df1 = pd.DataFrame(data1, index=dates)
data2 = {'A': np.arange(8),
'C': np.random.randn(8)
}
df2 = pd.DataFrame(data2, index=dates)
df = pd.concat([df1, df2], axis=1, ignore_index=False)
```
```
print(df1)
A B
2000-01-01 0 0.756225
2000-01-02 1 0.537196
2000-01-03 2 0.813128
2000-01-04 3 0.819868
2000-01-05 4 -0.653656
2000-01-06 5 1.246314
2000-01-07 6 -0.703276
2000-01-08 7 -0.743086
```
```
print(df2)
A C
2000-01-01 0 -1.584052
2000-01-02 1 -0.804060
2000-01-03 2 -1.350531
2000-01-04 3 -0.762914
2000-01-05 4 -0.589009
2000-01-06 5 0.395458
2000-01-07 6 -0.124446
2000-01-08 7 0.100183
```
df1 and df2 both contain column A (which is not an index column), so the unique column names are:
```
print(df.columns.unique())
['A' 'B' 'C']
```
When applying the below selection (assuming that columns with the same name contain the same values - which I know is true in this use case however may not always be true), the duplicate columns are kept.
```
print(df[df.columns.unique()])
A A B C
2000-01-01 0 0 0.086386 -2.558181
2000-01-02 1 1 0.217020 -0.964484
2000-01-03 2 2 -0.426538 -0.579129
2000-01-04 3 3 0.620989 -1.908091
2000-01-05 4 4 -1.463473 -0.345109
2000-01-06 5 5 -0.460185 0.670077
2000-01-07 6 6 -1.353535 0.226513
2000-01-08 7 7 0.329263 2.274627
```
#### Expected Output
A drop_duplicates for columns where for example the first duplicate column name is kept.
```
print(df.T.drop_duplicates().T)
A B C
2000-01-01 0.0 0.086386 -2.558181
2000-01-02 1.0 0.217020 -0.964484
2000-01-03 2.0 -0.426538 -0.579129
2000-01-04 3.0 0.620989 -1.908091
2000-01-05 4.0 -1.463473 -0.345109
2000-01-06 5.0 -0.460185 0.670077
2000-01-07 6.0 -1.353535 0.226513
2000-01-08 7.0 0.329263 2.274627
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-59-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_AU.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 5.0.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: 0.7.3.None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: 2.8
boto: None
pandas_datareader: None
```
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} | 1 | 2016-07-22T06:53:52Z | 2016-07-25T23:41:33Z | 2016-07-25T23:41:33Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
as the same as `is_unique`, it should be monotonic by definition
```
%timeit -n 1 -r 1 pd.RangeIndex(0, 1000000).is_monotonic_increasing
# current master
#1 loop, best of 1: 17.2 ms per loop
# this PR
#1 loop, best of 1: 45.1 µs per loop
```
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} | 4 | 2016-07-22T07:17:58Z | 2016-07-24T16:12:32Z | 2016-07-24T15:34:24Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
because `factorize` internally localize datetimetz, it raises when data contains DST boundary.
```
dti = pd.date_range('2016-11-06', freq='H', periods=5, tz='US/Eastern')
dti.factorize()
# AmbiguousTimeError: Cannot infer dst time from Timestamp('2016-11-06 01:00:00'), try using the 'ambiguous' argument
```
Skipped this localization to fix, also it improves perf.
```
dti = pd.date_range('2011-01-01', freq='H', periods=1000000, tz='Asia/Tokyo')
%timeit dti.factorize()
# on current master
#1 loop, best of 3: 475 ms per loop
# after this PR
#1 loop, best of 3: 262 ms per loop
```
**asv:**
```
before after ratio
[bb6b5e54] [a2c3370a]
- 22.46ms 9.97ms 0.44 timeseries.datetime_algorithm.time_dti_tz_factorize
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
```
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} | 2 | 2016-07-22T07:29:27Z | 2016-07-25T12:03:19Z | 2016-07-25T12:03:19Z | MEMBER | null | - [x] closes #10235
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
`Index/Series.duplicated` now uses dtype-based logic. Also skip algorithm if `Index.is_unique` is `True` (expected to be cached in practical situation).
**asv:**
```
before after ratio
[bb6b5e54] [16c6da4f]
- 34.41ms 4.69ms 0.14 algorithms.algorithm.time_int_duplicated
- 53.77ms 5.59ms 0.10 algorithms.algorithm.time_float_duplicated
- 60.22ms 40.20μs 0.00 algorithms.algorithm.time_int_unique_duplicated
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
```
**NOTE:** can create template for htable after #13716.
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} | 2 | 2016-07-22T09:53:25Z | 2016-08-03T12:38:40Z | 2016-07-22T14:07:57Z | NONE | null | I'm running into a strange problem where a full pandas build starts every time setup.py is invoked, with any arguments whatsover: `python setup.py`, `python setup.py --help`, `python setup.py xyz` ... Even `python -c 'import setup'` starts a build.
Tested with Python 3.5 & pandas 0.18.1 on Mac OS X and Ubuntu Server.
```
~ $ cd /tmp
/tmp $ virtualenv -p $(which python3) env
Running virtualenv with interpreter /usr/local/bin/python3
Using base prefix '/usr/local/Cellar/python3/3.5.1/Frameworks/Python.framework/Versions/3.5'
New python executable in /private/tmp/env/bin/python3.5
Also creating executable in /private/tmp/env/bin/python
Installing setuptools, pip, wheel...done.
/tmp $ . env/bin/activate
(env) /tmp $ wget https://pypi.python.org/packages/11/09/e66eb844daba8680ddff26335d5b4fead77f60f957678243549a8dd4830d/pandas-0.18.1.tar.gz#md5=42b9dba111a8f916352f49b512c029aa
--2016-07-22 11:44:49-- https://pypi.python.org/packages/11/09/e66eb844daba8680ddff26335d5b4fead77f60f957678243549a8dd4830d/pandas-0.18.1.tar.gz
Resolving pypi.python.org... 151.101.12.223, 2a04:4e42:3::223
Connecting to pypi.python.org|151.101.12.223|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 7283184 (6.9M) [application/octet-stream]
Saving to: 'pandas-0.18.1.tar.gz'
pandas-0.18.1.tar.gz 100%[===================================================================================================================================================================>] 6.95M 2.10MB/s in 3.3s
2016-07-22 11:44:52 (2.09 MB/s) - 'pandas-0.18.1.tar.gz' saved [7283184/7283184]
(env) /tmp $ tar zxf pandas-0.18.1.tar.gz
(env) /tmp $ cd pandas-0.18.1/
(env) /tmp/pandas-0.18.1 $ python setup.py --help
Running from numpy source directory.
/var/folders/84/6bwfrc955bdcvlgnhx2zqwzh0000gn/T/easy_install-f5yrgkx8/numpy-1.11.1/setup.py:327: UserWarning: Unrecognized setuptools command, proceeding with generating Cython sources and expanding templates
warnings.warn("Unrecognized setuptools command, proceeding with "
non-existing path in 'numpy/distutils': 'site.cfg'
/usr/local/Cellar/python3/3.5.1/Frameworks/Python.framework/Versions/3.5/lib/python3.5/distutils/dist.py:261: UserWarning: Unknown distribution option: 'define_macros'
warnings.warn(msg)
Could not locate executable gfortran
Could not locate executable f95
Could not locate executable f90
Could not locate executable f77
Could not locate executable xlf90
Could not locate executable xlf
Could not locate executable ifort
Could not locate executable ifc
Could not locate executable g77
Could not locate executable g95
Could not locate executable pgfortran
don't know how to compile Fortran code on platform 'posix'
_configtest.c:1:5: warning: incompatible redeclaration of library function 'exp' [-Wincompatible-library-redeclaration]
int exp (void);
^
_configtest.c:1:5: note: 'exp' is a builtin with type 'double (double)'
... and so on ...
```
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} | 3 | 2016-07-22T10:53:15Z | 2016-07-24T14:05:15Z | 2016-07-24T14:05:15Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [0, 1, 2, np.nan, 4],
'B': [0, 1, 2, np.nan, 4],
'C': [0, 1, 2, np.nan, 4],
'D': [0, 1, 2, np.nan, 4],
'E': [0, 1, 2, np.nan, 4],
'F': [0, 1, 2, np.nan, 4]})
print df.expanding(axis=1).sum()
```
#### Expected Output
```
A B C D E F
0 0.0 0.0 0.0 0.0 0.0 0.0
1 1.0 2.0 3.0 4.0 5.0 5.0
2 2.0 4.0 6.0 8.0 10.0 10.0
3 NaN NaN NaN NaN NaN NaN
4 4.0 8.0 12.0 16.0 20.0 20.0
```
However, the correct result should be:
```
A B C D E F
0 0.0 0.0 0.0 0.0 0.0 0.0
1 1.0 2.0 3.0 4.0 5.0 6.0
2 2.0 4.0 6.0 8.0 10.0 12.0
3 NaN NaN NaN NaN NaN NaN
4 4.0 8.0 12.0 16.0 20.0 24.0
```
Notice that the last column `E` is different. I've tracked this down and found that the _get_window function (for expanding) fails to return the correct number of windows when the following conditions are met:
1. `axis=1` is used instead of `axis=0` (default)
2. The number of rows in the dataframe is less than the number of columns
This is caused by the fact that the object is using `len(obj)` in determining the window size. Instead, it should be using `obj.shape[self.axis]`
#### output of `pd.show_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: None
LOCALE: None.None
pandas: 0.18.1+237.ge357ea1
nose: 1.3.7
pip: 8.1.2
setuptools: 20.1.1
Cython: 0.23.4
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: 0.7.0
IPython: 4.0.3
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.4.1
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: None
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.5.0
bs4: 4.4.1
html5lib: None
httplib2: 0.9
apiclient: 1.4.0
sqlalchemy: 1.0.11
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: 2.8
boto: 2.39.0
pandas_datareader: 0.2.0
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} | 0 | 2016-07-22T11:06:17Z | 2016-10-10T12:30:22Z | 2016-10-10T12:30:22Z | MEMBER | null | Small example:
```
In [87]: df = pd.DataFrame(columns=pd.MultiIndex.from_product([['A','B'],['a','b']]))
```
Setting the levels with an incorrect levels (too short), fails (correctly of course):
```
In [92]: df.columns.set_levels(['c'], level=1)
...
ValueError: On level 1, label max (1) >= length of level (1). NOTE: this index
is in an inconsistent state
```
When using `inplace=True`, this also gives the same error message, but the index _is_ actually changed to this inconsistent state:
```
In [93]: df.columns.set_levels(['c'], level=1, inplace=True)
...
ValueError: On level 1, label max (1) >= length of level (1). NOTE: this index
is in an inconsistent state
In [94]: df.columns
Out[94]:
MultiIndex(levels=[[u'A', u'B'], [u'c']],
labels=[[0, 0, 1, 1], [0, 1, 0, 1]])
```
Just showing this `df` in the console did crash my session in the case above, in the example from #13741, it gave the error "IndexError: index 1 is out of bounds for axis 0 with size 1"
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| null | 10 | 2016-07-22T11:36:07Z | 2017-05-08T09:29:29Z | 2017-05-07T14:00:15Z | MEMBER | null | - [x] closes #7964
- [ ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
This PR is **NOT** ready for review, but to show basic direction. Especially, supporting period arithmetic is quite unreliable because current `_TimeOp` class is basically for `np.ndarray` operations. I think refactoring `_TimeOp` is needed prior to this PR.
Would like to split the PR to 3 parts:
- 1. Add `PeriodDtype` definition, let `PeriodIndex` to have `PeriodDtype` like `period[M]`. (#13941)
- 2. Refactor `_TimeOp` splitting to `_DatetimeOp` and `_TimedeltaOp`, merging `DatetimeIndex` and `TimedeltaIndex` arithmetics.
- `_DatetimeOp` is for lhs or rhs is `datetime64` or `datetimetz` dtypes.
- `_TimedeltaOp` is for other `timedelta` related types.
- 3. Add `PeriodBlock`, adding `_PeriodOp` for arithmetic (this PR).
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} | 1 | 2016-07-22T14:43:10Z | 2016-07-22T17:03:36Z | 2016-07-22T17:03:15Z | CONTRIBUTOR | null | I try to mimic [this](http://stackoverflow.com/questions/25071937/filter-pandas-dataframe-based-on-max-values-in-a-column) with a datetime dtype column
#### Code Sample, a copy-pastable example if possible
``` python
import pandas as pd
df = pd.DataFrame(dict(
date = ['2006-12-01', '2008-12-01', '2011-11-01'],
id = [0, 1, 1],
))
df.date = pd.to_datetime(df.date)
print df.iloc[df.groupby(by = 'id')['date'].idxmin().values.ravel()]
print df.iloc[df.groupby(by = 'id')['date'].agg(pd.Series.idxmin)]
```
#### Expected Output
Both results should be the same but the second yield to the following error:
```
File "/home/benjello/openfisca/junk/pandas_datetime.py", line 21, in <module>
print df.iloc[df.groupby(by = 'id')['date'].agg(pd.Series.idxmin)]
File "/home/benjello/.local/lib/python2.7/site-packages/pandas/core/indexing.py", line 1296, in __getitem__
return self._getitem_axis(key, axis=0)
File "/home/benjello/.local/lib/python2.7/site-packages/pandas/core/indexing.py", line 1599, in _getitem_axis
self._is_valid_list_like(key, axis)
File "/home/benjello/.local/lib/python2.7/site-packages/pandas/core/indexing.py", line 1537, in _is_valid_list_like
if len(arr) and (arr.max() >= l or arr.min() < -l):
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.5.0-2-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 20.10.1
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 4.0.3
sphinx: 1.3.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.0
xlrd: 0.9.4
xlwt: 0.7.5
xlsxwriter: 0.7.3
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.999
httplib2: 0.9.1
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
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| null | 8 | 2016-07-22T18:06:25Z | 2016-08-09T23:24:25Z | 2016-08-09T23:24:25Z | CONTRIBUTOR | null | - [x] fixes #13753
- [ ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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} | 6 | 2016-07-22T19:31:27Z | 2020-01-07T23:45:41Z | 2020-01-07T23:45:41Z | CONTRIBUTOR | null | I had a recent use case which produced some unexpected output and I have isolated the problem down to a much simpler case to replicate the behavior.
Please see simplified code example below.
#### Code Sample, a copy-pastable example if possible
``` python
import pandas as pd
import numpy as np
data = {
'One' : pd.Series(['A', 1.2, np.nan]),
'Two' : pd.Series([1.4, 3.2, 4.5])
}
df = pd.DataFrame(data)
```
Doing the sum across the first column (which in this case is redundant). [**Note:** In my case I was doing groupby operations where some columns were being summed and others were single columns as indexed by a level of a MultiIndex. The issue was that some of the columns ended up being full of 0's)
``` python
df[['One']].sum(axis=1)
```
produces an **unexpected** outcome:
``` ipython
0 0.0
1 0.0
2 0.0
dtype: float64
```
I think this might be an **error** and it produces a valid column of 0's that is of `dtype=float64`.
#### Expected Output
``` ipython
0 A
1 1.2
2 NaN
Name: One, dtype: object
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-38-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
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} | 5 | 2016-07-22T20:44:16Z | 2016-07-23T13:12:45Z | 2016-07-23T13:12:45Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew not needed
`union_categoricals` doesn't handle `NaN` properly.
**on current master:**
```
from pandas.types.concat import union_categoricals
union_categoricals([pd.Categorical([np.nan, 1]), pd.Categorical([2, np.nan])])
# [1, 1, 2, 2]
# Categories (2, int64): [1, 2]
union_categoricals([pd.Categorical([np.nan]), pd.Categorical([np.nan])])
# IndexError: cannot do a non-empty take from an empty axes.
```
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} | 3 | 2016-07-23T04:25:57Z | 2017-09-18T13:09:51Z | 2017-09-18T10:55:09Z | NONE | null | #### Code Sample, a copy-pastable example if possible
I want to transfer the follwing dataframe to JSON using a dictionary style.
```
weightedRisk riskAsset safetyAsset weightedAsset riskRank
0 0.029839 0.029839 0.220161 0.25 2
1 0.053840 0.053840 0.446160 0.50 3
2 0.015485 0.015485 0.234515 0.25 4
```
#### Expected Output
the expected ouput should be:
```
"groupedWeightedRisk": {
"safetyAsset": [
0.2201611228,
0.4461597981,
0.2345148013
],
"riskAsset": [
0.0298388772,
0.0538402019,
0.0154851987
],
"riskRank": [
2,
3,
4
],
"weightedRisk": [
0.0298388772,
0.0538402019,
0.0154851987
],
"weightedAsset": [
0.25,
0.5,
0.25
]
}
```
But in pandas, all of the **to_json** method makes the ouput contains the index.
So.I use **to_dict(orient='list")** and then using **json.dumps** to serialize the data.
this method works well in most of the time. However , in the pervious case, the
**json.dumps** raise an error saying:
**TypeError: 2 is not JSON serializable**
then I checked the dataframe and locate the problem.
```
type(groupedWeightedRisk['riskRank'][0])
<class 'numpy.int64'>
```
one of the series is made up by **numpy.int64**, and this data type could not be JSON serializable directly.
Finally, I solve this problem using a function with a stupid way,that I serialise the data columns one by one.
```
def dataFrameToDict(df,indexMethod=None):
result={}
for i in df.columns:
result[i]=json.loads(df[i].to_json(orient='records'))
if indexMethod is not None:
if df.index.name is not None:
indexName=df.index.name
else:
indexName='index'
if type(df.index) == pd.tseries.index.DatetimeIndex:
result[indexName]=list(df.index.strftime("%Y-%m-%d"))
else:
result[indexName] = list(df.index)
return result
```
So, is there any possibility that pandas could add a method in **to_json** that could provide the a JSON data using dictionary style?
Thanks a lot
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} | 7 | 2016-07-23T05:18:55Z | 2016-07-25T15:15:52Z | 2016-07-25T15:14:32Z | CONTRIBUTOR | null | - [x] closes #13738
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
Added additional '\' characters in order to produce the correct format in the documentation.
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} | 2 | 2016-07-23T05:37:22Z | 2016-07-23T13:09:18Z | 2016-07-23T13:09:18Z | MEMBER | null | - [x] closes #13672
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 8 | 2016-07-23T05:43:03Z | 2016-07-29T10:24:51Z | 2016-07-29T10:24:46Z | MEMBER | null | - [x] related to #13410, #13524
- [x] tests added / passed
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- [x] whatsnew not needed
`.append / concat` can handle ordered `Categorical` which has the identical categories. However, `union_categorical` raises `TypeError`. This PR allows `union_categorical` to handle the case.
**on current mastser**
```
s1 = pd.Series(pd.Categorical([1, 2, 3], ordered=True))
s2 = pd.Series(pd.Categorical([3, 2, 1], categories=[1, 2, 3], ordered=True))
s1.append(s2)
#0 1
#1 2
#2 3
#0 3
#1 2
#2 1
# dtype: category
# Categories (3, int64): [1 < 2 < 3]
pd.types.concat.union_categoricals([s1.values, s2.values])
# TypeError: Can only combine unordered Categoricals
```
CC: @janschulz, @chris-b1
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} | 15 | 2016-07-23T09:54:08Z | 2016-07-24T14:01:31Z | 2016-07-24T14:00:35Z | MEMBER | null | Weird segfault arises when you call `lib.isnullobj` with an array that uses 0-field values to mean `None`.
Changed input to be a `Python` object (i.e. no typing), and the segfault went away. Discovered when there were segfaults in printing a `DataFrame` containing such an array.
Closes #13717.
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| null | 8 | 2016-07-23T16:30:21Z | 2017-04-18T10:14:27Z | 2017-04-18T10:01:05Z | MEMBER | null | - [ ] part of #4160
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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} | 36 | 2016-07-24T01:33:28Z | 2016-09-07T13:24:29Z | 2016-09-07T13:18:27Z | MEMBER | null | - [x] closes #13524
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
**on current master:**
```
# different categoricals
pd.concat([pd.Series([1, 2], dtype='category'),
pd.Series([3, 4], dtype='category')])
# ValueError: incompatible categories in categorical concat
# categorical + normal (values are contained in categories) -> object dtype
pd.concat([pd.Series([1, 2], dtype='category'),
pd.Series([1, 2])])
#0 1
#1 2
#0 1
#1 2
# dtype: object
# categorical + normal (values are not contained in categories) -> object dtype
pd.concat([pd.Series([1, 2], dtype='category'),
pd.Series([3, 4])])
#0 1
#1 2
#0 3
#1 4
# dtype: object
```
**this PR (updated according to the discussion):**
```
# different categoricals
pd.concat([pd.Series([1, 2], dtype='category'),
pd.Series([3, 4], dtype='category')])
#0 1
#1 2
#0 3
#1 4
# dtype: int64
# specifying union_categoricals keeps category if possible
pd.concat([pd.Series([1, 2], dtype='category'),
pd.Series([3, 4], dtype='category')], union_categoricals=True)
#0 1
#1 2
#0 3
#1 4
# dtype: category
Categories (4, int64): [1, 2, 3, 4]
# categorical + normal (values are contained in categories) -> category dtype
pd.concat([pd.Series([1, 2], dtype='category'),
pd.Series([1, 2])])
#0 1
#1 2
#0 1
#1 2
# dtype: int64
# categorical + normal (values are not contained in categories) -> int dtype (keep original dtype)
pd.concat([pd.Series([1, 2], dtype='category'),
pd.Series([3, 4])])
#0 1
#1 2
#0 3
#1 4
# dtype: int64
```
CC: @janschulz, @chris-b1
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} | 9 | 2016-07-24T01:41:03Z | 2017-01-25T10:36:55Z | 2017-01-25T10:36:55Z | MEMBER | null | - [x] closes #9809
- [ ] ToDo:
- [ ] Add detail of dtype promotion
Moved some topics to corresponding docs. Also removed following duplicates.
**Parsing Dates from Text Files:** the same contents exists on io doc.
- http://pandas-docs.github.io/pandas-docs-travis/gotchas.html#parsing-dates-from-text-files
- http://pandas-docs.github.io/pandas-docs-travis/io.html#specifying-date-columns
**Byte ordering issues:**
- http://pandas-docs.github.io/pandas-docs-travis/faq.html#byte-ordering-issues
- http://pandas-docs.github.io/pandas-docs-travis/gotchas.html#byte-ordering-issues
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- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
Must be fixed by `timedelta` dtype. Added tests.
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- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Fixed by #13692. Added tests.
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} | 4 | 2016-07-24T10:43:30Z | 2016-07-24T14:19:33Z | 2016-07-24T13:51:12Z | MEMBER | null | - [x] closes #13663
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
The root cause looks normal `Index` raises `OutOfBoudsDatetime`. I think it should be coerced to `object` dtype because user specifies no dtype.
```
pd.Index([datetime(9999, 1, 1)])
# on current master
# OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 9999-01-01 00:00:00
# this PR
Index([9999-01-01 00:00:00], dtype='object')
```
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} | 4 | 2016-07-24T14:22:07Z | 2016-07-26T18:25:57Z | 2016-07-26T18:25:33Z | CONTRIBUTOR | null | - [ ] related to #13746
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry not needed?
``` python
# before
In [7]: pd.to_numeric(['a'])
ValueError: Unable to parse string
# after
In [1]: pd.to_numeric(['a'])
ValueError: Unable to parse string "a" at position 0
```
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} | 4 | 2016-07-24T15:27:15Z | 2020-04-14T05:25:38Z | 2020-04-14T05:25:32Z | CONTRIBUTOR | null | xref #13351
[Here's the test](https://github.com/pydata/pandas/commit/964b7bba7f5878c79130479f75461c58dd0c4b3e), currently skipped
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} | 3 | 2016-07-24T16:16:35Z | 2017-04-05T02:07:01Z | 2016-08-04T11:43:56Z | CONTRIBUTOR | null | - [x] closes #11655
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
[Notebook comparing `DataFrame._html_repr_` to `DataFrame.style`](https://gist.github.com/609c398f814b4a505bf4f406670e457e)
I think we're identical for non-truncated DataFrames. That' has not been implemented in `Styler` yet.
Along the way I noticed two other things that ended up needing fixing.
1. DataFrame.columns.names were not displayed
2. CSS classes weren't being assigned correctly to row labels.
The fixes ended up being pretty intertwined, so I've put them in a single PR. Unfortunately, the commits are a bit jumbled as well :/
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} | 12 | 2016-07-24T16:27:33Z | 2016-07-25T23:34:18Z | 2016-07-25T23:34:18Z | CONTRIBUTOR | null | closes #13564
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} | 7 | 2016-07-24T16:49:21Z | 2020-01-05T17:26:09Z | 2020-01-05T17:25:58Z | CONTRIBUTOR | null | Document removed deprecations, originally indicated in #6581. This reference is in reverse chronological order of release.
## 1.0.0
- [x] #16994 ``inplace`` arg to ``.set_axis()`` (0.21.0) (#27525)
- [x] #17739 ``.get_value()`` and ``.set_value()`` (0.21.0) (#27377)
- [x] #22264 list-likes IN ``str.cat`` (0.24.0) (#27611)
- [x] #19339 ``IntervalIndex.from_intervals`` (0.23.0) https://github.com/pandas-dev/pandas/pull/27793
- [x] #29608 support for nested renamers in `aggregate`
- [x] #23592 tm.assert_raises_regex (0.24.0) (#29174)
- [x] #23739 keep_tz=False in DatetimeIndex.to_series (0.24.0) (#29731)
- [x] #19751 factorize(order=) kwarg (0.23.0) (#29732)
- [x] #22672 FrozenNDArray.searchsorted signature (v → value) (0.24.0) (#29732)
- [x] #23585 raise_conflic kwarg replaced by errors in .update() (0.24.0) (#29732)
- [x] #22458 Index.shift n in favor of periods (0.24.0) (#29732)
- [x] #18836 skip_footer in pd.read_excel (0.23.0) (#29721)
- [x] #18902 convert_datetime64 in DataFrame.to_records (0.23.0) (#29721)
- [x] #23053 nthreads arg in read_feather (0.24.0) (#29728)
- [x] #18458 DataFrame/Series.as_matrix (0.23.0) (#29720)
- [x] #18258 Series.from_array (0.23.0) (#29720)
- [x] #18477 .asobject (0.23.0) (#29720)
- [x] #17656 DataFrame.blocks, DataFrame.as_blocks() (0.21.0) (#29720)
- [x] #18529 DataFrame.from_items (0.23.0) (#29720)
- [x] #18627 tseries.plotting.tsplot (0.23.0) (#29726)
- [x] #21400 encoding arg in read_stata (0.24.0) (#29722)
- [x] #18577 broadcast & reduce kwarg in .apply() (0.23.0) (#29730)
- [x] #23917 pd.types.is_period, pd.types.is_datetimetz (0.24.0) (#29744)
- [x] #18800 Series.valid (0.23.0) (#29724)
- [x] #18812 .is_copy (0.23.0) (#29724)
- [x] #18243 .get_ftype_counts() (0.23.0) (#29724)
- [x] #20239 .get_duplicates() (0.23.0) (#29724)
- [x] #24203 Series.clip_lower, Series.clip_upper, DataFrame.clip_lower and DataFrame.clip_upper (0.24.0) (#29724)
- [x] #23601 ExtensionArray._formatting_values (0.24.0) (#27774)
- [x] #19239/#26137 SparseSeries, SparseDataFrame (0.25) (#28425)
- [x] #19434 dtype arg in make_block_same_klass (0.23.0) (#29723)
- [x] #19265 fastpath arg in Block (0.23.0) (#29723)
- [x] #18217 Index.summary() (0.23.0) (#29819
- [x] #23635 passing an int for usecols in read_excel (0.24.0) (#29795)
- [x] #20772 .freq setting on a datetimelike index (0.23.0) (#29801)
- [x] #20730 DatetimeIndex.offset (0.23.0) (#29801)
- [x] #23752 MultiIndex constructor kwarg labels, and .set_lables() deprecated in favor of codes (0.24.0) (#29766)
- [x] #21613 MultiIndex.to_hierarachial (0.24.0) (#29766)
- [x] #20613 pd.concat(...., sort=) (0.23.0) (#29786)
- [x] #21036 floordiv integer ndarray and Timedelta (0.23.0) (#29797)
- [x] #21896 Series.to_csv signature changes (0.24.0) (#29809)
- [x] #20584 ``Series.rolling.apply``etc for the ``raw`` parameter (0.23.0) (#29829)
- [x] #23110 fastpath keyword in Index constructors (0.24.0) (#29725)
- [x] #23539 allowing datetime64 data passed to TimedeltaIndex (0.24.0) (#29794)
- [x] #23937 allowing timedelta64 data passed to DatetimeIndex (0.24.0) (#29794)
- [x] #26744 DataFrame.ftypes, Series.ftype (0.25.0) (#29895)
- [x] #24050 lib.infer_dtype(...., skipna=None (0.24.0) (#29876)
- [x] #24751 Series.cat.categorical (0.24.0) (#29914)
- [x] #18164 DTI.weekday_name and .dt.weekday_nam (0.23.0) (#29831)
- [x] #22644 from ``Timestamp.tz_localize``, ``DatetimeIndex.tz_localize``, ``Series.dt.tz_localize`` remove errors keyword, replaced by nonexistent / ambiguous (0.24.0) (#29911)
- [x] #26839 change Timedelta.resolution to Timedelta.resolution_string (0.25.0) (#29910)
- [X] #18731 listifying tuples for groupby (0.23.0) (#29755)
- [x] #20841 Categorical.take default behaviour with -1 (0.23.0) (#29912)
- [x] #23990 passing dtype alias to DatetimeTZDtype(unit) parameter (0.24.0) (#29927)
- [x] #24114 Categorical where casting to object (0.24.0) (#29913)
- [x] #23396 FrozenNDArray (0.24.0) (#29840)
- [x] #10726 DataFrame.sort_index by keyword (0.17.0) (#29931)
- [x] #17295 reindexing for not-found in .loc (0.21.0) (#29802)
- [x] #17758 non-existant keys in MultiIndex (xref to #17295 for Index) (0.23.0) (#29802)
- [x] #17295 non-existant columns in .to_excel() & non-existant keys in .loc (0.21.0) (#29802)
- [x] #20721 .base, .flags, .stride, .itemsize, .data Series/Index attributes (0.23.0) (#29918)
- [x] #26684 DataFrame & Seriesto_dense/to_sparse (0.25.0) (#29900)
- [x] #27106 Index.dtype_str (0.25.0) (#29900)
- [x] #27199 Categorical.ravel should return a Categorical and not ndarray (0.25.0) (#29900)
- [x] #27145 .get_dtype_counts (0.25.0) (#29900)
- [x] #24694 .fillna(integer) with timedelta (0.24.0) (#29875)
- [x] #20995 passing multiple axes to DataFrame.dropna (0.23.0) (#29875)
- [x] #24048 Series.nonzero (0.24.0) (#29875)
- [x] #21775 Catetgorical.from_codes accepting float codes (0.24.0) (#29875)
- [x] #23621 passing tz and a datetime.datetime/Timestamp with tzinfo in the Timestamp constructor (0.24.0) (#29929)
- [x] #23919 verify_integrity arg in TimedeltaIndex, DatetimeIndex (0.24.0) (#29930)
- [x] #23919 passing start, end, periods to DatetimeIndex or TimedeltaIndex (0.24.0) (#29930)
- [x] #24157 time_rule kwarg in offsets.generate_range (0.24.0) (#24157)
- [x] #23767 Series.str.partition and Series.str.rpartition, replace pat with sep (0.24.0) (#29986)
- [x] #27106 Series.put (0.25.0) (#29986)
- [x] #27106 Series.real & Series.imag (0.25.0) (#29986)
- [x] #26409 Categorical.get_values, SparseArray.get_values, DataFrame.get_values, Series.get_values, Index.get_values (0.25.0) (#29989)
- [x] #26409 SparseArray.values (0.25.0) (#29989)
- [x] #18307 pandas.tseries.converter.register (???) (#30003)
- [x] #???? pandas.plottting._matplotlib.hist._grouped_plot "figsize" kwarg (???) (#30003)
- [x] #???? PlotAccessor keyword-only arguments (???) (#30003)
- [x] #19980 pandas.plotting._timeseries.tsplot (0.23.0) (#30003)
- [x] #26405 Series/DataFrame.compound (0.25) (#30088)
- [x] #22318 join_axes kwarg in pd.concat (0.25.0) (#30090)
- [x] #???? set_axis signature (0.21.0) (#30089)
- [x] #27198 ufunc.outer(Series) (0.25.0) (#30104)
- [x] #24486 box arg in pd.to_datetime (0.25.0) (#30111)
- [x] #24559 i8data for DTI (0.24.0) (#30115)
- [x] #16955 Series.argmin/argmax (0.21.0) (#30113)
- [x] #22535 integer add/sub with DatetimeIndex, TimedeltaIndex, PeriodIndex, Timestamp, Period (0.24.0) (#30117)
- [x] #23264 Y, M in Timedelta and friends (0.25.0) (#30118)
- [x] #???? Index.contains (0.25.0) (#30103)
- [x] #???? how,fill_method kwargs in resample (??? older than Feb 2016) (#30139)
- [x] #26403 ordered=None in CategoricalDtype in favor of ordered=False (0.25.0) (#29955)
- [x] #9493 remove deprecated `StataReader.data` method (???) (#30176)
- [x] #17328 remove deprecated "index" keyword from read_stata, StataReader, StataReader.read (???) (#30176)
- [x] #24806 passing non-nano dtype to TimedeltaIndex/DatetimeIndex (0.24.0) (#30199)
- [x] #15113/#26438 .ix (0.20.0 / DeprecationWarning), (0.25.0 FutureWarning) (#27620)
- [x] #27103 pd.to_msgpack and pd.read_msgpack (0.25.0) (#30112)
- [x] #27112 Series.item & Index.item (0.25.0) (#30175)
- [x] #20613 DataFrame.append "sort" keyword default change None to False (0.23.0) (#30251)
- [x] #21614 `Series.ptp (0.24.0) (#30458)
- [x] #21930 ``Series.compress`` (0.24.0) (#30514)
## 0.25.0
- [x] #15601 / #19247``Panel`` (0.20.0) (#27101 et al)
- [x] #15931 ``.agg(..)`` for groupby/rolling/resample with a renaming dict (0.20.0) (depends on #18366 for a proper replacement) (#26399)
- [x] #19269 / #19373 ``pandas.core.categorial`` move (0.23.0) (#25655)
- [x] #16942 ``pd.TimeGrouper`` (0.21.0) (#26477)
- [x] #16442, #20938 ``ExcelWriter.sheetname`` (0.21.0, 0.23.0) (#26464)
- [x] #17703 ``pd.TimeGrouper`` (0.21.0) (#26477)
- [x] #16488 ``ExcelWriter.parse_cols`` (0.21.0) (#26522)
- [x] #16970 ``pd.options.html.display`` (0.21.0) (#26540)
- [x] #16915 `convert_objects`(0.21.0), xref #11221, #11173 (initial deprecation) (#26612)
- [x] #17633 ``Series.select``, ``DataFrame.select`` (0.21.0) (#26641)
- [x] #17842 ``reindex_axis`` (0.21.0)
- [x] #17842 mappers for ``rename_axis`` (0.21.0)
- [x] #17982 ``cat.rename_categories`` with a ``Series`` as list-like (0.21.0)
- [x] #17812 ``.from_csv()`` (0.21.0)
- [x] #17820 ``tupleize_cols`` in ``read_csv`` (0.21.0)
- [x] #17877 ``tupleize_cols`` in ``to_csv`` (0.21.0)
- [x] #17774 ``.read_excel(..., parse_cols)`` (0.21.0)
- [x] #17744 ``.where/.mask(..., raise_on_error)`` (0.21.0)
- [x] #17742 ``.astype(...., categories=, ordered=)`` (0.21.0)
- [x] #17691 ``cdate_range`` (0.21.0)
- [x] #17493 ``SeriesGroupBy.nth``, for ``dropna=True`` (0.21.0) (#27168)
- [x] #17352 ``.take(..., convert=)`` param (0.21.0) (#27171)
- [x] #17346 duplicate elements in names for read_csv (0.21.0) (#27175)
- [x] #21359 compare datetime.datetime and datetime.date (0.23.0) (#27109)
## 0.24.0
- [x] #10892 Deprecate `LongPanel/WidePanel` (0.17.0), aliases since < 0.11.0 (#15748, #15802 revert); waiting on statsmodels 0.9.0 release (#18341)
- [x] ``pandas.core.datetools``, deprecated in 0.19.0 (https://github.com/pandas-dev/pandas/commit/3f3839b6cb00a7fbabfa8c0899be69c6ea088b3a) (#19119)
- [x] #14432 ambiguous name in level/column when grouping (#22415)
- [x] #14645 ``Series.repeat(reps)`` (0.20.0) (#22417)
- [x] #14645 ``Index.repeat(n)`` and ``MultiIndex.repeat(n)`` (#22574)
- [x] #15257 the ``as_indexer`` keyword in `str.match()` (0.20.0) (#22626)
- [x] #14645 ``Categorical.searchsorted(v)`` and ``Series.searchsorted(v)`` (#22670)
- [x] #14645 ``TimedeltaIndex.searchsorted(key)``, ``DatetimeIndex.searchsorted(key)``, and ``PeriodIndex.searchsorted(key)`` (#22670)
- [x] #15537 ``pandas.json`` (#22737)
- [x] #14686 ``SparseArray.to_dense(fill)`` (0.20.0) (#22910)
- [x] #14686 ``SparseSeries.to_dense(sparse_only)`` (0.20.0) (#22910)
- [x] #14967 ``.astype(raise_on_error)`` (0.20.0) (#23374)
- [x] #15099 ``Series.sortlevel`` and ``DataFrame.sortlevel`` (0.20.0) (#23375)
- [x] #15358 import of ``concat`` from ``pandas.tools.merge`` (#23376)
- [x] #16005 top-level scatter_matrix and plot_params + plotting methods in pandas.tools.plotting (0.20.0) (#23376)
- [x] #15501 ``.consolidate`` (0.20.0) (#23377)
- [x] #15537 ``pandas.parser``, ``pandas.lib``, ``pandas.tslib`` (0.20.0) (#23378)
- [x] #15538 ``pd.pnow()``, ``pd.match()``, ``pd.groupby()``, ``pd.Expr``, ``pd.Term`` (0.20.0) (#23380)
- [x] #15940 ``pd.get_store()`` (0.20.0) (#23380)
- [x] #16157 ``pandas.computation.expressions.set_use_numexpr()`` (0.20.0) (#23386)
- [x] #16157 ``pandas.types.concat.union_categorical()`` (0.20.0) (#23386)
- [x] #16250 ``shims for util and test`` (0.20.0) (#23386)
- [x] #16189 ``pandas.api.types.is_sequence`` (0.20.0) (#23390)
- [x] #16163 ``pandas.api.types.is_any_int_dtype``, ``is_floating_dtype`` (0.20.0) (#23390)
- [x] #15987 ``.astype(np.datetime64)`` and ``.astype(np.timedelta64)``, unit-less astypes (0.20.0) (#23392)
## 0.23.0
- [x] #11603 `freq`, `how` keywords (0.18.0) (#18601)
- [x] #13706 `pivot_annual` (0.19.0) (#18370)
- [x] #13739 `isleapyear` (0.19.0) (#18370)
- [x] #13358 `pd.ordered_merge` -> `pd.merge_ordered` (0.19.0) (#18459)
- [x] #11603 `freq` from ``.rolling/.expanding`` (#18601)
- [x] #11603 `how` from ``.rolling/.expanding`` (#18668)
- [x] #13735 import errors for io.data and io.wb (0.19.0, but deprecated in 0.17.0) (#18612)
- [x] #14007 `SparseList` (0.19.0) (#18621)
- [x] #13854 `Categorical.from_array` (0.19.0) (#18642)
- [x] #13386 `skip_footer` in `pd.read_csv` (0.19.0) (#18724)
- [x] #13373 `as_recarray` in `pd.read_csv` (0.19.0) (#18804)
- [x] #13360 `compact_ints` and `use_unsigned` in `pd.read_csv` (0.19.0) (#18851)
- [x] #13593 `.freq` in `Timestamp` rather than `.offset` (0.19.0) (#18927)
- [x] #13012 `Series.reshape` and `Categorical.reshape` (0.19.0) (#18954)
- [x] #11603 `pd.rolling_*`, `pd.expanding_*`, `pd.ewm*` (0.18.0); waiting on `dask` release (#18723)
- [x] #13874 `pandas.tseries.frequencies.get_standard_freq` & `to_offset` freqstr kw (0.19.0) (#19023)
- [x] #13776 `Panel4D\Panelnd` (0.19.0) (#19059)
- [x] #19107 ``display.line_width`` and ``display.height``
- [x] ``Categorical.labels`` (#19120)
- [x] ``flavor`` parameter of ``SQLiteDatabase`` (https://github.com/pandas-dev/pandas/commit/8acfad343c88760a6d09fea221996dd50393fa8a) (#19121)
- [x] ``str.extract(expand=None)`` (https://github.com/pandas-dev/pandas/commit/67730ddf46cc16c9c59a3260d37ccaba2603663a) (#19118)
- [x] #16223 ``pandas.tools.hashing``, ``pandas.util.hashing`` (0.20.0) (#19181)
- [x] #13147 `pandas.core.common` dtype introspection functions (0.19.0) https://github.com/pandas-dev/pandas/pull/19769
- [x] resample compat #20554
- [x] ``axis=None`` param in ``.replace()`` #20789
## 0.21.0
- [x] #10967 remove `ExcelWriter.has_index_names` (0.17.0) (PR #16522)
- [x] #12882 `Categorical.sort` (0.18.1) (PR #16728)
- [x] #12190 `options.display.mpl_style` (0.18.0) (PR https://github.com/pandas-dev/pandas/pull/16761)
- [x] #12591 `Index.sym_diff` (0.18.1) (PR https://github.com/pandas-dev/pandas/pull/16760)
- [x] #11149 `pd.eval` change `inplace=False` rather than `inplace=None` (0.18.0) (PR #16732)
- [x] #11834 `get_offset_name` removed (0.18.0) (PR #16863)
- [x] #2881 ``pd.options.display.line_width`` (PR #16993)
- [x] #3663 ``pd.options.display.height`` (PR #16993)
## 0.20.0
- [x] #10890 remove `TimeSeries` alias (0.17.0) (PR #15098)
- [x] #11135 remove `Series.is_timeseries` (0.17.0) (PR #15098)
- [x] #11308 `pandas.io.ga` (0.17.1) -> (PR #15223)
- [x] #11898 `pd.fama_macbeth`, `pd.ols`, can also remove `statsmodels` as a dep (0.18.0), (PR #15353)
- [x] #10711 `.irow`, `.icol`, `.iget_value` (from 0.11.0, but deprecated in 0.17.0) (PR #15547)
- [x] #10711 `DataFrame.iterkv` (pretty old, but changed `DeprecationWarning` to `FutureWarning` only in 0.17) (PR #15650)
- [x] #10632 `Categorical.name` (0.17.0) (PR #15654)
- [x] #11121 `generate_bq_schema` remove as no longer needed (0.17.0) (PR #15484)
- [x] #10458 `testing.assert_isinstance` (0.17.0) (PR #15652)
- [x] #10236`take_last` kw of `duplicated` and `drop_duplicates` (0.17.0) (#15710)
- [x] #10920`take_last` kw of `Series.nlargest` and `nsmallest` (0.17.0) (#15710)
- [x] #10792 `take_last` kw of `Groupby.nlargest/nsmallest`(0.17.0) (#15710)
- [x] #10726 `Series.order,sort`, `Index.order`, `DataFrame.sort` (0.17.0) (#15735)
- [x] #12027 back-compat for where with non-strings in `.select(...., where=)` for `HDFStore` (0.11.0/0.18.0) (#https://github.com/pandas-dev/pandas/pull/15798)
- [x] #10735 `combineAdd` / `combineMult` (0.17.0) (#15805)
- [x] #10748 Deprecate having `np.nan` in `Categorical.categories` (0.17.0) (#15806)
## 0.19.0
- [x] Deprecate colSpace in favor of col_space (no issue, commit 4a5a677) (PR #13857)
- [x] #8227 '+'/'-' for Index set ops (0.15.0) -> https://github.com/pydata/pandas/pull/14127
- [x] #9094 make DTI-DTI subtraction yield a `TimeDeltaIndex` (rather then a set op `.difference()`) (0.16.0) -> #14164
- [x] #9357, #3445 remove trellis plot. (0.16.0) (PR #13855)
- [x] #6910 Change `Panel.shift`'s signature to match `generic.shift()`'s (0.14.0) -> #14041
- [x] `flavor='mysql'` in `to_sql` (0.14.0) -> #13611)
- [x] `tquery`/`uquery` functions (0.14.0) -> #13616
- [x] #11274 `engine` kw in `to_csv()`, from 0.13 (0.17.1), #13419
- [x] #8376 remove `.levels` from `Categorical` constructor / accessor (0.15.0), #13612
- [x] #8486 remove `outtype` in `.to_dict()` replaced by `orient` (0.15.0), #13627
- [x] #8227 `diff` for Index set ops (0.15.0), #13669
- [x] #10951 Legacy offsets (0.17.0) (#13590)
- [x] #9615 remove `pandas.sandbox` (0.16.0), #13670
- [x] #9611, remove ability to set `cat.ordered` directly (0.16.0), #13671
- [x] #11157 `SparsePanel` (0.17.0), #13778
- [x] #6919 Remove `copy` keyword from `xs`,`minor_xs`,`major_xs` (4 places) (0.14.0), #13781
- [x] #10085 remove `return_type='series'|'frame'` (replaced by `expand=`) (0.16.1), #13701
- [x] `to_wide` deprecated since 2011 (https://github.com/pydata/pandas/commit/1134c9f6c40398d8eba37c312c88ce294e31c6c5) -> #14039
- [x] boxplot https://github.com/pydata/pandas/pull/7096 (0.14.0) (#12216)
- [x] change default of `return_type` from None to `'axes'`
- [x] update return_type section in visualization.rst
## 0.18
- [x] #4950 - `rolling_corr_pairwise()`, `expanding_corr_pairwise()` replace by `rolling_corr()`, `expanding_corr()` (0.14.0), in #11603
- [x] #6680, cols was deprecated in DataFrame.to_duplicated and drop_duplicates (0.14.0), #12165
- [x] #6292, sql functions (#12205)
- [x] #6930 factorize took an `order` argument but didn't do anything with it. (0.14.0) (https://github.com/pydata/pandas/pull/12274)
- [x] #4892 Float indexers in non-float indexes. (0.14.0) (#12246)
- [x] encoding `items` (in favor of `locs`) in msgpack (#10623) (0.17.0) (#12129)
## 0.17
- [x] Deprecate colSpace in favor of col_space (no issue, commit 4a5a677c) (0.8.0 I think) -> Removed #10890
- [x] #2304 Deprecate broadcasting TimeSeries along DataFrame index (0.10.0) -> Removed #10890
- [x] #5231 `na_last` arg from Series.sort (0.14.0) -> Removed #10726
- [x] #7088 Deprecate percentile_width in favor of percentiles (0.14.0) -> Removed in #10881
- [x] #3787 Deprecate load and save in favor of pickle and to_pickle (0.12.0) -> Removed #10892
- [x] #4853, #4864 Deprecate timeRule and offset in favor of freq () (0.13.0) -> Removed #10892
- [x] #4713 Remove kind from read_excel (0.13.0) -> Removed #10892
- [x] #4770, #7032 Deprecate infer_types to have no effect (0.13.0) -> Removed #10892
- [x] #4645 Deprecate table keyword in HDFStore (0.13.0) -> Removed #10892
## 0.16
- [x] #5505, DataFrame.pivot_table and crosstab were refactored to use the same arguments as pivot (index and columns) and FutureWarnings added for the old arguments. Per @jreback, in 0.16, these deprecated arguments should be removed and the docstring updated. This issue is submitted as a reminder to do so and should be tagged with a 0.156 milestone.
- [x] #6686, cols was deprecated in DataFrame.to_excel and to_csv (rows is not deprecated, don't touch it!)
- [x] #8140, remove `covert_dummies` from `core/reshape` entirely. Deprecated in favor of enhanced `get_dummies`. Deprecated as of 0.15
- [x] #8481, remove `pandas.tools.util.py/value_range`
## 0.15
- [x] `DataFrame.delevel` already deprecated in (0.7) | {
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} | 1 | 2016-07-24T18:47:49Z | 2016-07-25T11:21:29Z | 2016-07-25T11:21:29Z | NONE | null | Much simpler example
```
In [1]: df = pandas.DataFrame({ 'x' : [], 'y' : [], 'z' : []})
In [2]: df
Out[2]:
Empty DataFrame
Columns: [x, y, z]
Index: []
In [3]: df.applymap(lambda x: x)
Out[3]:
x NaN
y NaN
z NaN
dtype: float64
In [4]: df = pandas.DataFrame({ 'x' : [1], 'y' : [1], 'z' : [1]})
In [5]: df.applymap(lambda x: x)
Out[5]:
x y z
0 1 1 1
```
The docs for [Dataframe.applymap](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.applymap.html) clearly say it will return a DataFrame. However that does not happen if you call it on a DataFrame with no rows -- it returns a series. Not only that, the series has values in it, which is not what anyone would expect from an empty DataFrame.
This is with pandas 0.18.0.
```
#For our use case, an 0-row dataframe is 100% legitimate. This experiment really did return 0 rows.
df = pandas.DataFrame({ 'x' : [], 'y' : [], 'z' : []}) #empty dataframe
len(df) #returns 0, just what you'd expect from an empty dataframe.
#Strip off whitespace from any strings in the dataframe. Obviously not needed for this empty
#dataframe, but would be needed on a dataframe that had rows. (Yes, I know there's alternative
#ways to do this, I'm not looking for workarounds. I'm focused on how applymap works.)
df2 = df.applymap(lambda x : x.strip() if isinstance(x, str) and len(x) > 0 else x)
len(df2) #WHOA! This is 3, not 0. How'd that happen?
type(df2) #Oh, it returned a series instead of a dataframe. The docs do NOT say it does that.
#The docs says it always returns a DataFrame, which is what most developers would expect.
```
Almost anyone would expect applymap() on an empty DataFrame to return an empty DataFrame, not convert it to a Series with NaNs. We should change it to return an empty DataFrame.
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| null | 2 | 2016-07-24T20:46:48Z | 2016-08-09T23:24:01Z | 2016-08-09T23:24:01Z | NONE | null | Small change to fix #13522.
Upgrading from 0.17 to 0.18 caused .copy() to no longer be called.
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} | 2 | 2016-07-24T21:44:10Z | 2016-07-25T21:58:01Z | 2016-07-25T21:57:33Z | MEMBER | null | Title is self-explanatory. Picks up where #6919 left off.
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} | 7 | 2016-07-25T02:35:39Z | 2016-07-27T10:40:12Z | 2016-07-27T10:39:32Z | CONTRIBUTOR | null | - [x] closes #13746
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
This is my first time contributing to the docs, I will gladly take any criticism.
Thanks
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} | 10 | 2016-07-25T10:36:10Z | 2018-07-02T23:44:12Z | 2018-07-02T23:44:12Z | NONE | null | I've already explained the problem [link](http://stackoverflow.com/questions/38563047/concat-dataframes-in-pandas-with-different-frequency-removes-the-timezone?noredirect=1#comment64520255_38563047).
When I merge 2 dataframes, using join() or concat() (I didn't try with merge) and one of these has been previously resampled, averaging the values each 15 minutes, i loose the timezone
#### Code Sample, a copy-pastable example if possible
```
df1
2016-07-05 11:30:00+01:00 -100.81
2016-07-05 11:31:00+01:00 -99.34
2016-07-05 11:32:00+01:00 -95.09
..............
..............
2016-07-05 11:45:00+01:00 -83.62
2016-07-05 11:46:00+01:00 -1.57
2016-07-05 11:47:00+01:00 21.01
..............
```
```
df2['column1']
2016-07-05 11:30:00+01:00 -79,45
2016-07-05 11:45:00+01:00 -51.11
2016-07-05 12:00:00+01:00 -12.67
2016-07-05 12:15:00+01:00 15.21
..........
```
```
print df1.index
DatetimeIndex(['2016-07-05 11:30:00+01:00', '2016-07-05 11:31:00+01:00',
'2016-07-05 11:32:00+01:00', '2016-07-05 11:33:00+01:00',
...
'2016-07-19 14:30:00+01:00', '2016-07-19 14:31:00+01:00'],
dtype='datetime64[ns, Europe/London]', length=1358, freq=None)
print df2.index
DatetimeIndex(['2016-07-05 11:30:00+01:00', '2016-07-05 11:45:00+01:00',
'2016-07-05 12:00:00+01:00', '2016-07-05 12:15:00+01:00',
...
'2016-07-19 14:30:00+01:00', '2016-07-19 14:45:00+01:00'],
dtype='datetime64[ns, Europe/London]', length=1358, freq=None)
```
`df1 = df1.resample('15Min').mean()`
```
print df1.index
DatetimeIndex(['2016-07-05 11:30:00+01:00', '2016-07-05 11:45:00+01:00',
'2016-07-05 12:00:00+01:00', '2016-07-05 12:15:00+01:00',
...
'2016-07-19 14:30:00+01:00'],
dtype='datetime64[ns, Europe/London]', length=1358, freq='15T')
```
`df = pd.concat([df1, df2], axis=1)`
```
print df.index
DatetimeIndex(['2016-07-05 11:30:00+01:00', '2016-07-05 11:45:00+01:00',
'2016-07-05 12:00:00+01:00', '2016-07-05 12:15:00+01:00',
...
'2016-07-19 14:30:00+01:00'],
dtype='datetime64[ns, UTC]', length=1358, freq='15T')
```
#### Expected Output
```
print df.index
DatetimeIndex(['2016-07-05 11:30:00+01:00', '2016-07-05 11:45:00+01:00',
'2016-07-05 12:00:00+01:00', '2016-07-05 12:15:00+01:00',
...
'2016-07-19 14:30:00+01:00'],
dtype='datetime64[ns, Europe/London]', length=1358, freq='15T')
```
The output looses the timezone and it comes back to 'UTC'.
I think the problem is related to this [issue](https://github.com/pydata/pandas/issues/12898), where the same problem comes out when aggregating.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-28-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 24.0.1
Cython: None
numpy: 1.11.1
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 4.2.1
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
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} | 1 | 2016-07-25T11:12:58Z | 2016-08-16T09:57:38Z | 2016-08-16T09:57:38Z | CONTRIBUTOR | null | {
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} | 7 | 2016-07-25T11:17:06Z | 2016-07-25T23:36:15Z | 2016-07-25T23:36:15Z | CONTRIBUTOR | null | This is imported via `pandas.io.api`. I think making a sub-class might work that has the deprecation.
This is used internally, just want to remove the external visibility (as not been necessary for quite a few releases).
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} | 2 | 2016-07-25T12:06:13Z | 2016-07-28T23:41:41Z | 2016-07-28T23:41:41Z | CONTRIBUTOR | null | use Tempita support to provide templates for routines in `hashtable.pyx`. Lots of duplicated code by dtype that can easily use fused types, so code generation is needed. Following along the lines of #13716 would be nice.
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} | 16 | 2016-07-25T15:11:04Z | 2016-08-02T10:42:11Z | 2016-08-02T10:41:49Z | CONTRIBUTOR | null | - [X] 1 test added & passed
- [X] passes `git diff upstream/master | flake8 --diff`
This changes `SparseDataFrame` to use proper subclassing functionality so slicing of subclasses of `SparseDataFrame` works. Example of a failure that this PR fixes:
``` python
import pandas as pd
class DenseSubclassDF(pd.DataFrame):
_constructor = property(lambda self: DenseSubclassDF)
_constructor_sliced = property(lambda self: DenseSubclassS)
class DenseSubclassS(pd.Series):
_constructor = property(lambda self: DenseSubclassS)
_constructor_expanddim = property(lambda self: DenseSubclassDF)
class SparseSubclassDF(pd.SparseDataFrame):
_constructor = property(lambda self: SparseSubclassDF)
_constructor_sliced = property(lambda self: SparseSubclassS)
class SparseSubclassS(pd.SparseSeries):
_constructor = property(lambda self: SparseSubclassS)
_constructor_expanddim = property(lambda self: SparseSubclassDF)
ddf = DenseSubclassDF([[1,2,3], [4,5,6], [7,8,9]])
sdf = SparseSubclassDF([[1,2,3], [4,5,6], [7,8,9]])
print(type(ddf.iloc[0])) # <class '__main__.DenseSubclassS'>
print(type(ddf.iloc[:2])) # <class '__main__.DenseSubclassDF'>
print(type(ddf[:2])) # <class '__main__.DenseSubclassDF'>
# sparse doesn't preserve types
print(type(sdf.iloc[0])) # <class '__main__.SparseSubclassS'>
print(type(sdf.iloc[:2])) # <class 'pandas.sparse.frame.SparseDataFrame'>
print(type(sdf[:2])) # <class 'pandas.sparse.frame.SparseDataFrame'>
```
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} | 25 | 2016-07-25T17:34:16Z | 2016-07-31T19:06:36Z | 2016-07-27T10:49:48Z | CONTRIBUTOR | null | ### Summary
The pull request:
- [x] closes #13703;
- [x] passes all tests in `test.sh` except one (see the output);
- [x] passes the code style test `git diff upstream/master | flake8 --diff`;
### Details
Basically the function [**parse_trim_buffers**](https://github.com/pydata/pandas/blob/master/pandas/src/parser/tokenizer.c#L1217) did not properly move word pointers in **parser->words** and related fields. This pull request aims at fixing that.
Changes in **parse_trim_buffers**:
- block **L1239 -- L1256** (**/* trim stream */**) was augmented with **parser->words** update loop;
- blocks **L1224 -- L1237** (**/* trim words, word_starts */**) and **L1239 -- L1256**(**/* trim stream */**) were swapped to preserve pointer consistency;
### Output of `test.sh`:
```
======================================================================
FAIL: test_round_trip_frame_sep (pandas.io.tests.test_clipboard.TestClipboard)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/ivannz/Github/pd_fork/pandas/pandas/io/tests/test_clipboard.py", line 73, in test_round_trip_frame_sep
self.check_round_trip_frame(dt, sep=',')
File "/Users/ivannz/Github/pd_fork/pandas/pandas/io/tests/test_clipboard.py", line 69, in check_round_trip_frame
tm.assert_frame_equal(data, result, check_dtype=False)
File "/Users/ivannz/Github/pd_fork/pandas/pandas/util/testing.py", line 1276, in assert_frame_equal
right.columns))
File "/Users/ivannz/Github/pd_fork/pandas/pandas/util/testing.py", line 1022, in raise_assert_detail
raise AssertionError(msg)
AssertionError: DataFrame are different
DataFrame shape (number of columns) are different
[left]: 2, Index([u'en', u'es'], dtype='object')
[right]: 0, Index([], dtype='object')
----------------------------------------------------------------------
Ran 10377 tests in 1197.728s
FAILED (SKIP=363, failures=1)
```
Apart from this, there were a couple of _deprecation warnings_ in files other that _tokenizer.c_.
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} | 1 | 2016-07-25T22:13:13Z | 2016-07-25T22:29:16Z | 2016-07-25T22:29:16Z | NONE | null | This seems like a bug to me. Can't really figure out why this is happening. Calling `sys.getsizeof` on a FrozenList returns `TypeError: must be type, not FrozenList`
#### Code Sample, a copy-pastable example if possible
```
from pandas.core.base import FrozenList
import sys
fl = FrozenList()
sys.getsizeof(fl)
```
#### Expected Output
Some Integer
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-74-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.2
setuptools: 19.7
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.1.1
sphinx: 1.4.1
patsy: None
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: 0.7.4.None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
```
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} | 2 | 2016-07-25T22:20:46Z | 2017-03-03T10:12:22Z | 2017-03-03T10:12:21Z | MEMBER | null | Triggered by #13785, I looked at the top level namespace, and I was wondering if the following functions are needed:
- [ ] `pd.Expr`
- [ ] `pd.pnow()`: returns the current date as a Period with specified freq (https://github.com/pydata/pandas/issues/12641)
- [ ] `pd.info()`
- [ ] `pd.groupby()`
- [ ] `match`: "Compute locations of to_match into values" (imported from `core/api.py` from `\core\algorithms.py` together with `factorize`, `unique` and `value_counts`, but I don't think `match` is used anywhere in the docs)
- [ ] `pd.Term`
Quite a lot are already deprecated: `TimeSeries`, `SparseTimeSeries`, all `rolling_` and `expanding_` methods
I also learned the existence of `pd.lreshape` and `pd.plot_params` .. :-) (but the latter is mentioned in the docs, `lreshape` not).
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} | 4 | 2016-07-25T23:07:58Z | 2016-07-29T00:08:42Z | 2016-07-29T00:08:33Z | MEMBER | null | - [x] closes #6194
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
after this PR:
```
idx = pd.Index([1, 2, np.nan, 4])
idx.dropna()
# Float64Index([1.0, 2.0, 4.0], dtype='float64')
```
MI has option `how` to drop if any/all levels are NA (the same as `DataFrame`)
```
midx = pd.MultiIndex.from_arrays([[1, 2, np.nan, 4], [1, 2, np.nan, np.nan]])
midx.dropna()
# MultiIndex(levels=[[1, 2, 4], [1, 2]],
# labels=[[0, 1], [0, 1]])
midx.dropna(how='all')
# MultiIndex(levels=[[1, 2, 4], [1, 2]],
# labels=[[0, 1, 2], [0, 1, -1]])
```
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} | 11 | 2016-07-25T23:21:30Z | 2016-07-29T00:20:39Z | 2016-07-29T00:20:27Z | CONTRIBUTOR | null | - [x] closes #13322
- [x] tests passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Fixed bug in pytables.py where series with categories were not being filtered properly.
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} | 2 | 2016-07-25T23:44:00Z | 2016-07-27T10:22:38Z | 2016-07-27T10:22:38Z | CONTRIBUTOR | null | xref #13749
this is not consistent with other Indexes where we don't allow them to be constructed with no data passed (it can be `None`), but that's the idea. `start` must be not-None.
IOW this should raise `ValueError`
```
In [3]: pd.RangeIndex()
Out[3]: RangeIndex(start=0, stop=0, step=1)
```
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} | 6 | 2016-07-26T00:18:40Z | 2016-07-26T09:02:21Z | 2016-07-26T08:46:33Z | CONTRIBUTOR | null | The current asv usage instructions in contributing.rst may be unclear for PRs with multiple commits.
By default `asv continuous` uses head^ as the commit to compare to, whereas for PRs you usually want to compare to master. Write the instructions so that the comparison point is clear.
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} | 2 | 2016-07-26T00:49:37Z | 2016-07-26T18:31:37Z | 2016-07-26T10:44:46Z | NONE | null | # Summary
Passing 'nunique' to resample produces incorrect output. However, passing `pd.Series.nunique` works.
#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
from pandas import Timestamp
time = [Timestamp('2016-06-28 09:35:35'), Timestamp('2016-06-28 16:09:30'), Timestamp('2016-06-28 16:46:28')]
data = ['1', '2', '3']
test = pd.DataFrame({'time':time, 'data':data})
display(test.set_index('time').resample('H').apply({'data':'nunique'}))
display(test.set_index('time').resample('H').apply({'data':pd.Series.nunique}))
```
#### Expected Output
(INCORRECT, in bold)
2016-06-28 09:00:00 1
2016-06-28 10:00:00 0
2016-06-28 11:00:00 0
2016-06-28 12:00:00 0
2016-06-28 13:00:00 0
2016-06-28 14:00:00 0
2016-06-28 15:00:00 0
2016-06-28 16:00:00 **1**
(CORRECT, in bold)
2016-06-28 09:00:00 1
2016-06-28 10:00:00 0
2016-06-28 11:00:00 0
2016-06-28 12:00:00 0
2016-06-28 13:00:00 0
2016-06-28 14:00:00 0
2016-06-28 15:00:00 0
2016-06-28 16:00:00 **2**
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 4.1.10-17.31.amzn1.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 20.3.1
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
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} | 12 | 2016-07-26T01:39:52Z | 2017-04-05T02:08:08Z | 2016-09-02T11:30:54Z | CONTRIBUTOR | null | closes #8725
Changes `get_dummies` return columns with `uint8` dtypes instead of coercing to floats if they were alongside other float columns.
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} | 8 | 2016-07-26T01:55:40Z | 2016-09-13T22:25:32Z | 2016-09-13T22:25:32Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
``` python
import pandas as pd
import numpy as np
ind = [
['AA','AA','AA','BB','BB'],
['A' ,'B' ,'B' ,'a' ,'b']
]
ind_nonLex = [
['CC','CC','CC','BB','BB'],
['A' ,'B' ,'B' ,'a' ,'b']
]
strCol=pd.DataFrame([['fooA'],['fooB'],['fooC'],['fooD'],['fooE']])
dat=np.arange(1,26).reshape(5,5)
df=pd.concat([strCol, pd.DataFrame(dat)], axis=1)
df1=pd.DataFrame(df.values, index=ind_nonLex)
df2=pd.DataFrame(df.values, index=ind)
```
df1 (whose index is not lex sorted) fails with iloc access:
```
>>> df1.iloc[0,0]
C:\Users\rikuhiro\Anaconda3\envs\pd-check\lib\site-packages\ipykernel\__main__.py:1: PerformanceWarning: indexing past lexsort depth may impact performance.
if __name__ == '__main__':
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-37-884ae2904642> in <module>()
----> 1 df1.iloc[0,0]
C:\Users\rikuhiro\Anaconda3\envs\pd-check\lib\site-packages\pandas\core\indexing.py in __getitem__(self, key)
1292
1293 if type(key) is tuple:
-> 1294 return self._getitem_tuple(key)
1295 else:
1296 return self._getitem_axis(key, axis=0)
C:\Users\rikuhiro\Anaconda3\envs\pd-check\lib\site-packages\pandas\core\indexing.py in _getitem_tuple(self, tup)
1561
1562 # if the dim was reduced, then pass a lower-dim the next time
-> 1563 if retval.ndim < self.ndim:
1564 axis -= 1
1565
AttributeError: 'str' object has no attribute 'ndim'
```
#### Expected Output
df2 (whose index is lex sorted) works as expected:
``` python
>>> df2.iloc[0,0]
'fooA'
```
This attributeError does not occur when the DataFrame.values consist of numpy objects (e.g. numpy.int32) because they have the ndim attribute. (Although the performance warning remains, it may be another issue).
I found that the addition of an **if** statement can remedy this in pandas/core/indexing.py. This just makes the **_getitem_tuple(self, tup)** be aware of objects without the ndim attribute, as **_getitem_nested_tuple(self, tup)** is (I will prepare a pull request if it is helpful.)
``` python
@@ -1569,6 +1569,10 @@ def _getitem_tuple(self, tup):
retval = getattr(retval, self.name)._getitem_axis(key, axis=axis)
+ # if we have a scalar, we are done
+ if lib.isscalar(retval) or not hasattr(retval, 'ndim'):
+ break
+
# if the dim was reduced, then pass a lower-dim the next time
if retval.ndim < self.ndim:
axis -= 1
```
#### output of `pd.show_versions()`
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 23.0.0
Cython: None
numpy: 1.11.1
scipy: None
statsmodels: None
xarray: None
IPython: 5.0.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/13799 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13799/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13799/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13799/events | https://github.com/pandas-dev/pandas/issues/13799 | 167,533,602 | MDU6SXNzdWUxNjc1MzM2MDI= | 13,799 | Joining on a column, not index, gives erroneous result | {
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} | 4 | 2016-07-26T06:53:10Z | 2019-10-21T19:52:16Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd
d1 = pd.DataFrame({'a': [1, 2], 'b': [11, 22]})
d2 = pd.DataFrame({'a': [1, 2], 'c': ['aa', 'bb']})
d1.join(d2, on=['a'], how='outer', lsuffix='.d1', rsuffix='.d2')
```
#### Actual output
```
a a.d1 b a.d2 c
0 1.0 1.0 11.0 2.0 bb
1 2.0 2.0 22.0 NaN NaN
1 0.0 NaN NaN 1.0 aa
```
#### Expected Output
```
a b c
0 1 11 aa
1 2 22 bb
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-65-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 1.5.4
setuptools: 3.3
Cython: None
numpy: 1.10.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.0.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.6
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: 0.9.2
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
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https://api.github.com/repos/pandas-dev/pandas/issues/13800 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13800/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13800/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13800/events | https://github.com/pandas-dev/pandas/pull/13800 | 167,553,953 | MDExOlB1bGxSZXF1ZXN0Nzg4MTM4OTc= | 13,800 | TST: Add test for skipfooter + decimal in read_csv | {
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} | 5 | 2016-07-26T08:54:02Z | 2016-07-27T00:55:35Z | 2016-07-26T22:44:17Z | MEMBER | null | Title is self-explanatory. Only for Python engine because C engine doesn't support `skipfooter` yet.
Closes #6971.
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} | 6 | 2016-07-26T11:04:18Z | 2016-08-16T22:34:49Z | 2016-08-16T22:34:43Z | MEMBER | null | xref #2579
This at least solves the direct negative consequence (erroring code by importing pandas) of registering our converters by default.
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https://api.github.com/repos/pandas-dev/pandas/issues/13802 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13802/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13802/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13802/events | https://github.com/pandas-dev/pandas/pull/13802 | 167,593,953 | MDExOlB1bGxSZXF1ZXN0Nzg4NDE4MDk= | 13,802 | BUG: TypeError in merge with timedelta64 column | {
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} | 1 | 2016-07-26T12:22:23Z | 2016-07-26T22:49:35Z | 2016-07-26T22:49:35Z | MEMBER | null | - [x] closes #13389
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
A root cause is `isnull` raises `TypeError` if datetime-like has other time unit than `ns`.
```
pd.isnull(np.array([1, 2], dtype='datetime64[D]'))
# TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
```
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} | 3 | 2016-07-26T12:24:54Z | 2016-07-27T10:23:05Z | 2016-07-27T10:22:38Z | MEMBER | null | - [x] closes #13793
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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