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} | 9 | 2016-07-10T06:04:42Z | 2017-01-04T08:24:51Z | 2017-01-04T08:24:51Z | NONE | null | ``` python
#!/usr/bin/env python3
from io import StringIO
import pandas as pd
test_timeseries = """\
2008-02-07 09:40,1032.43
2008-02-07 09:50,1042.54
2008-02-07 10:00,1051.65
"""
df = pd.read_csv(StringIO(test_timeseries), parse_dates=[0],
usecols=['date', 'value'], index_col=0, header=None,
names=('date', 'value'))
print (df.value.dtype)
```
I run this program 10 times and the result is sometimes `float64` and sometimes `object`.
This happens with pandas 0.18.1 on Debian Jessie amd64 with Python 3.4.2 and numpy 1.11.1. I don't see it happening with Debian's packaged pandas 0.14.1.
I can work around this by specifying the `dtype` argument; but shouldn't pandas behave deterministically when it's omitted?
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- [x] passes `git diff upstream/master | flake8 --diff`
Cleanup duplicated code paths before fixing some ops related issues.
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- [x] passes `git diff upstream/master | flake8 --diff`
Remove workaround added in #353. The test added at that time has been passed.
- https://github.com/pydata/pandas/blob/master/pandas/tests/series/test_operators.py#L1264
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} | 7 | 2016-07-10T15:35:45Z | 2016-07-13T02:20:32Z | 2016-07-13T02:17:23Z | CONTRIBUTOR | null | - [x] passes `git diff upstream/master | flake8 --diff`
This commit suppresses these warnings
warning: comparison of constant -1 with expression\
of type 'PANDAS_DATETIMEUNIT' is always true\
[-Wtautological-constant-out-of-range-compare]
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} | 28 | 2016-07-10T20:35:40Z | 2016-08-18T10:49:51Z | 2016-08-18T10:49:30Z | CONTRIBUTOR | null | - [x] closes #13577
- [x] passed 1 new test
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 5 | 2016-07-10T22:39:40Z | 2016-07-15T00:32:17Z | 2016-07-15T00:30:34Z | MEMBER | null | - [x] closes #12759, #13582
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
- `pd.Period('NaT', freq='D')` now returns `pd.NaT` rather than its own nat repr.
- To be compat with `Period` arithmetic, `pd.NaT` now supports addition / subtraction with `int` (previously `ValueError`).
```
pd.NaT + 1
# pd.NaT
```
Added tests for following:
- [x] `Period` and `PeriodIndex`, `Series` ops
- [x] add, sub, comp
- [x] `PeriodIndex` creation from list-like which contains `Period` and `NaT` (#13430)
- [x] `PeriodIndex` boxing
- [x] `.to_period`
- [x] `__contains__` any NaT-like(`pd.NaT, None, float('nan'), np.nan`)
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} | 2 | 2016-07-10T22:52:51Z | 2016-07-15T00:10:18Z | 2016-07-15T00:10:16Z | MEMBER | null | - [x] follow-up for #13593
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added tests and fixed missed doc change.
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} | 4 | 2016-07-11T07:32:53Z | 2016-07-15T10:30:43Z | 2016-07-15T10:28:35Z | MEMBER | null | Deprecated back in `0.15.0` and therefore long overdue. Closes #8376.
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} | 2 | 2016-07-11T08:16:12Z | 2016-08-15T22:27:20Z | 2016-08-15T22:27:20Z | MEMBER | null | From the added docs in https://github.com/pydata/pandas/pull/13425:
```
In [10]: pd.to_timedelta(['apple', '1 day'], errors='ignore')
...
ValueError: unit abbreviation w/o a number
In [12]: pd.to_timedelta(['apple', pd.Timedelta('1day')], errors='ignore')
...
ValueError: unit abbreviation w/o a number
```
It's the same error as you get with `errors='raise'` (so not captured somewhere), `errors='coerce` is working correctly.
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} | 3 | 2016-07-11T10:18:00Z | 2016-07-23T19:28:48Z | 2016-07-23T19:28:48Z | MEMBER | null | Rebase of PR https://github.com/pydata/pandas/pull/13252
Closes https://github.com/pydata/pandas/issues/11886
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} | 1 | 2016-07-11T10:53:24Z | 2016-08-03T10:30:31Z | 2016-08-03T10:30:30Z | NONE | null | BigQuery now sports a SQL dialect that's compatible with the 2011 SQL standard. Queries are still run using BigQuery's Legacy SQL dialect at the moment (see https://cloud.google.com/bigquery/sql-reference/enabling-standard-sql) but they may soon switch to Standard SQL by default. `pandas.io.gbq.read_gbq()` should therefore have an argument that determines the SQL dialect that's used to run the query.
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} | 1 | 2016-07-11T12:55:20Z | 2016-07-21T11:07:00Z | 2016-07-21T11:06:59Z | MEMBER | null | Were already deprecated since 0.14
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} | 2 | 2016-07-11T13:42:11Z | 2016-07-12T07:07:41Z | 2016-07-12T07:07:41Z | MEMBER | null | Closes #9963
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} | 7 | 2016-07-11T14:04:18Z | 2016-07-19T20:46:17Z | 2016-07-19T13:15:22Z | MEMBER | null | With the latest IPython:
```
In [1]: rng = pd.date_range('20130101', periods=10, freq='T')
...: ts = pd.Series(np.arange(len(rng)), index=rng)
In [3]: r = ts.resample('2T')
In [4]: r
Out[4]: c:\users\vdbosscj\scipy\ipython-joris\IPython\utils\dir2.py:65: FutureWa
rning: .resample() is now a deferred operation
use .resample(...).mean() instead of .resample(...)
canary = getattr(obj, '_ipython_canary_method_should_not_exist_', None)
DatetimeIndexResampler [freq=<2 * Minutes>, axis=0, closed=left, label=left, con
vention=start, base=0]
In [5]: import IPython
In [6]: IPython.__version__
Out[6]: '5.0.0'
In [7]: pd.__version__
Out[7]: '0.18.1+184.g15c2a3a'
```
while using IPython 4.2:
```
In [1]: rng = pd.date_range('20130101', periods=10, freq='T')
In [2]: ts = pd.Series(np.arange(len(rng)), index=rng)
In [3]: r = ts.resample('2T')
In [4]: r
Out[4]: DatetimeIndexResampler [freq=<2 * Minutes>, axis=0, closed=left, label=l
eft, convention=start, base=0]
In [5]: import IPython
In [6]: IPython.__version__
Out[6]: '4.2.0'
In [7]: pd.__version__
Out[7]: '0.18.1+184.g15c2a3a'
```
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} | 4 | 2016-07-11T17:00:02Z | 2016-07-14T08:15:44Z | 2016-07-14T08:15:23Z | MEMBER | null | - [x] tests passed
- [x] passes `git diff upstream/master | flake8 --diff`
`merge`, `join` and `concat` related tests are mixed each other. Cleaned up these tests.
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| null | 3 | 2016-07-11T17:01:56Z | 2016-07-11T18:21:11Z | 2016-07-11T18:21:10Z | NONE | null | I am curious if there would be any interest in having COMTRADE import functionality in core pandas. This is a file format used to record electrical transient measurements in a power system. It is defined by [IEEE C37.111](https://en.wikipedia.org/wiki/Comtrade). I have an implementation that reads the files and creates a pandas dataframe. I would be willing to devote some time to creating a read_comtrade() method in pandas if there is interest. Otherwise, I will just create a third-party library.
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https://api.github.com/repos/pandas-dev/pandas/issues/13621 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13621/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13621/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13621/events | https://github.com/pandas-dev/pandas/pull/13621 | 164,886,308 | MDExOlB1bGxSZXF1ZXN0NzY5ODg2MTA= | 13,621 | TST: Move plotting related tests to tests/plotting | {
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} | 14 | 2016-07-11T17:03:57Z | 2016-07-22T12:09:20Z | 2016-07-22T12:09:11Z | MEMBER | null | - [x] related to #13579
- [x] tests passed
- [x] passes `git diff upstream/master | flake8 --diff`
Because `test_graphics` is getting huge, split them based on data types under `tests/plotting` to make visualization related tests easier.
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} | 6 | 2016-07-11T19:34:03Z | 2016-07-21T16:18:20Z | 2016-07-21T15:05:26Z | CONTRIBUTOR | null | - [X] closes #10806
- [X] tests added / passed
- [X] passes `git diff upstream/master | flake8 --diff`
- [X] whatsnew entry
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} | 3 | 2016-07-11T21:32:26Z | 2016-08-01T19:22:43Z | 2016-08-01T19:22:43Z | NONE | null | Passing `mode='w'` to `pd.read_hdf` erases the target file immediately.
I believe this behavior is undesired, because deleting data is a side effect that no method called `read_anything` should have. The fact that a mode argument is even accepted is not documented anywhere. I just lost some experimental data this way, no backup...
#### Code Sample
``` python
import pandas as pd
import numpy as np
df = pd.DataFrame(data=np.random.rand(10))
#write some data to a new hdf5 file
df.to_hdf('test.hdf','test')
#read back the data with mode='w'
df = pd.read_hdf('test.hdf','test',mode='w')
```
#### Expected Output
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-4-5659d158fdb7> in <module>()
----> 1 df = pd.read_hdf('test.hdf','test',mode='w')
C:\Anaconda2\lib\site-packages\pandas\io\pytables.pyc in read_hdf(path_or_buf, key, **kwargs)
328 'multiple datasets.')
329 key = keys[0]
--> 330 return store.select(key, auto_close=auto_close, **kwargs)
331 except:
332 # if there is an error, close the store
C:\Anaconda2\lib\site-packages\pandas\io\pytables.pyc in select(self, key, where, start, stop, columns, iterator, chunksize, auto_close, **kwargs)
660 group = self.get_node(key)
661 if group is None:
--> 662 raise KeyError('No object named %s in the file' % key)
663
664 # create the storer and axes
KeyError: 'No object named test in the file'
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
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: 1.0.0
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
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} | 1 | 2016-07-11T23:06:11Z | 2016-07-12T10:52:19Z | 2016-07-12T10:52:19Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
`Timedelta` op may raise `ValueError` for invalid inputs because of internal conversion.
```
pd.Timedelta('1 days') + 'a'
# ValueError: unit abbreviation w/o a number
```
Now it raises `TypeError`.
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| null | 9 | 2016-07-11T23:46:55Z | 2016-12-30T23:37:49Z | 2016-12-30T21:45:27Z | NONE | null | - [ X] closes #13595
- [X ] tests added / passed
- [X ] passes `git diff upstream/master | flake8 --diff`
- [X ] whatsnew entry
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} | 4 | 2016-07-12T05:25:53Z | 2016-09-03T09:20:29Z | 2016-09-03T09:20:29Z | MEMBER | null | xref #7795 #13221. Series/Index concat-like op which triggers object-coercion is not well tested. Followings are needed:
- Refactor `concat` internal to make it consistent / stabled.
- Add comprehensive tests to cover both Index/Series object-coercion cases
#### Code Sample, a copy-pastable example if possible
Found some problems below:
```
# NG. though it looks OK, the objects are python built-in (must be Timestamp and Timedelta)
s = pd.Series([pd.Timestamp('2011-01-01')]).append(pd.Series([pd.Timedelta('1 days')]))
s
#0 2011-01-01 00:00:00
#0 1 day, 0:00:00
# dtype: object
type(s.iloc[0]), type(s.iloc[-1])
# (datetime.datetime, datetime.timedelta)
```
```
# NG, the result must be object dtype contaions Timestamp and Timedelta
idx = pd.DatetimeIndex(['2011-01-01']).append(pd.TimedeltaIndex(['1 days']))
idx
# Index([2011-01-01 00:00:00, 86400000000000], dtype='object')
type(idx[0]), type(idx[-1])
# (pandas.tslib.Timestamp, int)
```
#### Expected Output
- result must be object dtype containing `Timestamp` and `Timedelta` (not `datetime` and `timedelta`)
#### output of `pd.show_versions()`
0.18.1
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} | 3 | 2016-07-12T05:48:29Z | 2016-07-12T17:19:57Z | 2016-07-12T17:19:49Z | MEMBER | null | Follows up from #8486 in `0.15.0` by removing `outtype` in `DataFrame.to_dict()`
Fortunately or unfortunately, no tests were written then to test the deprecation, so there was nothing to remove from the test suite this time around.
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} | 2 | 2016-07-12T08:46:03Z | 2019-08-16T12:14:43Z | 2019-08-16T12:14:43Z | NONE | null | On pandas 0.18.1, for some values of `a, b`
#### Code Sample
`pd.Categorical([1,2,None, None]).fillna(pd.Series([1,1, a, b]))
`
#### Expected Output
Either the same as
`pd.Categorical([1, 2, a, b])`
or
`ValueError: fill value must be in categories`
If a or b do not match the categories. This is what whe get when we do `pd.Categorical([1, 2, None, None]).fillna(3)`
#### Actual output
`ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
`
This error message seems rather unenlightening...
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```
import pandas as pd
d = pd.DataFrame([1,2,5,6,9,3,6,5,9,7,11,36,4,7,8,25,8,24])
b = [0,5,10,15,20,25,30,35,40,45,50,55]
g = d.groupby(pd.cut(d[0],b))
print g.mean()
print g.median()
print g.get_group('(0, 5]').median()
print g.get_group('(40, 45]').median()
```
#### Expected Output
```
0
0
(0, 5] 3.333333
(5, 10] 7.500000
(10, 15] 11.000000
(15, 20] NaN
(20, 25] 24.500000
(25, 30] NaN
(30, 35] NaN
(35, 40] 36.000000
(40, 45] NaN
(45, 50] NaN
(50, 55] NaN
0
0
(0, 5] 3.5
(5, 10] 7.5
(10, 15] 11.0
(15, 20] 18.0
(20, 25] 24.5
(25, 30] 30.5
(30, 35] 30.5
(35, 40] 36.0
(40, 45] 18.0
(45, 50] 18.0
(50, 55] 18.0
0 3.5
dtype: float64
Traceback (most recent call last):
File "<ipython-input-9-0663486889da>", line 1, in <module>
runfile('C:/PythonDir/test04.py', wdir='C:/PythonDir')
File "C:\Anaconda2\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 714, in runfile
execfile(filename, namespace)
File "C:\Anaconda2\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 74, in execfile
exec(compile(scripttext, filename, 'exec'), glob, loc)
File "C:/PythonDir/test04.py", line 20, in <module>
print g.get_group('(40, 45]').median()
File "C:\Anaconda2\lib\site-packages\pandas\core\groupby.py", line 587, in get_group
raise KeyError(name)
KeyError: '(40, 45]'
```
This example shows how the median-function of the groupby object outputs a random number instead of NaN like the mean-function does when a bin is empty. Directly trying to call that bin with its key leads to an error since it doesn't exist, yet the full median output suggests it does exist and that the value might even be meaningful (like in the (15, 20] bin or the (30, 35] bin). The wrong numbers that are returned can change randomly, another possible output using the same code might look like this:
```
(0, 5] 3.500000e+00
(5, 10] 7.500000e+00
(10, 15] 1.100000e+01
(15, 20] 1.800000e+01
(20, 25] 2.450000e+01
(25, 30] 3.050000e+01
(30, 35] 3.050000e+01
(35, 40] 3.600000e+01
(40, 45] 4.927210e+165
(45, 50] 4.927210e+165
(50, 55] 4.927210e+165
```
#### output of `pd.show_versions()`
`pandas: 0.18.1`
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} | 2 | 2016-07-12T15:26:07Z | 2016-07-14T14:26:07Z | 2016-07-14T14:26:07Z | MEMBER | null | Was trying to run benchmarks, and bumped into some issue (+ the DataMatrix, I just removed that as this is now removed from pandas, so not much sense having benchmarks for that)
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} | 11 | 2016-07-12T17:41:50Z | 2016-07-19T02:00:46Z | 2016-07-19T01:59:54Z | CONTRIBUTOR | null | - [x] closes #13536
- [x] closes #13535
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Add support for writing variable labels
Fix documentation for to_stata
Clean up function name to improve readability
closes #13536
closes #13535
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} | 5 | 2016-07-12T23:00:45Z | 2016-07-21T14:31:43Z | 2016-07-21T14:31:18Z | CONTRIBUTOR | null | - [x] closes #13160
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} | 4 | 2016-07-12T23:39:05Z | 2016-07-21T16:30:15Z | 2016-07-20T21:56:46Z | CONTRIBUTOR | null | - [x] closes #13599
- [x] whatsnew entry
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} | 66 | 2016-07-13T02:01:01Z | 2017-05-03T23:41:38Z | 2017-04-27T21:32:11Z | CONTRIBUTOR | null | In xref #13147
established a bit of a public api in `pandas.api`; ATM this only contains the type introspection routines.
## 1st question
Some disagreement on whether we should not do this, and rather just expose `pandas.types` directly.
I think `pandas.api` is actually very important because:
1) limits scope of what we choose to expose in the future; its not simply 'everything' that isnt nailed down (the case now). This does change pandas to make everything by default private, EXCEPT what is explicitly public.
2) a single entry point for other packages to use the pandas public API that is known / stable / maintained (will with c-API as well)
3) provide consistent naming externally (and allow us to fix / hide internally as needed)
4) namespaced. I only import _what_ I need as a user / other package, rather than _everything_
## ~~2nd question~~
as discussed [here](https://github.com/pydata/pandas/pull/13147#discussion_r70594911), should these deprecated API functions should be `DeprecationWrarning` rather than `FutureWarning`?
-> done in https://github.com/pydata/pandas/pull/13990
Ideally we should resolve this before the end of 0.19.0 (or remove it).
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} | 1 | 2016-07-13T02:08:02Z | 2019-07-13T12:04:22Z | 2019-07-13T12:04:22Z | CONTRIBUTOR | null | xref #13147
ideally add doc-strings and add to API.rst? for the introspectors
these are pretty intuitve (e.g. `is_integer`), but have some nuances that
doc-strings (and an example) would be useful.
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} | 1 | 2016-07-13T02:33:41Z | 2016-07-14T08:26:42Z | 2016-07-13T02:46:02Z | NONE | null | Hello,
Yahoo seems to have changed their site design as I no longer can pull any options data with panda.
Does anyone else experience this?
Here's what's called:
#### Code Sample, a copy-pastable example if possible
# !/usr/bin/python
import sys, getopt
import pandas as pd
import numpy as np
from pandas.io.data import DataFrame
from pandas.io.data import Options
import h5py as h5
from pandas_datareader import data, wb
import datetime
import time
ticker = str(sys.argv[1])
new_data=pd.DataFrame()
option = Options(ticker, 'yahoo')
data = option.get_all_data()
new_data = new_data.append(data)
new_data.to_csv('cache/'+ticker)
And here's what I am getting:
Traceback (most recent call last):
File "db1.py", line 18, in <module>
data = option.get_all_data()
File "/usr/local/lib/python2.7/dist-packages/pandas/io/data.py", line 1134, in get_all_data
expiry_dates = self.expiry_dates
File "/usr/local/lib/python2.7/dist-packages/pandas/io/data.py", line 1165, in expiry_dates
expiry_dates, _ = self._get_expiry_dates_and_links()
File "/usr/local/lib/python2.7/dist-packages/pandas/io/data.py", line 1191, in _get_expiry_dates_and_links
raise RemoteDataError('Data not available')
pandas.io.data.RemoteDataError: Data not available
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-24-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.7.0
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: None
sphinx: None
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2014.10
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.3
matplotlib: 1.5.1
openpyxl: 2.3.0
xlrd: 0.9.4
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.5.0
bs4: 4.4.1
html5lib: 0.999
httplib2: 0.9.1
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: 0.2.0
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} | 10 | 2016-07-13T04:15:37Z | 2021-05-01T22:35:22Z | null | MEMBER | null | xref #1134. There is another inconsistencies related to op. `Index/Series/DataFrame` op can accept 1-d list-like as input and coercing to `Index/Series`. However, supported 1-d list-likes differ depending on the class and kind of op.
#### Code Sample, a copy-pastable example if possible
```
# Series + equal length list, OK
pd.Series([1, 2, 3]) + [2, 2, 2]
#0 3
#1 4
#2 5
# dtype: int64
# Series + equal length Index, OK
pd.Series([1, 2, 3]) + pd.Index([2, 2, 2])
#0 3
#1 4
#2 5
# dtype: int64
# DataFrame + equal length list, OK
pd.DataFrame([[1, 2, 3]]) + [2, 2, 2]
# 0 1 2
#0 3 4 5
# DataFrame + equal length Index, NG
pd.DataFrame([[1, 2, 3]]) + pd.Index([2, 2, 2])
# ValueError: cannot evaluate a numeric op with unequal lengths
```
I've organize the result once as the below table. I think all these must be supported and consistent.
| Class | op | list | tuple | ndarray(1dim) | Index |
| --- | --- | --- | --- | --- | --- |
| Index | Arithmetic | x | x | o | o |
| Index | Comparison | o | o | o | o |
| Index | Boolean | o | o | o | o |
| Series | Arithmetic | o | o | o | o |
| Series | Comparison | o | o | o | o |
| Series | Boolean | o | o | o | x |
| Frame | Arithmetic | o | o | o | x |
| Frame | Comparison | o | o | o | o |
| Frame | Boolean | o | o | o | x |
NOTE: Index result may depends on its type.
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| null | 7 | 2016-07-13T06:41:03Z | 2021-05-01T22:35:40Z | null | NONE | null | Currently, with an exponential weighted moving window, there are two options, adjusted, or non-adjusted. The first one results in excessively high variance at the beginning of the series, the second results in an artificially low mean. Another alternative is to intialise the series with a scalar value - most obviously the mean. Here is a comparison of how these three variations look:
This is from a hacky iterative implementation I just wrote as a proof of concept, but it shouldn't be hard to write an implementation that's just as efficient as the adjusting version. If this seems useful, I'm happy to make a PR.
What would be the best API design? Perhaps just add an `initialise=None` that accepts floats, and overrides the `adjust=` argument if it is set?
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} | 7 | 2016-07-13T10:26:03Z | 2016-08-21T18:33:01Z | 2016-08-21T18:33:01Z | CONTRIBUTOR | null | https://travis-ci.org/pydata/pandas/jobs/144343193
@jorisvandenbossche
@TomAugspurger
@sinhrks
I just restarted the build, but failed the same way.
version change?
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} | 1 | 2016-07-13T14:25:13Z | 2016-07-13T14:38:15Z | 2016-07-13T14:38:09Z | MEMBER | null | Lots of good refactoring in #13147 , but can't use them if they aren't included!
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- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry: imho not worth it?
origin: https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=827938
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} | 0 | 2016-07-13T15:18:00Z | 2016-07-20T20:21:37Z | 2016-07-17T12:40:27Z | CONTRIBUTOR | null | `DataFrame.rename_axis()` is documented here: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.rename_axis.html
But if you go to http://pandas.pydata.org/pandas-docs/stable/api.html, it is not in the sidebar or list of all methods.
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} | 5 | 2016-07-13T16:20:52Z | 2016-07-14T10:48:47Z | 2016-07-14T10:48:43Z | CONTRIBUTOR | null | - [x] passes `git diff upstream/master | flake8 --diff`
This commit suppresses these warnings
warning: implicit conversion from enumeration type\
'NPY_DATETIMEUNIT' to different enumeration type\
'PANDAS_DATETIMEUNIT' [-Wenum-conversion]
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| null | 4 | 2016-07-13T17:02:17Z | 2016-07-14T11:26:36Z | 2016-07-14T11:26:36Z | CONTRIBUTOR | null | Hello,
I have a column of datetimes which I'm trying to group by moving them to the last day of the calendar quarter. However, I think when the datetime is on the last day of the quarter, It doesn't work.
#### Code Sample, a copy-pastable example if possible
```
In [8]: pd.Series(pd.DatetimeIndex(['2016-06-30 12:32'])).dt.date + pd.offsets.QuarterEnd()
Out[8]:
0 2016-09-30
dtype: datetime64[ns]
```
Quarter Begin appears to be even more screwed up. It is moving the date to March 1, which isn't the beginning of a quarter.
```
pd.Series(pd.DatetimeIndex(['2016-04-01 04:02'])).dt.date - pd.offsets.QuarterBegin()
Out[9]:
0 2016-03-01
dtype: datetime64[ns]
```
#### Expected Output
I expect this to be `2016-06-30`.
#### output of `pd.show_versions()`
```
In [25]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 2012ServerR2
machine: AMD64
processor: Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US
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: 0.8.0.dev0+032a9ab
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
```
I _think_ a suitable workaround for QuarterEnd is to convert the datetime to a date, then subtract a day, then move to QuartrEnd. (Untested).
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| null | 0 | 2016-07-13T20:34:08Z | 2021-05-01T22:36:24Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd
import numpy as np
index=list(range(8))
columns=pd.MultiIndex.from_product([['alpha', 'beta',], ['C','D'],[1,2]], names=['first', 'second','third'])
df1=pd.DataFrame(index=index,columns=columns,data=np.random.rand(8,8))
df2=pd.DataFrame(index=index,columns=columns,data=np.random.rand(8,8))
pd.merge(df1,df2,left_index=True,right_index=True,indicator=True)
```
This gives `AttributeError: Can only use .cat accessor with a 'category' dtype`. If there are only two levels, the error happens in a different place: `AttributeError: 'numpy.ndarray' object has no attribute 'start'`
#### Expected Output
I think that this should put "_merge" in the first level and null in the other levels, but I don't know. Maybe it's ok to not have this functionality but just give a better error message (merge indicator not supported with multiindex columns).
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.1
nose: 1.3.7
pip: 8.1.0
setuptools: 20.2.2
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: 2.4.4
matplotlib: 1.5.1
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: None
lxml: 3.4.4
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: 2.8
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} | 0 | 2016-07-13T21:13:35Z | 2016-07-15T10:21:32Z | 2016-07-15T10:21:32Z | CONTRIBUTOR | null | This just started failing on windows on master, in the last few PR's. Prob related to #13147.
tuples are not being upcast like lists.
```
======================================================================
FAIL: test_alignment_non_pandas (pandas.tests.frame.test_operators.TestDataFrameOperators)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\conda\Documents\pandas3.5\pandas\tests\frame\test_operators.py", line 1203, in test_alignment_non_pandas
Series([1, 2, 3], index=df.index))
File "C:\Users\conda\Documents\pandas3.5\pandas\util\testing.py", line 1157, in assert_series_equal
assert_attr_equal('dtype', left, right)
File "C:\Users\conda\Documents\pandas3.5\pandas\util\testing.py", line 882, in assert_attr_equal
left_attr, right_attr)
File "C:\Users\conda\Documents\pandas3.5\pandas\util\testing.py", line 1021, in raise_assert_detail
raise AssertionError(msg)
AssertionError: Attributes are different
Attribute "dtype" are different
[left]: int32
[right]: int64None
```
```
In [3]: import pandas as pd
In [4]: pd.__version__
Out[4]: '0.18.1+193.g20de266.dirty'
In [5]: pd.Series((1,2,3))
Out[5]:
0 1
1 2
2 3
dtype: int32
In [6]: pd.Series([1,2,3])
Out[6]:
0 1
1 2
2 3
dtype: int64
```
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} | 2 | 2016-07-13T21:24:04Z | 2016-07-14T07:13:01Z | 2016-07-14T07:12:52Z | MEMBER | null | xref #13639
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| null | 7 | 2016-07-13T22:13:25Z | 2017-03-27T12:00:04Z | 2017-03-26T14:40:10Z | CONTRIBUTOR | null | Born from a gitter conversation with @jorisvandenbossche and @jreback.
As of https://github.com/pydata/pandas/issues/10748, it's deprecated to have `np.NaN` as a category label. The deprecation warning also fires if `None` is a category, but it's unclear whether this is intended behavior, and the error message explicitly refers to NaN, which is confusing.
#### Code Sample, a copy-pastable example if possible
```
pd.Categorical.from_codes([0, 1, 2], [None, 'a', 'b'])
Setting NaNs in `categories` is deprecated and will be removed in a future version of pandas.
```
#### Expected Output
I wouldn't have expected a warning about `NaN` on an array that doesn't contain `NaN`.
#### output of `pd.show_versions()`
<details>
In [6]: pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-16-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: 24.0.2
Cython: 0.22.1
numpy: 1.11.1
scipy: 0.15.1
statsmodels: 0.6.1
xarray: None
IPython: 3.2.1
sphinx: 1.3.4
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2016.4
blosc: 1.2.8
bottleneck: 1.0.0
tables: None
numexpr: 2.4.6
matplotlib: 1.4.3
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.8
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.7.3
boto: 2.38.0
pandas_datareader: 0.2.1
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} | 6 | 2016-07-14T01:30:40Z | 2021-05-01T22:41:53Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
``` python
from io import StringIO
from pandas import read_csv
dtf = read_csv(StringIO("DATE_TIME,A\n2/8/2015 6:00:30,1"))
print(dtf)
dtf.set_index(dtf.DATE_TIME, drop=True, inplace=True)
print(dtf.columns)
print(dtf)
```
#### Current output
``` python
DATE_TIME A
0 2/8/2015 6:00:30 1
Index(['DATE_TIME', 'A'], dtype='object')
DATE_TIME A
DATE_TIME
2/8/2015 6:00:30 2/8/2015 6:00:30 1
```
#### Expected Output
``` python
DATE_TIME A
0 2/8/2015 6:00:30 1
Index(['A'], dtype='object')
A
DATE_TIME
2/8/2015 6:00:30 1
```
#### 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: None
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 20.6.7
Cython: None
numpy: 1.11.1
scipy: 0.16.1
statsmodels: None
xarray: None
IPython: 4.0.1
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.0
openpyxl: 2.3.5
xlrd: 1.0.0
xlwt: 1.0.0
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.0
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
None
```
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} | 4 | 2016-07-14T08:17:48Z | 2016-07-14T20:12:35Z | 2016-07-14T10:00:06Z | CONTRIBUTOR | null | See title and example below. I believe this is due to the fact that combination of indices with different timezones first converts to object dtype, then rebases all timestamps to UTC for comparison and then constructs a DatetimeIndex from that. However, this doesn't seem to be applied for the individual levels in a MultiIndex. This is on latest stable 0.18.1.
``` python
In [3]: %cpaste
Pasting code; enter '--' alone on the line to stop or use Ctrl-D.
:tz1, tz2 = 'America/New_York', 'UTC'
:
:from1, to1 = [pd.Timestamp('20160101', tz=tz1), pd.Timestamp('20160102', tz=tz1)], [pd.Timestamp('20160102', tz=tz1), pd.Timestamp('20160103', tz=tz1)]
:
:from2, to2 = [pd.Timestamp('20160103', tz=tz2), pd.Timestamp('20160104', tz=tz2)], [pd.Timestamp('20160104', tz=tz2), pd.Timestamp('20160105', tz=tz2)]
:
:index1 = pd.MultiIndex.from_arrays([from1, to1])
:df1 = pd.DataFrame([1, 2], index=index1)
:
:index2 = pd.MultiIndex.from_arrays([from2, to2])
:df2 = pd.DataFrame([1, 2], index=index2)
:
:result = df1.combine_first(df2)
:--
In [4]: df1.index.get_level_values(0)
Out[4]: DatetimeIndex(['2016-01-01 00:00:00-05:00', '2016-01-02 00:00:00-05:00'], dtype='datetime64[ns, America/New_York]', freq=None)
In [5]: df2.index.get_level_values(0)
Out[5]: DatetimeIndex(['2016-01-03', '2016-01-04'], dtype='datetime64[ns, UTC]', freq=None)
In [6]: result.index.get_level_values(0)
Out[6]:
Index([2016-01-01 00:00:00-05:00, 2016-01-02 00:00:00-05:00,
2016-01-03 00:00:00+00:00, 2016-01-04 00:00:00+00:00],
dtype='object')
```
Works correctly if the inputs have the same timezone
``` python
In [12]: %cpaste
Pasting code; enter '--' alone on the line to stop or use Ctrl-D.
:tz1, tz2 = 'America/New_York', 'America/New_York'
:
:from1, to1 = [pd.Timestamp('20160101', tz=tz1), pd.Timestamp('20160102', tz=tz1)], [pd.Timestamp('20160102', tz=tz1), pd.Timestamp('20160103', tz=tz1)]
:
:from2, to2 = [pd.Timestamp('20160103', tz=tz2), pd.Timestamp('20160104', tz=tz2)], [pd.Timestamp('20160104', tz=tz2), pd.Timestamp('20160105', tz=tz2)]
:
:index1 = pd.MultiIndex.from_arrays([from1, to1])
:df1 = pd.DataFrame([1, 2], index=index1)
:
:index2 = pd.MultiIndex.from_arrays([from2, to2])
:df2 = pd.DataFrame([1, 2], index=index2)
:
:result = df1.combine_first(df2)
:
:--
In [13]: result.index.get_level_values(0)
Out[13]:
DatetimeIndex(['2016-01-01 00:00:00-05:00', '2016-01-02 00:00:00-05:00',
'2016-01-03 00:00:00-05:00', '2016-01-04 00:00:00-05:00'],
dtype='datetime64[ns, America/New_York]', freq=None)
```
Behavior is correct for single indices:
``` python
In [7]: %cpaste
Pasting code; enter '--' alone on the line to stop or use Ctrl-D.
:
:tz1, tz2 = 'America/New_York', 'UTC'
:
:index1 = [pd.Timestamp('20160101', tz=tz1), pd.Timestamp('20160102', tz=tz1)]
:index2 = [pd.Timestamp('20160103', tz=tz2), pd.Timestamp('20160104', tz=tz2)]
:
:df1 = pd.DataFrame([1, 2], index=index1)
:df2 = pd.DataFrame([1, 2], index=index2)
:
:result = df1.combine_first(df2)
:--
In [8]: df2.index
Out[8]: DatetimeIndex(['2016-01-03', '2016-01-04'], dtype='datetime64[ns, UTC]', freq=None)
In [9]: df1.index
Out[9]: DatetimeIndex(['2016-01-01 00:00:00-05:00', '2016-01-02 00:00:00-05:00'], dtype='datetime64[ns, America/New_York]', freq=None)
In [10]: result.index
Out[10]:
DatetimeIndex(['2016-01-01 05:00:00+00:00', '2016-01-02 05:00:00+00:00',
'2016-01-03 00:00:00+00:00', '2016-01-04 00:00:00+00:00'],
dtype='datetime64[ns, UTC]', freq=None)
```
#### output of `pd.show_versions()`
``` python
In [1]: import pandas as pd
In [2]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-88-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.6
pip: 8.1.1
setuptools: 20.3
Cython: 0.22
numpy: 1.9.2
scipy: 0.17.0
statsmodels: 0.6.1.post1
xarray: None
IPython: 3.1.0
sphinx: None
patsy: 0.2.1
dateutil: 2.4.2
pytz: 2015.4
blosc: None
bottleneck: 1.0.0
tables: None
numexpr: 2.4.3
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/13651 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13651/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13651/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13651/events | https://github.com/pandas-dev/pandas/issues/13651 | 165,517,361 | MDU6SXNzdWUxNjU1MTczNjE= | 13,651 | `df.to_hdf(...format='table')` writes in different format than I'd expect, having used pytables before. | {
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| null | 1 | 2016-07-14T09:27:30Z | 2016-07-14T10:15:59Z | 2016-07-14T10:10:55Z | NONE | null | I'm dissatisfied with the current way `DataFrame.to_hdf(...format='table')` works. Take this simple example:
``` python
import numpy as np
import pandas as pd
import tables
data = {
'foo': np.zeros(5, dtype=float),
'bar': np.ones(5, dtype=float),
'baz': 2 * np.ones(5, dtype=float),
}
df = pd.DataFrame.from_dict(data)
df.to_hdf('bla.h5', '/mu', mode='a', format='table',
complevel=5, complib='zlib', fletcher32=True)
```
Now if we inspect the file with pytables, there are some issues with this:
```
In [1]: h5 = tb.open_file('bla.h5')
In [2]: h5.root.mu
Out[2]:
/mu (Group) ''
children := ['table' (Table)]
In [3]: h5.root.mu.table
Out[3]:
/mu/table (Table(5,), shuffle, zlib(5)) ''
description := {
"index": Int64Col(shape=(), dflt=0, pos=0),
"values_block_0": Float64Col(shape=(3,), dflt=0.0, pos=1)}
byteorder := 'little'
chunkshape := (2048,)
autoindex := True
colindexes := {
"index": Index(6, medium, shuffle, zlib(1)).is_csi=False}
In [4]: h5.root.mu.table.dtype
Out[4]: dtype([('index', '<i8'), ('values_block_0', '<f8', (3,))])
```
I've been using pytables + numpy so far to write Dataframes / structured arrays to HDF5 Tables. Compared to that, the pandas way of writing tables has 2 issues:
- What I'd have expected from the above example is that it'd create a table (not a group) called `mu` sitting right at the root node. Instead, it uses the `mu` as _Groupname_, and names the table itself just `table`, which is not exactly informing.
- The Table datatype does not hold the full information. Since all the Dataframe columns I passed are float, it just creates a _single table column_ holding 3-tuples. But I'd like it to create 3 separate Float columns, with the column names reflecting `DataFrame.columns`.
#### Workaround
I've been circumventing this issue so far by using `Dataframe.to_records()` to convert it to a structured numpy array & then use pytables directly to write the table to disc. This works without issues.
I'll whip up a Pull Request later I suppose.
#### output of `pd.show_versions()`
```
[?1034h
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-573.12.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 21.0.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: 3.2.3.1
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: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
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} | 17 | 2016-07-14T10:59:43Z | 2016-07-20T21:54:09Z | 2016-07-20T21:54:09Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
``` python
import pandas
import codecs
import traceback
import sys
pandas.show_versions()
print("Pandas version: {}\n".format(pandas.__version__))
print("Python version: {}\n".format(sys.version_info))
def build_stream():
filename = "tests/data/sauron.SHIFT_JIS.csv"
handle = open(filename, "rb")
codec = codecs.lookup("utf-8")
utf8 = codecs.lookup('utf-8')
# stream must be binary UTF8
stream = codecs.StreamRecoder(
handle, utf8.encode, utf8.decode, codec.streamreader, codec.streamwriter)
return stream
def test_pandas(use_python):
stream = build_stream()
if use_python:
engine = 'python'
else:
engine = 'c'
df = pandas.read_csv(stream, engine=engine)
print("Showing stream error on read\n")
try:
stream = build_stream()
data = stream.read()
except Exception as exc:
traceback.print_exc(file=sys.stdout)
print("Showing stream error on read_csv (python engine)\n")
try:
stream = test_pandas(True)
except Exception as exc:
traceback.print_exc(file=sys.stdout)
print("Showing missing stream error on read_csv (python c)\n")
try:
stream = test_pandas(False)
except Exception as exc:
traceback.print_exc(file=sys.stdout)
...
```
#### Actual Output
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-55-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.1
nose: 1.3.7
Cython: 0.24.0a0
numpy: 1.9.2
scipy: 0.16.0
statsmodels: None
IPython: 4.1.2
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.4
bottleneck: None
tables: None
numexpr: 2.4.3
matplotlib: None
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Pandas version: 0.16.1
Python version: sys.version_info(major=3, minor=4, micro=3, releaselevel='final', serial=0)
Showing stream error on read
Traceback (most recent call last):
File "pandas_bug.py", line 34, in <module>
data = stream.read()
File "/home/brett/.virtualenvs/datasets-service/lib/python3.4/codecs.py", line 798, in read
data = self.reader.read(size)
File "/home/brett/.virtualenvs/datasets-service/lib/python3.4/codecs.py", line 497, in read
newchars, decodedbytes = self.decode(data, self.errors)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x83 in position 12: invalid start byte
Showing stream error on read_csv (python engine)
Traceback (most recent call last):
File "pandas_bug.py", line 41, in <module>
stream = test_pandas(True)
File "pandas_bug.py", line 28, in test_pandas
df = pandas.read_csv(stream, engine=engine)
File "/home/brett/.virtualenvs/datasets-service/lib/python3.4/site-packages/pandas/io/parsers.py", line 474, in parser_f
return _read(filepath_or_buffer, kwds)
File "/home/brett/.virtualenvs/datasets-service/lib/python3.4/site-packages/pandas/io/parsers.py", line 250, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/home/brett/.virtualenvs/datasets-service/lib/python3.4/site-packages/pandas/io/parsers.py", line 566, in __init__
self._make_engine(self.engine)
File "/home/brett/.virtualenvs/datasets-service/lib/python3.4/site-packages/pandas/io/parsers.py", line 711, in _make_engine
self._engine = klass(self.f, **self.options)
File "/home/brett/.virtualenvs/datasets-service/lib/python3.4/site-packages/pandas/io/parsers.py", line 1427, in __init__
self.columns, self.num_original_columns = self._infer_columns()
File "/home/brett/.virtualenvs/datasets-service/lib/python3.4/site-packages/pandas/io/parsers.py", line 1642, in _infer_columns
line = self._buffered_line()
File "/home/brett/.virtualenvs/datasets-service/lib/python3.4/site-packages/pandas/io/parsers.py", line 1769, in _buffered_line
return self._next_line()
File "/home/brett/.virtualenvs/datasets-service/lib/python3.4/site-packages/pandas/io/parsers.py", line 1800, in _next_line
orig_line = next(self.data)
File "/home/brett/.virtualenvs/datasets-service/lib/python3.4/codecs.py", line 820, in __next__
data = next(self.reader)
File "/home/brett/.virtualenvs/datasets-service/lib/python3.4/codecs.py", line 638, in __next__
line = self.readline()
File "/home/brett/.virtualenvs/datasets-service/lib/python3.4/codecs.py", line 551, in readline
data = self.read(readsize, firstline=True)
File "/home/brett/.virtualenvs/datasets-service/lib/python3.4/codecs.py", line 497, in read
newchars, decodedbytes = self.decode(data, self.errors)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x83 in position 0: invalid start byte
Showing missing stream error on read_csv (python c)
Traceback (most recent call last):
File "pandas_bug.py", line 48, in <module>
stream = test_pandas(False)
File "pandas_bug.py", line 28, in test_pandas
df = pandas.read_csv(stream, engine=engine)
File "/home/brett/.virtualenvs/datasets-service/lib/python3.4/site-packages/pandas/io/parsers.py", line 474, in parser_f
return _read(filepath_or_buffer, kwds)
File "/home/brett/.virtualenvs/datasets-service/lib/python3.4/site-packages/pandas/io/parsers.py", line 250, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/home/brett/.virtualenvs/datasets-service/lib/python3.4/site-packages/pandas/io/parsers.py", line 566, in __init__
self._make_engine(self.engine)
File "/home/brett/.virtualenvs/datasets-service/lib/python3.4/site-packages/pandas/io/parsers.py", line 705, in _make_engine
self._engine = CParserWrapper(self.f, **self.options)
File "/home/brett/.virtualenvs/datasets-service/lib/python3.4/site-packages/pandas/io/parsers.py", line 1072, in __init__
self._reader = _parser.TextReader(src, **kwds)
File "pandas/parser.pyx", line 509, in pandas.parser.TextReader.__cinit__ (pandas/parser.c:4732)
File "pandas/parser.pyx", line 635, in pandas.parser.TextReader._get_header (pandas/parser.c:6244)
File "pandas/parser.pyx", line 831, in pandas.parser.TextReader._tokenize_rows (pandas/parser.c:8275)
File "pandas/parser.pyx", line 1742, in pandas.parser.raise_parser_error (pandas/parser.c:20691)
pandas.parser.CParserError: Error tokenizing data. C error: Calling read(nbytes) on source failed. Try engine='python'.
```
#### Expected Behavior
The C engine should behave like python engine. This should be possible by using PyErr_Occurred .
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} | 4 | 2016-07-14T13:16:54Z | 2020-09-30T02:04:41Z | 2020-09-30T02:04:41Z | NONE | null | When running `to_string` on a Series with empty strings at the beginning and end of the Series data, the empty strings are removed from the resulting output. These empty strings should be included to represent the empty data.
#### Code Sample:
``` python
import pandas as pd
pd.Series(["", "Hello", "World", "", "", "Mooooo", "", ""]).to_string(index=False)
```
#### Expected Output:
``` python
u'\nHello\nWorld\n \n \nMooooo\n\n'
```
#### Actual Output:
``` python
u'Hello\nWorld\n \n \nMooooo'
```
#### `pd.show_versions()`:
```
>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Linux
OS-release: 3.0.101-0.47.67-default
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.6
pip: 1.5.6
setuptools: 3.6
Cython: 0.24
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: 0.7.2
IPython: 4.2.0
sphinx: 1.2.2
patsy: 0.3.0
dateutil: 2.3
pytz: 2015.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4
matplotlib: 1.5.1
openpyxl: 2.0.4
xlrd: 0.9.0
xlwt: 0.7.4
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: 1.0b8
httplib2: None
apiclient: None
sqlalchemy: 0.9.7
pymysql: 0.6.2.None
psycopg2: None
jinja2: 2.8
boto: 2.34.0
pandas_datareader: None
```
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"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/45"
} | 2 | 2016-07-14T13:22:27Z | 2016-11-28T09:48:59Z | 2016-11-28T09:48:59Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd
f = pd.read_sas('test.sas7bdat', iterator=True)
for chunk in f:
print(f)
---------------------------------------------------------------------------
AbstractMethodError Traceback (most recent call last)
<ipython-input-3-bedf2769bffe> in <module>()
----> 1 for chunk in f:
2 print(chunk)
C:\Anaconda3\lib\site-packages\pandas\io\common.py in __next__(self)
99
100 def __next__(self):
--> 101 raise AbstractMethodError(self)
102
103 if not compat.PY3:
AbstractMethodError: This method must be defined in the concrete class of SAS7BDATReader
```
#### Expected Output
Print of each chunk
I'm no expert, but it seems as if the SAS7BDAT class inherits from BaseIterator but does not provide its own `__next__` method
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
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: 0.4.1
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
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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} | 2 | 2016-07-14T16:02:18Z | 2019-11-13T01:27:53Z | 2019-11-13T01:27:53Z | MEMBER | null | When doing `.loc(axis=1)[...]`, this only works when indexing into multiple levels. Here an example with a MultiIndex column:
```
In [23]: df = pd.DataFrame(np.arange(27).reshape(3, 9), columns=pd.MultiIndex.from_product([['a1', 'a2', 'a3'], ['b1', 'b2', 'b3']]))
In [24]: df
Out[24]:
a1 a2 a3
b1 b2 b3 b1 b2 b3 b1 b2 b3
0 0 1 2 3 4 5 6 7 8
1 9 10 11 12 13 14 15 16 17
2 18 19 20 21 22 23 24 25 26
In [25]: df.loc(axis=1)['a1':'a2', ['b1', 'b2']]
Out[25]:
a1 a2
b1 b2 b1 b2
0 0 1 3 4
1 9 10 12 13
2 18 19 21 22
```
Given the above, I would expect leaving out the `['b1', 'b2']` to work as well, but in this case it looks like the `axis=1` is ignored, which is a bit surprising IMO:
```
In [26]: df.loc(axis=1)['a1':'a2']
Out[26]:
Empty DataFrame
Columns: [(a1, b1), (a1, b2), (a1, b3), (a2, b1), (a2, b2), (a2, b3), (a3, b1),
(a3, b2), (a3, b3)]
Index: []
In [27]: df.loc(axis=1)['a1']
...
KeyError: 'the label [a1] is not in the [index]'
```
Also when having a simple frame, the same observation applies:
```
In [28]: df2 = pd.DataFrame(np.arange(9).reshape(3,3), columns=['a', 'b', 'c'])
In [29]: df2
Out[29]:
a b c
0 0 1 2
1 3 4 5
2 6 7 8
In [30]: df2.loc(axis=1)['a']
...
KeyError: 'the label [a] is not in the [index]'
```
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| null | 4 | 2016-07-14T16:15:04Z | 2016-11-16T22:21:26Z | 2016-11-16T22:21:25Z | NONE | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
I ran into this error with my code and the error message didn't help me figure out I was dealing with duplicate columns that couldn't be handled. I think this is more helpful
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} | 3 | 2016-07-14T19:33:00Z | 2021-08-19T21:41:49Z | 2021-08-19T21:41:49Z | NONE | null | #### Code Sample, a copy-pastable example if possible
``` py
# add color gradient to column(s)
import numpy as np
import math
from pandas import *
from IPython.display import display, HTML
# color gradient
COLORS = ["#F1F2B5", "#CCD7A5", "#A7BC96", "#82A186", "#5D8677", "#386B67", "#135058"]
# rows/columns to skip applying gradient to
SKIPROWSORCOLUMNS = ["Day","Users"]
# index in row or columns to skip
SKIPINDICES = [0,1]
# dataframe to operate on
df = DataFrame([["Monday",100,.65,.39,.33],["Tuesday",120,.45,.32,.31],["Wednesday",150,.75,.33,.30]])
df.columns = ["Day","Users","M1","M2","M3"] #column headers
# takes dataframe and colors
# returns colormap
def applyColorGradient(data, colors=COLORS, skiprowsorcolumns=SKIPROWSORCOLUMNS, skipindices=SKIPINDICES):
numberOfColors = len(colors)
i = 0
maxNumberUnset = True
maxNumber = 0
while (i < len(data)):
if (i not in skipindices):
if(maxNumberUnset):
maxNumber = data[i]
maxNumberUnset = False
else:
current = data[i]
if(current > maxNumber):
maxNumber = current
i = i + 1
name = data.name
dontskip = data.name not in skiprowsorcolumns
colorArray = [colors[int(math.floor(value/maxNumber*(numberOfColors-1)))] if dontskip and index not in skipindices else '' for index,value in enumerate(data)]
print(colorArray)
colorSeries = pandas.Series(["background-color:" + color if color else '' for color in colorArray], name=data.name)
display(colorSeries)
return colorSeries
# applying colorgradient to dataframe
gradientdf = df.style.apply(applyColorGradient, axis=1) #0 is used for columns
# display table
display(gradientdf) #0 is used for columns
```
#### Expected Output
With axis = 0, this manages to give each cell a background color that is indicated. When I change this to a row based function, this doesn't color any of the cells, even though the color series are coming in. Is this a bug or am I doing something wrong?
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-91-generic
machine: x86_64
processor:
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.17.1
nose: None
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.23.5
numpy: 1.10.4
scipy: 0.17.1
statsmodels: 0.6.1
IPython: 4.2.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: None
numexpr: 2.5.2
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: None
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} | 7 | 2016-07-14T20:15:38Z | 2020-06-29T17:37:11Z | 2020-03-14T20:31:22Z | CONTRIBUTOR | null | ```
Assignment to multiple columns of a :class:`DataFrame` when some of the columns do not exist would previously assign the values to the last column. Now, new columns would be constructed with the right values.
.. ipython:: python
df = pd.DataFrame({'a': [0, 1, 2], 'b': [3, 4, 5]})
df
*Previous behavior*:
.. code-block:: ipython
In [3]: df[['a', 'c']] = 1
In [4]: df
Out[4]:
a b
0 1 1
1 1 1
2 1 1
*New behavior*:
.. ipython:: python
df[['a', 'c']] = 1
df
```
---
```
import pandas as pd
df = pd.DataFrame({'a': [1, 2]})
df['b'] = 3 # creates column 'b'
df[['a', 'b']] = 4 # assigns to columns 'a' and 'b'
df[['c', 'd']] = 5 # KeyError: "['c' 'd'] not in index" !
df[['b', 'c']] = 6 # KeyError: "['c'] not in index" !
```
I would have expected all the above cases to work, but for some reason the last two fail. New column creation only seems to work for a single column, whilst multiple-column assignment works only if all columns exist.
> INSTALLED VERSIONS
> python: 3.5.1.final.0
> python-bits: 64
> pandas: 0.18.1
> numpy: 1.11.1
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| null | 4 | 2016-07-14T21:43:42Z | 2021-05-01T22:44:15Z | null | NONE | null | Here is a sample code to reproduce the error:
```
In [1]: df1 = pd.DataFrame(np.random.rand(5,2))
df2 = pd.DataFrame(np.random.rand(5,2))
In [2]: df2.join(df1)
Out[2]: ---------------------------------------------------------------------------
ValueError: columns overlap but no suffix specified: RangeIndex(start=0, stop=2, step=1)
In [3]: df2.join(df1, lsuffix='_x', rsuffix='_x')
Out[2]: 0_x 1_x 0_x 1_x
0 0.904888 0.491802 0.509346 0.367847
1 0.282420 0.092652 0.672786 0.358450
2 0.339018 0.318990 0.359977 0.640366
3 0.775293 0.767872 0.820965 0.018728
4 0.543648 0.412799 0.650457 0.712789
```
So ultimately one does get a merged DataFrame with overlapping column names. Then why raise an error in the first place?
Note, I am using the latest Pandas, Python and Numpy.
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closes #7795
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} | 10 | 2016-07-15T01:39:21Z | 2017-04-05T02:07:03Z | 2016-08-22T20:35:12Z | CONTRIBUTOR | null | - [x] closes #13333
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Just a WIP for now, I want to cleanup how I handle the errorbar changes.
Will wait for a matplotlib RC before changing the Travis matrix I suppose?
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} | 2 | 2016-07-15T11:52:07Z | 2016-07-24T13:51:12Z | 2016-07-24T13:51:12Z | NONE | null | I expected that for each value a the number of entries would be returned, instead it throws an OutOfBoundsDatetime although it is not pandas object in first place, but a datetime object. I am aware that the pandas timestamps have a limited time span, however the objects are not timestamps, but datetimes... Thanks!
#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
import datetime
s = pd.Series([datetime.datetime(9999,1,1)])
s.value_counts()
```
#### Expected Output
```
---------------------------------------------------------------------------
OutOfBoundsDatetime Traceback (most recent call last)
<ipython-input-7-1b9293e6eb60> in <module>()
----> 1 s.value_counts()
/usr/lib/python2.7/dist-packages/pandas/core/base.pyc in value_counts(self, normalize, sort, ascending, bins, dropna)
466 from pandas.tseries.api import DatetimeIndex, PeriodIndex
467 result = value_counts(self, sort=sort, ascending=ascending,
--> 468 normalize=normalize, bins=bins, dropna=dropna)
469
470 if isinstance(self, PeriodIndex):
/usr/lib/python2.7/dist-packages/pandas/core/algorithms.pyc in value_counts(values, sort, ascending, normalize, bins, dropna)
318
319 if not isinstance(keys, Index):
--> 320 keys = Index(keys)
321 result = Series(counts, index=keys, name=name)
322
/usr/lib/python2.7/dist-packages/pandas/core/index.pyc in __new__(cls, data, dtype, copy, name, fastpath, tupleize_cols, **kwargs)
177 tslib.is_timestamp_array(subarr)):
178 from pandas.tseries.index import DatetimeIndex
--> 179 return DatetimeIndex(subarr, copy=copy, name=name, **kwargs)
180 elif (inferred.startswith('timedelta') or
181 lib.is_timedelta_array(subarr)):
/usr/lib/python2.7/dist-packages/pandas/util/decorators.pyc in wrapper(*args, **kwargs)
87 else:
88 kwargs[new_arg_name] = new_arg_value
---> 89 return func(*args, **kwargs)
90 return wrapper
91 return _deprecate_kwarg
/usr/lib/python2.7/dist-packages/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, closed, ambiguous, dtype, **kwargs)
317 except ValueError:
318 # tz aware
--> 319 subarr = tools._to_datetime(data, box=False, utc=True)
320
321 # we may not have been able to convert
/usr/lib/python2.7/dist-packages/pandas/tseries/tools.pyc in _to_datetime(arg, errors, dayfirst, yearfirst, utc, box, format, exact, unit, freq, infer_datetime_format)
393 return _convert_listlike(arg, box, format, name=arg.name)
394 elif com.is_list_like(arg):
--> 395 return _convert_listlike(arg, box, format)
396
397 return _convert_listlike(np.array([ arg ]), box, format)[0]
/usr/lib/python2.7/dist-packages/pandas/tseries/tools.pyc in _convert_listlike(arg, box, format, name)
381 return DatetimeIndex._simple_new(values, name=name, tz=tz)
382 except (ValueError, TypeError):
--> 383 raise e
384
385 if arg is None:
OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 9999-01-01 00:00:00
```
#### 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: de_DE.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.7.0
Cython: None
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
IPython: 2.4.1
sphinx: None
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2014.10
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.3
matplotlib: 1.5.1
openpyxl: 2.3.0
xlrd: 0.9.4
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.5.0
bs4: 4.4.1
html5lib: 0.999
httplib2: 0.9.1
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
Jinja2: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/13664 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13664/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13664/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13664/events | https://github.com/pandas-dev/pandas/pull/13664 | 165,784,680 | MDExOlB1bGxSZXF1ZXN0Nzc2MTQwMDI= | 13,664 | API: Index doesn't results in PeriodIndex if Period contains NaT | {
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} | 0 | 2016-07-15T13:17:55Z | 2016-07-19T02:07:54Z | 2016-07-19T02:07:54Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
This improve `Period` handling a little.
- normal `Index` constructor can accept:
- `Period` and `NaT` mixed input (result is `PeriodIndex`) `Period` version of #13477)
- `Period` with multiple freq (result is `object` dtype)
- `PeriodIndex.fillna` can accept `Period` with different freq (result is `object` dtype)
The below describes the behavior on current master
```
# this should be PeriodIndex
pd.Index([pd.Period('2011-01', freq='M'), pd.NaT])
# Index([2011-01, NaT], dtype='object')
# this should be object dtype
pd.Index([pd.Period('2011-01', freq='M'), pd.Period('2011-01-01', freq='D')])
# pandas._period.IncompatibleFrequency: Input has different freq=D from PeriodIndex(freq=M)
# this should be object dtype
pd.PeriodIndex(['2011-01', 'NaT'], freq='M').fillna(pd.Period('2011-01-01', freq='D'))
# pandas._period.IncompatibleFrequency: Input has different freq=D from PeriodIndex(freq=M)
```
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| null | 7 | 2016-07-15T14:43:01Z | 2019-09-18T14:02:16Z | 2019-09-18T14:02:16Z | NONE | null | This works fine:
```
df = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'))
df2 = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'))
df.merge(df2, how="left", on="A")
```
This fails with "TypeError: type object argument after \* must be a sequence, not map"
```
df = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD')).to_sparse()
df2 = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD')).to_sparse()
df.merge(df2, how="left", on="A")
```
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| null | 6 | 2016-07-15T17:59:52Z | 2021-05-01T22:45:22Z | null | CONTRIBUTOR | null | In pandas 0.17.1, grouping a DatetimeIndex with monthly frequency produced labels keyed at the start of the month, and `nth()` produces a label keyed by the selected row. In 0.18, we unconditionally return groups labelled by the end of the month.
#### Old Pandas
```
In [25]: pd.__version__
Out[25]: u'0.17.1'
In [26]: dates = pd.date_range('2014', periods=90)
In [27]: pd.Series(index=dates).groupby(pd.TimeGrouper(freq='M')).nth(5)
Out[27]:
2014-01-06 NaN
2014-02-06 NaN
2014-03-06 NaN
dtype: float64
```
#### Latest Pandas
```
In [71]: pd.__version__
Out[71]: u'0.18.1'
In [72]: dates = pd.date_range('2014', periods=90)
In [73]: pd.Series(index=dates).groupby(pd.TimeGrouper(freq='M')).nth(5)
Out[73]:
2014-01-31 NaN
2014-02-28 NaN
2014-03-31 NaN
dtype: float64
```
#### Expected Output
I expect the behavior from 0.17.1.
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} | 2 | 2016-07-15T23:28:53Z | 2016-07-16T09:30:33Z | 2016-07-16T09:29:35Z | NONE | null | I am not sure, but it appears that obtaining options data from yahoo is broken as it results into following error at the end.
#### Code Sample, a copy-pastable example if possible
from pandas.io.data import Options
aapl = Options('aapl', 'yahoo')
data = aapl.get_all_data()
#### Expected Output
AAPL options data for near-term expiry dates
#### output of `pd.show_versions()`
pandas: 0.18.1+204.gd7c028d
pandas_datareader: 0.2.1
#### output of self._get_expiry_dates()
pandas/io/data.py in _get_expiry_dates_and_links(self)
1191
1192 if len(expiry_dates) == 0:
1193 raise RemoteDataError('Data not available')
1194
1195 expiry_links = dict(zip(expiry_dates, links))
RemoteDataError: Data not available
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} | 8 | 2016-07-16T03:01:13Z | 2016-07-24T19:25:26Z | 2016-07-24T19:24:00Z | MEMBER | null | Per discussion with @jorisvandenbossche <a href="https://github.com/pydata/pandas/pull/13611#discussion_r71048508">here</a>.
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} | 2 | 2016-07-16T04:58:34Z | 2016-07-20T21:28:14Z | 2016-07-20T21:28:11Z | MEMBER | null | Deprecated all the way back in `0.15.0` <a href="https://github.com/pydata/pandas/pull/8227">here</a>.
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} | 3 | 2016-07-16T06:56:51Z | 2016-07-19T01:20:26Z | 2016-07-19T01:20:18Z | MEMBER | null | Deprecated back in `0.16.0` <a href="https://github.com/pydata/pandas/pull/9615">here</a>, and the module seems to be rarely, if at all, used according to @jorisvandenbossche .
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} | 5 | 2016-07-16T07:41:48Z | 2016-07-19T02:13:44Z | 2016-07-19T01:44:45Z | MEMBER | null | Deprecated back in `0.16.0` <a href="https://github.com/pydata/pandas/pull/9611">here</a>.
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} | 1 | 2016-07-16T07:43:04Z | 2016-07-23T13:09:18Z | 2016-07-23T13:09:18Z | NONE | null | I'm not sure if this is a bug or intended behavior, but documentation says "If periods is none, generated index will extend to first conforming time on or just past end argument", and it appears here that in the nanosecond frequency case, the generated index only extends to just _before_ the end argument.
#### Code Sample, a copy-pastable example if possible
``` python
> start = pd.Timestamp(1)
> end = pd.Timestamp(4)
> pd.DatetimeIndex(start=start, end=end, freq='N')
DatetimeIndex(['1970-01-01 00:00:00.000000001',
'1970-01-01 00:00:00.000000002',
'1970-01-01 00:00:00.000000003'],
dtype='datetime64[ns]', freq='N')
```
whereas a similar call with annual frequency gives:
``` python
> start = pd.Timestamp('1971')
> end = pd.Timestamp('1974')
> pd.DatetimeIndex(start=start, end=end, freq='AS')
DatetimeIndex(['1971-01-01',
'1972-01-01',
'1973-01-01',
'1974-01-01'],
dtype='datetime64[ns]', freq='AS-JAN')
```
#### output of `pd.show_versions()`
pandas: 0.18.0
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} | 4 | 2016-07-16T16:18:31Z | 2016-07-20T22:16:32Z | 2016-07-20T22:16:28Z | CONTRIBUTOR | null | - [x] passes `git diff upstream/master | flake8 --diff`
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} | 1 | 2016-07-16T17:22:42Z | 2016-07-16T18:48:14Z | 2016-07-16T18:43:07Z | NONE | null | I have a very long csv file with some bad lines. Here is a sample of a bad line between some good lines:
2016.07.03 09:43:38\t0.01055556\t16.146\t26.9444444444444\n
2016.07.03 09:43:43\t0.01194444\t16.146\t26.9883333333333\n
2016.07.03 09:43:48\t0.01333333\t16.105\t27.0383333333333\n
2016.07.03 09:43:53\t0.01472222\t16.102\t42731,00\n
2016.07.03 09:43:58\t0.01611111\t16.138\t27.1822222222222\n
So basically the sensor has made a mistake when writing the 4th line, and written 42731,00 instead of an actual number. I want to just skip lines like that, so I read this file with the following statement:
a = pd.read_csv(StringIO(bdy), sep = '\t',
skiprows = 2,
header = None, error_bad_lines = False, warn_bad_lines = True,
parse_dates = [0], decimal = '.')
This results in the 4th column in the resulting df to be of type object (a string) instead of float64.
If i try to force the reader to parse by doing:
pd.read_csv(StringIO(bdy), sep = '\t',
skiprows = 2,
header = None, error_bad_lines = False, warn_bad_lines = True,
parse_dates = [0], decimal = '.', dtype={1:np.float64,2:np.float64,3:np.float64})
i get: ValueError: could not convert string to float...but it is not being ignored even though i have set error_bad_lines = False. So how can i actually parse this correctly - preferrebly without having to supply dtype? Is this a bug?
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} | 4 | 2016-07-16T17:57:53Z | 2016-07-19T13:15:26Z | 2016-07-19T13:15:22Z | CONTRIBUTOR | null | - [ ] closes #13618, closes #13520
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
Prevents warnings triggered by IPython by matching against `'_repr'` or `'_ipython'`, and expands the `resample` warning message to indicate what caused the deferred object to be materialized.
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removed unnecessary trailing `None`.
```
pd.util.testing.assert_index_equal(pd.Index([1, 2, 3]), pd.Index([1 ,2, 4]))
# AssertionError: Index are different
#
# Index values are different (33.33333 %)
# [left]: Int64Index([1, 2, 3], dtype='int64')
# [right]: Int64Index([1, 2, 4], dtype='int64')None
```
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not sure if there is any reason we haven't supported...
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} | 0 | 2016-07-16T20:57:58Z | 2016-07-17T18:47:18Z | 2016-07-16T20:58:28Z | NONE | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
Merge from pydata master
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} | 4 | 2016-07-16T22:54:06Z | 2017-03-04T11:11:00Z | 2016-08-10T12:25:06Z | MEMBER | null | - [x] closes #9161, closes #13544
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Do not use area legend workaround if mpl >= 1.5.0 is available. Legends are now displayed properly if mpl >= 1.5.0.
#9161:
```
df = pd.DataFrame(np.random.rand(10, 3))
df.plot(kind='area', subplots=True, sharex=True, legend=True)
```
#13544:
```
df = pd.DataFrame(np.random.rand(20, 5), columns=['A', 'B', 'C', 'D', 'E'])
df.plot(kind='area', linewidth=0.1).legend(loc="center left", bbox_to_anchor=(1.02, 0.5))
```
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} | 1 | 2016-07-16T23:03:38Z | 2017-09-23T21:08:40Z | 2017-09-23T21:08:40Z | MEMBER | null | For these optional deps, pandas doesn't specify the supported versions. Therefore, pandas needs some codes to support older ones.
- scipy
- matplotlib
- openpyxl
I don't think we need strict support version policy for these packages, but how about once decide "recommended versions", and show warnings if user env doesn't meet it (can remove legacy codes in future maybe).
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} | 0 | 2016-07-17T07:34:34Z | 2016-07-17T18:30:52Z | 2016-07-17T18:30:19Z | CONTRIBUTOR | null | For the .cum\* functions the generated documentation description is redundant. As an example, [DataFrame.cumsum()](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.cumsum.html?highlight=cumsum#pandas.DataFrame.cumsum) has the description "Return cumulative cumsum over requested axis." when it should say "Return cumulative sum over requested axis.".
After looking into this, I found the problem on line 5507 of generic.py where the _name_ is being used instead of the _desc_.
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} | 2 | 2016-07-17T08:21:38Z | 2016-07-17T18:30:44Z | 2016-07-17T18:30:19Z | CONTRIBUTOR | null | closes #13682
I changed the template function to use the _desc_ instead of the _name_ in generic.py.
SIDENOTE: I accidentally committed to a branch with another PR, so I rebased without it and created a new branch with this commit.
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} | 14 | 2016-07-17T09:25:59Z | 2016-09-01T07:28:54Z | 2016-09-01T07:28:54Z | CONTRIBUTOR | null | Maybe one of the imports in `show_versions` has unwanted side effects?
``` python
>>> import pandas as pd
>>> pd.DataFrame({'spam': range(10)})
spam
0 0
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 24.0.3
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.0rc2
statsmodels: None
xarray: None
IPython: 5.0.0
sphinx: 1.4.5
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: None
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.3.5
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: 3.6.0
bs4: None
html5lib: 0.999999999
httplib2: 0.9.2
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
>>> pd.DataFrame({'spam': range(10)})
Traceback (most recent call last):
File "<pyshell#3>", line 1, in <module>
pd.DataFrame({'spam': range(10)})
File "C:\Program Files\Python27\lib\site-packages\pandas\core\base.py", line 67, in __repr__
return str(self)
File "C:\Program Files\Python27\lib\site-packages\pandas\core\base.py", line 47, in __str__
return self.__bytes__()
File "C:\Program Files\Python27\lib\site-packages\pandas\core\base.py", line 59, in __bytes__
return self.__unicode__().encode(encoding, 'replace')
File "C:\Program Files\Python27\lib\site-packages\pandas\core\frame.py", line 535, in __unicode__
line_width=width, show_dimensions=show_dimensions)
File "C:\Program Files\Python27\lib\site-packages\pandas\core\frame.py", line 1488, in to_string
formatter.to_string()
File "C:\Program Files\Python27\lib\site-packages\pandas\formats\format.py", line 549, in to_string
strcols = self._to_str_columns()
File "C:\Program Files\Python27\lib\site-packages\pandas\formats\format.py", line 467, in _to_str_columns
str_index = self._get_formatted_index(frame)
File "C:\Program Files\Python27\lib\site-packages\pandas\formats\format.py", line 746, in _get_formatted_index
fmt_index = [index.format(name=show_index_names, formatter=fmt)]
File "C:\Program Files\Python27\lib\site-packages\pandas\indexes\base.py", line 1462, in format
return self._format_with_header(header, **kwargs)
File "C:\Program Files\Python27\lib\site-packages\pandas\indexes\base.py", line 1486, in _format_with_header
result = _trim_front(format_array(values, None, justify='left'))
File "C:\Program Files\Python27\lib\site-packages\pandas\formats\format.py", line 2007, in format_array
return fmt_obj.get_result()
File "C:\Program Files\Python27\lib\site-packages\pandas\formats\format.py", line 2027, in get_result
return _make_fixed_width(fmt_values, self.justify)
File "C:\Program Files\Python27\lib\site-packages\pandas\formats\format.py", line 2394, in _make_fixed_width
max_len = np.max([adj.len(x) for x in strings])
File "C:\Program Files\Python27\lib\site-packages\numpy\core\fromnumeric.py", line 2293, in amax
out=out, **kwargs)
File "C:\Program Files\Python27\lib\site-packages\numpy\core\_methods.py", line 26, in _amax
return umr_maximum(a, axis, None, out, keepdims)
TypeError: an integer is required
```
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| null | 1 | 2016-07-17T12:56:09Z | 2016-07-17T13:19:27Z | 2016-07-17T13:19:27Z | NONE | null | Sample:
```
import pandas as pd
import numpy as np
df = pd.DataFrame({'A':[1,2],
'B':[3,4],
'C':[5,6]}, index=['a','b'])
print (df)
A B C
a 1 3 5
b 2 4 6
```
This works nice:
```
L = ['c','d']
df = df.set_index(np.array(L))
print (df)
A B C
c 1 3 5
d 2 4 6
df = df.set_index(pd.Index(L))
print (df)
A B C
c 1 3 5
d 2 4 6
```
But if check [docs](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.set_index.html) this can works too, but **KeyError**:
```
df = df.set_index(L)
KeyError: 'c'
```
It is also in [SO](http://stackoverflow.com/questions/38421170/cant-set-index-of-a-pandas-data-frame-getting-keyerror).
```
print (pd.show_versions())
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: sk_SK
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: None
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
None
```
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} | 2 | 2016-07-17T14:02:42Z | 2016-07-17T18:32:35Z | 2016-07-17T18:32:25Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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} | 7 | 2016-07-17T16:16:56Z | 2016-07-19T01:16:31Z | 2016-07-19T01:14:14Z | CONTRIBUTOR | null | - [x] closes #13603
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
pd.NaT, None, float('nan') and np.nan are
converted NaT in TestTimedeltas.
So we should convert keys if keys are these value.
Fix #13603
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} | 1 | 2016-07-17T18:09:45Z | 2016-07-23T16:25:35Z | 2016-07-23T16:25:20Z | CONTRIBUTOR | null | I've been using pandas for a while and am just now starting to contribute. One of the things that has bugged me for a while is that in the docs there is no scroll to top button. Some of the pages get pretty long, and I'm usually without a Home button on my laptop, so I end up _stuck_ (exaggeration) deep in the docs.
I looked into templating with Sphinx and discovered that it's pretty easy to add this feature to all pages. Under pandas/doc/_templates, I created a file layout.html (see code below) that adds a small bit of HTML, CSS, and JS which create a scroll to top button. I kept it to a basic implementation, but feel free if anyone has any suggestions about style or the breakpoint for viewing the button.
**pandas/doc/_templates/layout.html**
```
{% extends "!layout.html" %} {%- block footer %}
<style type="text/css">
.scrollToTop {
text-align: center;
font-weight: bold;
position: fixed;
bottom: 60px;
right: 40px;
display: none;
}
</style>
<a href="#" class="scrollToTop">Scroll To Top</a>
<script type="text/javascript">
$(document).ready(function() {
//Check to see if the window is top if not then display button
$(window).scroll(function() {
if ($(this).scrollTop() > 200) {
$('.scrollToTop').fadeIn();
} else {
$('.scrollToTop').fadeOut();
}
});
//Click event to scroll to top
$('.scrollToTop').click(function() {
$('html, body').animate({
scrollTop: 0
}, 500);
return false;
});
});
</script>
{% endblock %}
```
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} | 10 | 2016-07-17T19:06:17Z | 2016-07-23T16:26:00Z | 2016-07-23T16:25:20Z | CONTRIBUTOR | null | - [x] closes #13688
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
Added scroll to top feature to documentation div.footer using Sphinx template
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} | 4 | 2016-07-17T19:24:26Z | 2016-07-19T01:11:36Z | 2016-07-19T01:11:18Z | CONTRIBUTOR | null | - [x] closes #13598
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 2 | 2016-07-18T12:46:10Z | 2020-10-16T19:17:11Z | 2020-10-16T19:17:11Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```
In [2]: s = pd.Series(0, index=pd.MultiIndex.from_product([[0], [1,2]]))
In [3]: s.loc[[]]
Out[3]: Series([], dtype: int64)
In [4]: s = pd.Series(0, index=pd.MultiIndex.from_product([[0], [1,1]]))
In [5]: s.loc[[]]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-5-f9b6211189ca> in <module>()
----> 1 s.loc[[]]
/home/pietro/nobackup/repo/pandas/pandas/core/indexing.py in __getitem__(self, key)
1304 return self._getitem_tuple(key)
1305 else:
-> 1306 return self._getitem_axis(key, axis=0)
1307
1308 def _getitem_axis(self, key, axis=0):
/home/pietro/nobackup/repo/pandas/pandas/core/indexing.py in _getitem_axis(self, key, axis)
1464 raise ValueError('Cannot index with multidimensional key')
1465
-> 1466 return self._getitem_iterable(key, axis=axis)
1467
1468 # nested tuple slicing
/home/pietro/nobackup/repo/pandas/pandas/core/indexing.py in _getitem_iterable(self, key, axis)
1095
1096 new_target, indexer, new_indexer = labels._reindex_non_unique(
-> 1097 keyarr)
1098
1099 if new_indexer is not None:
/home/pietro/nobackup/repo/pandas/pandas/indexes/base.py in _reindex_non_unique(self, target)
2497 new_indexer[~check] = -1
2498
-> 2499 new_index = self._shallow_copy_with_infer(new_labels, freq=None)
2500 return new_index, indexer, new_indexer
2501
/home/pietro/nobackup/repo/pandas/pandas/indexes/multi.py in _shallow_copy_with_infer(self, values, **kwargs)
393
394 def _shallow_copy_with_infer(self, values=None, **kwargs):
--> 395 return self._shallow_copy(values, **kwargs)
396
397 @Appender(_index_shared_docs['_shallow_copy'])
/home/pietro/nobackup/repo/pandas/pandas/indexes/multi.py in _shallow_copy(self, values, **kwargs)
402 # discards freq
403 kwargs.pop('freq', None)
--> 404 return MultiIndex.from_tuples(values, **kwargs)
405 return self.view()
406
/home/pietro/nobackup/repo/pandas/pandas/indexes/multi.py in from_tuples(cls, tuples, sortorder, names)
889 if len(tuples) == 0:
890 # I think this is right? Not quite sure...
--> 891 raise TypeError('Cannot infer number of levels from empty list')
892
893 if isinstance(tuples, (np.ndarray, Index)):
TypeError: Cannot infer number of levels from empty list
```
#### Expected Output
`Out[5]: Series([], dtype: int64)
`
By the way, on top of the inconsistency above,
`In [6]: s.loc[s.iloc[0:0]]`
results in the same error, which is even more unexpected because `s.iloc[0:0].index` is a perfectly valid `MultiIndex`, and so it is certainly possible to infer the number of levels from it.
Might be related to #13490 (the error is the same, but I ignore the level at which the open PR applies).
#### output of `pd.show_versions()`
```
In [7]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.5.0-2-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
LOCALE: it_IT.UTF-8
pandas: 0.18.1+206.gc42455b
nose: 1.3.7
pip: 1.5.6
setuptools: 18.4
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.16.0
statsmodels: 0.8.0.dev0+111ddc0
xarray: None
IPython: 5.0.0.dev
sphinx: 1.3.1
patsy: 0.3.0-dev
dateutil: 2.2
pytz: 2012c
blosc: None
bottleneck: 1.1.0dev
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: 1.1.2
xlsxwriter: 0.7.3
lxml: None
bs4: 4.4.0
html5lib: 0.999
httplib2: 0.9.1
apiclient: 1.5.0
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: 0.2.1
```
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} | 4 | 2016-07-18T12:57:41Z | 2016-07-19T13:06:23Z | 2016-07-19T13:06:23Z | MEMBER | null | - [x] closes #11169, closes #11287
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
cleaned up `DatetimeIndex` constructor removing slower string-parsing path.
## Performance Improvement
related to #7599, internally use `to_datetime` always as it tries some fastpath.
```
inp = np.array(['2011-01-01 09:00' for i in range(10000)])
# on current master
%timeit pd.DatetimeIndex(inp)
1 loops, best of 3: 3.41 s per loop
%timeit pd.to_datetime(inp)
100 loops, best of 3: 4.77 ms per loop
# after the PR
%timeit pd.DatetimeIndex(inp)
#100 loops, best of 3: 4.23 ms per loop
%timeit pd.to_datetime(inp)
#100 loops, best of 3: 4.25 ms per loop
```
## Bug Fixes
The cleanup fixed these 2 kind of issues:
#### 1. #11169 and #11287 Invalid string parsing may raise `TypeError`
I met the same issue on travis and fixed with try-except clause (I can't reproduce it on my local Mac).
#### 2. Index may incorrectly coerces mismatched tz
on current master, `DatetimeIndex` and normal `Index` behaves differently.
```
# OK
pd.DatetimeIndex([pd.Timestamp('2011-01-01', tz='US/Eastern')], tz='US/Pacific')
# TypeError: Already tz-aware, use tz_convert to convert.
# NG, it ignores mismatch and coerce to passed tz
pd.Index([pd.Timestamp('2011-01-01', tz='US/Eastern')], tz='US/Pacific')
DatetimeIndex(['2010-12-31 21:00:00-08:00'], dtype='datetime64[ns, US/Pacific]', freq=None)
```
after the PR both behave the same, showing understandable error.
```
pd.Index([pd.Timestamp('2011-01-01', tz='US/Eastern')], tz='US/Pacific')
# TypeError: data is already tz-aware US/Eastern, unable to set specified tz: US/Pacific
pd.DatetimeIndex([pd.Timestamp('2011-01-01', tz='US/Eastern')], tz='US/Pacific')
# TypeError: data is already tz-aware US/Eastern, unable to set specified tz: US/Pacific
```
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} | 11 | 2016-07-18T14:43:01Z | 2016-07-20T21:54:19Z | 2016-07-20T21:54:09Z | CONTRIBUTOR | null | - [x] closes #13652
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 1 | 2016-07-18T15:29:17Z | 2016-07-21T16:18:59Z | 2016-07-18T20:40:24Z | NONE | null | #### Code Sample, a copy-pastable example if possible
to_excel function displays datetime types incorrectly in Excel
before: datetime64[ns] - 2016-09-28 (seconds removed)
after saving in excel file via - to_excel -> shows up as 2016-09-28 00:00:00 in excel (00:00:00 is added for no reason)
#### Expected Output
2016-09-28 in excel
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.11-23.53.amzn1.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.0
pip: 8.1.2
setuptools: 12.2
Cython: None
numpy: 1.7.2
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.1
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.2.0
openpyxl: 2.4.0-b1
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: None
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: 1.0.14
pymysql: None
psycopg2: None
jinja2: 2.7.2
boto: 2.39.0
pandas_datareader: None
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} | 1 | 2016-07-18T18:38:35Z | 2016-07-19T01:16:41Z | 2016-07-19T01:16:41Z | CONTRIBUTOR | null | Using these DataFrames for this example:
```
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 can perform the `pd.merge_asof()` as expected:
```
In [153]: df1
Out[153]:
time username
0 2016-07-15 13:30:00.030 bob
In [154]: df2
Out[154]:
time version
0 2016-07-15 13:30:00.000 1
1 2016-07-15 13:30:00.030 2
In [155]: pd.merge_asof(df1, df2, on='time')
Out[155]:
time username version
0 2016-07-15 13:30:00.030 bob 2
```
If I disable the exact matches, then I get the prior entry as expected:
```
In [156]: pd.merge_asof(df1, df2, on='time', allow_exact_matches=False)
Out[156]:
time username version
0 2016-07-15 13:30:00.030 bob 1
```
But the tolerance isn't respected when I disable the exact matches!
```
In [157]: pd.merge_asof(df1, df2, on='time', allow_exact_matches=False, tolerance=pd.Timedelta('10ms'))
Out[157]:
time username version
0 2016-07-15 13:30:00.030 bob 1
```
I expect to get a null here.
This is pandas version `0.18.0+408.gd8f3d73`.
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} | 4 | 2016-07-18T19:40:13Z | 2019-05-13T04:03:03Z | 2019-05-13T04:03:02Z | CONTRIBUTOR | null | First, assigning a shorter Categorical into a longer DataFrame does something strange:
```
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3]})
df['b'] = 4
df['c'] = pd.Series([5, 6, 7, 8])
df['d'] = pd.Series([9])
df['e'] = pd.Categorical('foo')
```
If you `print df` it looks reasonable:
```
a b c d e
0 1 4 5 9.0 foo
1 2 4 6 NaN
2 3 4 7 NaN
```
But if you do `df.iloc[1]` you get:
```
IndexError: index 1 is out of bounds for axis 0 with size 1
```
Next, try a Categorical longer than the DataFrame:
```
df['f'] = pd.Categorical(['foo', 'bar', 'baz', 'qux'])
```
This does not look right:
```
a b c d e f
0 1 4 5 9.0 foo foo
1 2 4 6 NaN bar
2 3 4 7 NaN baz
qux
```
When we assigned a 4-element Series (`c`), the extra elements were truncated. Yet a 4-element Categorical ends up with a "trailing value" which has no index and does not change `len(df)` (which is still 3, despite the appearance when printed).
I would expect the above code to give a result more similar to what you get if you use `Series` everywhere instead of `Categorical`, i.e.:
```
a b c d e f
0 1 4 5 9.0 foo foo
1 2 4 6 NaN NaN bar
2 3 4 7 NaN NaN baz
```
In which case `df.iloc[1]` works:
```
a 2
b 4
c 6
d NaN
e NaN
f bar
```
I'm using Pandas 0.18.1.
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} | 4 | 2016-07-18T21:01:32Z | 2016-07-18T21:46:58Z | 2016-07-18T21:45:35Z | NONE | null | #### Code Sample, a copy-pastable example if possible
I have a dataframe with two columns `start` and `end` both have the dtype of `datetime64[ns, tzoffset(None, -21600)]` but depending on how I access it the output removes the timezone info for for example
```
df.ix[0, 'end']
Out[30]: Timestamp('2016-07-17 06:26:00')
```
but
```
df['end'][0]
Out[32]: Timestamp('2016-07-17 00:26:00-0600', tz='tzoffset(None, -21600)')
```
Issue rises when I am doing something like this (`length` is pandas.tslib.Timedelta):
```
df.ix[0, 'end'] = df.ix[0, 'start'] + df.ix[0, 'length']
ValueError Traceback (most recent call last)
<ipython-input-34-f63ebfd12a2e> in <module>()
----> 1 df.ix[0, 'end'] = df.ix[0, 'start'] + df.ix[0, 'length']
/usr/lib64/python2.7/site-packages/pandas/core/indexing.pyc in __setitem__(self, key, value)
130 key = com._apply_if_callable(key, self.obj)
131 indexer = self._get_setitem_indexer(key)
--> 132 self._setitem_with_indexer(indexer, value)
133
134 def _has_valid_type(self, k, axis):
/usr/lib64/python2.7/site-packages/pandas/core/indexing.pyc in _setitem_with_indexer(self, indexer, value)
534 # scalar
535 for item in labels:
--> 536 setter(item, value)
537
538 else:
/usr/lib64/python2.7/site-packages/pandas/core/indexing.pyc in setter(item, v)
459 s._consolidate_inplace()
460 s = s.copy()
--> 461 s._data = s._data.setitem(indexer=pi, value=v)
462 s._maybe_update_cacher(clear=True)
463
/usr/lib64/python2.7/site-packages/pandas/core/internals.pyc in setitem(self, **kwargs)
2915
2916 def setitem(self, **kwargs):
-> 2917 return self.apply('setitem', **kwargs)
2918
2919 def putmask(self, **kwargs):
/usr/lib64/python2.7/site-packages/pandas/core/internals.pyc in apply(self, f, axes, filter, do_integrity_check, consolidate, raw, **kwargs)
2888
2889 kwargs['mgr'] = self
-> 2890 applied = getattr(b, f)(**kwargs)
2891 result_blocks = _extend_blocks(applied, result_blocks)
2892
/usr/lib64/python2.7/site-packages/pandas/core/internals.pyc in setitem(self, indexer, value, mgr)
640
641 # coerce args
--> 642 values, _, value, _ = self._try_coerce_args(self.values, value)
643 arr_value = np.array(value)
644
/usr/lib64/python2.7/site-packages/pandas/core/internals.pyc in _try_coerce_args(self, values, other)
2270 # test we can have an equal time zone
2271 if tz is None or str(tz) != str(self.values.tz):
-> 2272 raise ValueError("incompatible or non tz-aware value")
2273 other_mask = isnull(other)
2274 other = other.tz_localize(None).value
ValueError: incompatible or non tz-aware value
```
However if I do it like this:
`df.ix[0, 'end'] = df['start'][0] + df.ix[0, 'length']`
It works fine since that type of selection does not lose the timezone info.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.5.7-200.fc23.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.utf8
pandas: 0.18.1
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: None
IPython: 4.1.1
sphinx: 1.2.3
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 0.6.0
tables: None
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: 1.8.6
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.4.4
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
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} | 2 | 2016-07-18T21:41:52Z | 2016-07-19T01:16:41Z | 2016-07-19T01:16:41Z | CONTRIBUTOR | null | closes #13695
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} | 4 | 2016-07-19T00:41:52Z | 2019-09-05T13:16:20Z | 2016-07-19T01:25:53Z | CONTRIBUTOR | null | I was wondering if, given the recent set of developments and improvements to `asfreq` and `resample`, we now have a more efficient method for solving [this problem](http://stackoverflow.com/questions/29356412/efficiently-re-indexing-one-level-with-forward-fill-in-a-multi-index-dataframe) [from SO].
Example:
```
item_uid created_at value
0S0099v8iI 2015-03-25 10652.79
0F01ddgkRa 2015-03-25 1414.71
0F02BZeTr6 2015-03-20 51505.22
0F02BZeTr6 2015-03-23 51837.97
0F02BZeTr6 2015-03-24 51578.63
0F02BZeTr6 2015-03-25 NaN
0F02BZeTr6 2015-03-26 NaN
0F02BZeTr6 2015-03-27 50893.42
0F02BcIzNo 2015-03-17 1230.00
0F02BcIzNo 2015-03-23 1130.00
0F02F4gAMs 2015-03-25 1855.96
0F02Vwd6Ou 2015-03-19 5709.33
0F04OlAs0R 2015-03-18 321.44
0F05GInfPa 2015-03-16 664.68
0F05PQARFJ 2015-03-18 1074.31
0F05PQARFJ 2015-03-26 1098.31
0F06LFhBCK 2015-03-18 211.49
0F06ryso80 2015-03-16 13.73
0F06ryso80 2015-03-20 12.00
0F07gg7Oth 2015-03-19 2325.70
```
which, if we were to resample with a daily frequency, would return (e.g. for `item_uid = 0F02BZeTr6`):
```
item_uid created_at value
0F02BZeTr6 2015-03-20 51505.22
0F02BZeTr6 2015-03-21 51505.22
0F02BZeTr6 2015-03-22 51505.22
0F02BZeTr6 2015-03-23 51837.97
0F02BZeTr6 2015-03-24 51578.63
0F02BZeTr6 2015-03-25 51578.63
0F02BZeTr6 2015-03-26 51578.63
0F02BZeTr6 2015-03-27 50893.42
0F02BZeTr6 2015-03-28 50893.42
0F02BZeTr6 2015-03-29 50893.42
```
I mention this partly because `unstacking` can be very slow when our index is huge. Any thoughts here are welcome!
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} | 10 | 2016-07-19T01:20:36Z | 2016-07-19T04:42:29Z | 2016-07-19T01:22:23Z | CONTRIBUTOR | null | Consider the following DataFrame:
```
0F001tTt1Q 0F00dqgq6a 0F00ewG3TT
1911-01-01 00:00:00.000000 complete no_creatives complete
2016-07-11 04:14:55.281017 None None None
2016-07-11 04:14:55.836767 None None None
2016-07-11 04:14:56.379465 None None None
2016-07-11 04:14:57.078314 None None None
2016-07-11 04:15:48.984814 None None None
2016-07-11 04:27:35.625008 None None None
2016-07-11 04:28:17.095956 None None None
2016-07-11 04:29:35.750822 None None None
2016-07-11 04:30:31.932919 None None None
```
I want to resample it on the following dates with a **forward fill**:
```
dates = pd.date_range(date(2016, 7, 11), date(2016, 7, 18))
```
This doesn't completely do it:
```
> df.reindex(drange, method='ffill')
0F001tTt1Q 0F00dqgq6a 0F00ewG3TT
2016-07-11 complete no_creatives complete
2016-07-12 None None None
2016-07-13 None None None
2016-07-14 None None None
2016-07-15 None None None
2016-07-16 None None None
2016-07-17 None None None
2016-07-18 None None None
```
Oddly, I have to do a `ffill` **"twice"** (below, in the same line):
```
>> df.reindex(drange, method='ffill').ffill()
0F001tTt1Q 0F00dqgq6a 0F00ewG3TT
2016-07-11 complete no_creatives complete
2016-07-12 complete no_creatives complete
2016-07-13 complete no_creatives complete
2016-07-14 complete no_creatives complete
2016-07-15 complete no_creatives complete
2016-07-16 complete no_creatives complete
2016-07-17 complete no_creatives complete
2016-07-18 complete no_creatives complete
```
Is that expected? Am I missing something?
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} | 5 | 2016-07-19T05:43:05Z | 2016-07-22T08:50:48Z | 2016-07-22T08:50:40Z | MEMBER | null | Continues where #10085 left off by removing the `return_type` parameter for good.
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} | 10 | 2016-07-19T07:20:34Z | 2016-07-20T21:27:15Z | 2016-07-20T21:27:06Z | MEMBER | null | Per discussion with @jreback <a href="https://github.com/pydata/pandas/pull/13671#issuecomment-233508468">here</a>.
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} | 6 | 2016-07-19T09:54:15Z | 2016-07-27T10:50:08Z | 2016-07-27T10:49:48Z | CONTRIBUTOR | null | Dear developers,
I am using pandas in an application where I need to process large **csv** files (around 1Gb each) which have approximately 800k records and 400+ columns of mixed type. That is why I decided to use data iterator functionality of **pd.read_csv()**. When experimenting with `chunksize` my application seems to crash somewhere inside **TextReader__string_convert** call.
Here is an archive with a sample CSV data file that seems to cause the crash (it also includes crash dump reports, a copy of the example, and a snapshot of versions of installed python packages).
[read_csv_crash.tar.gz](https://github.com/pydata/pandas/files/371134/read_csv_crash.tar.gz)
#### Code Sample
To run this example you would have to extract `dataset.csv` from the supplied archive.
``` python
import pandas as pd
for n_lines in range(82, 87):
filelike = open("dataset.csv", "r")
iterator_ = pd.read_csv(filelike, header=None, engine="c",
dtype=object, chunksize=n_lines)
for chunk_ in iterator_:
print n_lines, chunk_.iloc[0, 0], chunk_.iloc[-1, 0]
filelike.close()
```
Please, note that this crash does not seem to occur when the file is less than 260Kib. Also note that playing with `low_memory` setting did not alleviate the problem.
#### Expected Output
This code sample outputs this:
```
82 9999-9 9999-9
82 9999-9 9999-9
82 9999-9 9999-9
83 9999-9 9999-9
83 9999-9 9999-9
83 9999-9 9999-9
84 9999-9 9999-9
84 9999-9 9999-9
Segmentation fault: 11
```
#### output of `pd.show_versions()`
The output of this call is attached to this issue.
[pd_show_versions.txt](https://github.com/pydata/pandas/files/371069/pd_show_versions.txt)
#### Python greetings string
```
Python 2.7.10 (v2.7.10:15c95b7d81dc, May 23 2015, 09:33:12)
[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)] on darwin
```
#### OSX version
```
OS Version: Mac OS X 10.11.5 (15F34)
Model: Macmini6,2, BootROM MM61.0106.B0A, 4 processors, Intel Core i7, 2,6 GHz, 16 GB, SMC 2.8f
```
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