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} | 4 | 2014-10-22T11:27:11Z | 2014-11-06T12:43:58Z | 2014-11-06T12:43:58Z | NONE | null | xref #8653
Having a similar issue
idx = date_range('2014-10-08 00:00','2014-10-09 00:00', freq='D', tz='Europe/Berlin')
pd.Series(5, idx).resample('MS')
ValueError: Values falls after last bin
While if I remove the tz :
idx = date_range('2014-10-08 00:00','2014-10-09 00:00', freq='D')
pd.Series(5, idx).resample('MS')
that works perfectly
version : '0.15.0rc1'
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} | 9 | 2014-10-22T17:54:20Z | 2014-11-17T00:34:21Z | 2014-11-17T00:34:21Z | CONTRIBUTOR | null | I'm deploying an PyQt(5.3.2) application using py2app. Everything works fine with 0.14.1, 0.15.0 fails at importing. As an example one can take the following `main.py`:
``` python
import pandas
def main():
x = pandas.Series([0, 1, 2])
print(x)
if __name__ == "__main__":
main()
```
and use the default `setup.py`:
``` python
from setuptools import setup
import py2app
setup(
app=['main.py'],
options={'py2app': {'argv_emulation': True}},
setup_requires=['py2app'],
)
```
and run `python3 setup.py py2app` to build the .app and then start the application (Please note the example can only be runned via Terminal, since it lacks a GUI)
In both Python 2.7.8 and 3.4.2 the .app builds fine but fails on 3.4.2 to start and dumps (stripped to the helpful parts):
```
'module' object has no attribute '__pyx_capi__'
2014-10-22 19:36:32.784 main[26763:321107] main Error
Traceback (most recent call last):
File ".../dist/main.app/Contents/Resources/__ _run()
File ".../dist/main.app/Contents/Resources/__ exec(compile(source, path, 'exec'), globals(), globals())
File ".../dist/main.app/Contents/Resources/main. import pandas
File "pandas/__init__.pyc", line 7, in <module>
File "pandas/tslib.pyc", line 14, in <module>
File "pandas/tslib.pyc", line 10, in __load
File "pandas/tslib.pyx", line 2839, in init pandas.tslib (pandas/tslib.c:80041)
File "pandas/algos.pyc", line 14, in <module>
File "pandas/algos.pyc", line 10, in __load
File "pandas/algos.pyx", line 64, in init pandas.algos (pandas/algos.c:180213)
File "pandas/lib.pyc", line 14, in <module>
File "pandas/lib.pyc", line 10, in __load
File "pandas/lib.pyx", line 1, in init pandas.lib (pandas/lib.c:78098)
AttributeError: 'module' object has no attribute '__pyx_capi__'
```
If the example would run as expected it would print (again stripped):
```
0 0
1 1
2 2
dtype: int64
```
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} | 2 | 2014-10-22T18:30:26Z | 2018-07-06T20:21:05Z | 2017-07-11T09:56:56Z | CONTRIBUTOR | null | I'm trying to save a panel that has a mixed type minor axis using HDFStore. I can save the panel when using the fixed format (although I get a PerformanceWarning), but it fails for the table format. Example code:
``` python
import pandas as pd
import numpy as np
d1 = pd.DataFrame({1:pd.Series(np.random.randn(10)), 2:pd.Series(np.random.randn(10))})
d2 = pd.DataFrame({1:pd.Series(np.random.randn(10)), 2:pd.Series(np.random.randn(10))})
good_panel = pd.Panel({'a':d1, 'b':d2})
print good_panel
with pd.get_store('/tmp/goodstore.h5', mode='w') as store:
store.put('/test', good_panel, 'table', append=False)
b1 = pd.DataFrame({1:pd.Series(np.random.randn(10)), 'a':pd.Series(np.random.randn(10))})
b2 = pd.DataFrame({1:pd.Series(np.random.randn(10)), 'a':pd.Series(np.random.randn(10))})
bad_panel = pd.Panel({'a':b1, 'b':b2})
print bad_panel
with pd.get_store('/tmp/badstore.h5', mode='w') as store:
store.put('/test', bad_panel, 'fixed', append=False)
print 'saved as fixed'
with pd.get_store('/tmp/badstore.h5', mode='w') as store:
store.put('/test', bad_panel, 'table', append=False)
```
The error I get is:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-5-3528d8319daf> in <module>()
9
10 with pd.get_store('/tmp/badstore.h5', mode='w') as store:
---> 11 store.put('/test', bad_panel, 'table', append=False)
/home/tony/.virtualenvs/dev2/lib/python2.7/site-packages/pandas/io/pytables.pyc in put(self, key, value, format, append, **kwargs)
829 format = get_option("io.hdf.default_format") or 'fixed'
830 kwargs = self._validate_format(format, kwargs)
--> 831 self._write_to_group(key, value, append=append, **kwargs)
832
833 def remove(self, key, where=None, start=None, stop=None):
/home/tony/.virtualenvs/dev2/lib/python2.7/site-packages/pandas/io/pytables.pyc in _write_to_group(self, key, value, format, index, append, complib, encoding, **kwargs)
1278
1279 # write the object
-> 1280 s.write(obj=value, append=append, complib=complib, **kwargs)
1281
1282 if s.is_table and index:
/home/tony/.virtualenvs/dev2/lib/python2.7/site-packages/pandas/io/pytables.pyc in write(self, obj, axes, append, complib, complevel, fletcher32, min_itemsize, chunksize, expectedrows, dropna, **kwargs)
3657
3658 # create the table
-> 3659 table = self._handle.create_table(self.group, **options)
3660
3661 else:
/home/tony/.virtualenvs/dev2/lib/python2.7/site-packages/tables/file.pyc in create_table(self, where, name, description, title, filters, expectedrows, chunkshape, byteorder, createparents, obj)
1065 description=description, title=title,
1066 filters=filters, expectedrows=expectedrows,
-> 1067 chunkshape=chunkshape, byteorder=byteorder)
1068
1069 if obj is not None:
/home/tony/.virtualenvs/dev2/lib/python2.7/site-packages/tables/table.pyc in __init__(self, parentnode, name, description, title, filters, expectedrows, chunkshape, byteorder, _log)
805 if new and isinstance(description, dict):
806 # Dictionary case
--> 807 self.description = Description(description)
808 elif new and (type(description) == type(IsDescription)
809 and issubclass(description, IsDescription)):
/home/tony/.virtualenvs/dev2/lib/python2.7/site-packages/tables/description.pyc in __init__(self, classdict, nestedlvl, validate)
493 'got: "%s". Please make use of the Col(), or '
494 'descendant, constructor to properly '
--> 495 'initialize columns.' % object)
496 object._v_pos = pos # Set the position of this object
497 object._v_parent = self # The parent description
TypeError: Passing an incorrect value to a table column. Expected a Col (or subclass) instance and got: "ObjectAtom()". Please make use of the Col(), or descendant, constructor to properly initialize columns.
```
My system info:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.17.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.0
nose: 1.3.4
Cython: 0.20.1
numpy: 1.9.0
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.3.0
sphinx: 1.2.2
patsy: 0.2.1
dateutil: 2.2
pytz: 2014.7
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.0
openpyxl: 1.8.6
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.3.5
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/8604 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8604/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8604/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8604/events | https://github.com/pandas-dev/pandas/pull/8604 | 46,550,068 | MDExOlB1bGxSZXF1ZXN0MjMxODI5MzA= | 8,604 | Fix typo in visualization.rst doc | {
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| null | 1 | 2014-10-22T19:28:18Z | 2014-10-22T20:47:45Z | 2014-10-22T20:47:38Z | CONTRIBUTOR | null | {
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| null | 0 | 2014-10-22T20:43:13Z | 2014-10-27T15:39:02Z | 2014-10-27T15:39:02Z | MEMBER | null | - changed link to toplevel read_csv
- since `infer_types` is in essence removed, the comment made no sense anymore I think
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https://api.github.com/repos/pandas-dev/pandas/issues/8606 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8606/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8606/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8606/events | https://github.com/pandas-dev/pandas/pull/8606 | 46,558,735 | MDExOlB1bGxSZXF1ZXN0MjMxODgzNjY= | 8,606 | DOC: reorg whatsnew files to subfolder | {
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| null | 1 | 2014-10-22T20:48:17Z | 2014-10-23T06:57:57Z | 2014-10-23T06:57:57Z | MEMBER | null | Moved all whatsnew files into a subfolder, because it are becoming a bit too many files in one folder (doc/source/) I think.
@jreback What do you think?
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} | 8 | 2014-10-22T21:15:03Z | 2014-10-30T11:19:44Z | 2014-10-27T12:13:31Z | NONE | null | My dataframe has a timestamp column that is encoded as unix epoch. When I convert the column using named index selection it works fine, but when I use the '.ix' syntax it coerces the whole dataframe. Example:
```
import pandas as pd
df = pd.DataFrame(
{'timestamp':[1413840976, 1413842580, 1413760580],
'delta':[1174, 904, 161],
'elapsed':[7673, 9277, 1470]
})
df2 = df.copy()
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='s')
df2.ix[:,2] = pd.to_datetime(df['timestamp'], unit='s')
df
delta elapsed timestamp
0 1174 7673 2014-10-20 21:36:16
1 904 9277 2014-10-20 22:03:00
2 161 1470 2014-10-19 23:16:20
df2
delta elapsed timestamp
0 2014-10-20 21:36:16 1970-01-01 00:00:00.000007673 1970-01-01 00:00:01.413840976
1 2014-10-20 22:03:00 1970-01-01 00:00:00.000009277 1970-01-01 00:00:01.413842580
2 2014-10-19 23:16:20 1970-01-01 00:00:00.000001470 1970-01-01 00:00:01.413760580
```
I strongly suspect the difference in behavior here is problematic and should be resolved. If this is actually "how things should work," I'd greatly appreciate it if someone could explain why the different indexing styles produce these different results
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} | 4 | 2014-10-23T00:31:10Z | 2014-10-30T11:19:44Z | 2014-10-25T23:35:27Z | NONE | null | The following used to work:
```
In [7]: import pandas as pd
In [8]: import numpy as np
In [9]: a = np.zeros(5, dtype=np.float)
In [10]: df = pd.DataFrame({"foo": [10, 20, 30, 40, 50]})
In [11]: df.index
Out[11]: Int64Index([0, 1, 2, 3, 4], dtype='int64')
In [12]: df.index * 3.2
Out[12]: Int64Index([0.0, 3.2, 6.4, 9.6, 12.8], dtype='float64')
In [13]: a - df.index * 3.2
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-13-8a6574b7beca> in <module>()
----> 1 a - df.index * 3.2
TypeError: unsupported operand type(s) for -: 'numpy.ndarray' and 'Int64Index'
In [14]: pd.__version__
Out[14]: '0.15.0'
```
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} | 8 | 2014-10-23T02:29:08Z | 2014-10-30T11:20:23Z | 2014-10-24T23:51:37Z | MEMBER | null | This fix ensures that `idx.shape == (len(idx),)` if `idx` is a MultiIndex.
Also added tests for other ndarray compat properties.
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| null | 2 | 2014-10-23T02:41:56Z | 2014-10-25T02:28:09Z | 2014-10-25T02:27:17Z | CONTRIBUTOR | null | The arguments are showing in two lists.
[Link](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_html.html)
I'll fix this myself, if somebody doesn't get to it quickly. It's good for a first PR, and I've done a couple PRs myself.

Edit: Removed reference to formatters.
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| null | 9 | 2014-10-23T10:11:10Z | 2014-10-27T02:09:26Z | 2014-10-25T00:40:01Z | NONE | null | In my understanding, DataFrame(in the way how it's displayed) could be treated as a **transposed** SQL table. A row represents a 'property' and a column represents a record. It's clear and consistent in creation using `dict of Series or dicts` and `dict of ndarrays / lists`. As below
``` py
In [32]: d = {'user1':['Jean', 'Grey', 18, 'F'],
'user2':['Logan', 'Howlett', 33, 'M']}
In [34]: DataFrame(d, index=['firstname','lastname','age','gender'])
Out[34]:
user1 user2
firstname Jean Logan
lastname Grey Howlett
age 18 33
gender F M
```
However when creation using Structed ndarrary, it seems work the other way around, a row represent a record. As below.
``` py
In [45]: dtype=np.dtype([('firstname','U10'),('lastname','U10'),('age','int32'), ('gender', 'U1')])
In [46]: dt = np.array([('Jean','Grey',18,'F'),('Logan', 'Howlett', 33,'M')], dtype=dtype)
In [47]: DataFrame(dt)
Out[47]:
firstname lastname age gender
0 Jean Grey 18 F
1 Logan Howlett 33 M
```
This is bugging me a lot, why is this(I'm guessing the internal store), but is there a way to overcome it?
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} | 19 | 2014-10-23T10:31:22Z | 2015-07-13T08:44:50Z | 2014-11-05T11:41:57Z | NONE | null | Yahoo reconfigured their options page (http://finance.yahoo.com/q/op?s=AAPL+Options) which broke the entire Options class.
Are there plans for a fix in a future release?
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} | 40 | 2014-10-23T12:11:27Z | 2015-03-04T20:46:25Z | 2015-03-04T20:46:25Z | MEMBER | null | I didn't directly find an issue about it, or an explanation in the docs, but I stumbled today on the following, which did surprise me a bit:
Considering the following dataframe:
```
In [18]: df = pd.DataFrame(np.random.randn(5,2), index=pd.date_range('2012-01-01', periods=5))
In [19]: df
Out[19]:
0 1
2012-01-01 2.511337 -0.776326
2012-01-02 0.133589 0.441911
2012-01-03 0.348167 1.285188
2012-01-04 1.075843 1.282131
2012-01-05 0.683006 0.558459
```
Slicing with a label that is not included in the index works with `.ix`, but not with `.loc`:
```
In [20]: df.ix['2012-01-03':'2012-01-31']
Out[20]:
0 1
2012-01-03 0.348167 1.285188
2012-01-04 1.075843 1.282131
2012-01-05 0.683006 0.558459
In [21]: df.loc['2012-01-03':'2012-01-31']
...
KeyError: 'stop bound [2012-01-31] is not in the [index]'
```
Context: I was updating some older code, and I wanted to replace `.ix` with `.loc` (as this is what we recommend if it is purely label based to prevent confusion).
Some things:
- If this is intended, I don't find this stated somewhere in the docs. So the docs are at least lacking at this point.
- the inconsistency between `[]`, `.ix[]` and `.loc[]` is a bit surprising here
- it is also inconsistent with `iloc` -> that behaviour was changed in 0.14 to allow out of bound slicing (http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#whatsnew-0140-api)
- Specifically for datetime-line indexing, it is also inconsistent with the feature of partial string indexing: `df.loc['2012-01-03':'2012-01']` will work and do the expected while `df.loc['2012-01-03':'2012-01-31']` fails
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} | 20 | 2014-10-23T14:20:45Z | 2014-11-05T14:34:26Z | 2014-11-05T14:34:26Z | MEMBER | null | From http://stackoverflow.com/questions/26526230/plotting-datetimeindex-on-x-axis-with-matplotlib-creates-wrong-ticks-in-pandas-0
```
df = pd.DataFrame({'RandomValues': np.random.randint(1, 50, 60)},
index=pd.date_range("2012-01-01", periods=60))
```
Plotting of course works:
```
In [79]: df.plot()
Out[79]: <matplotlib.axes._subplots.AxesSubplot at 0xf571550>
```
But plotting with matplotlib's `plot` functions gives no longer a datetime formatted x-axis, but just ints:
```
In [80]: plt.plot(df.index, df.RandomValues)
Out[80]: [<matplotlib.lines.Line2D at 0xfb5f748>]
```
**Reason**: matplotlibs `plot` calls `np.atleast_1d` on the input (https://github.com/matplotlib/matplotlib/blob/v1.4.0/lib/matplotlib/axes/_base.py#L220). Previously this returned an Index, now it returns a array of `datetime64` values, which matplotlib can't handle.
Issues:
- This is actually a problem of matplotlib (they should support datetime64), but of course: a lot of users rely now on the behaviour that it did work with a pandas `Index` also.
- Is there a way to restore this behaviour? I don't see directly a solution?
- We should update our docs, as this behaviour is also mentioned there briefly: http://pandas.pydata.org/pandas-docs/stable/visualization.html#plotting-directly-with-matplotlib + figure out why this didn't fail in the doc build (the figure seems not updated with the 0.15 doc build)
It seems there was some discussion about this in the Index-no-subclass PR: https://github.com/pydata/pandas/pull/7891#issuecomment-50936333 and https://github.com/pydata/pandas/pull/7891#discussion_r15862386 (I commented there about exactly this issue, but I don't know why we didn't look further in it then)
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} | 4 | 2014-10-23T17:55:20Z | 2015-07-29T20:17:19Z | 2015-07-29T20:17:06Z | NONE | null | to_datetime is not converting input consistently between a pandas series or a python list of either strings or datetime objects. for example:
import pandas as pd
s = pd.Series(['102214','102314'])
df = pd.Series({'dates':['102214','102314']})
lst = ['102214','102314']
pd.to_datetime(s,format='%m%d%y') and pd.to_datetime(df['dates'],format='%m%d%y') return a numpy.datetime64 ndarray, while pd.to_datetime(lst,format='%m%d%y') returns a pandas.DateTimeIndex object. Is this behavior an expected result in 0.15.0?
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} | 3 | 2014-10-23T18:10:13Z | 2018-06-18T22:34:29Z | 2018-06-18T22:34:29Z | CONTRIBUTOR | null | xref #5694 #8531 (?)
xref #8817
This is presumably caused by the fact that pytz time zones internalize the offset of the current time.
``` python
In [1]: import pandas as pd
In [2]: idx = pd.date_range('2013-11-03', tz='America/Chicago',
...: periods=6, freq='H')
In [3]: pd.Series(index=idx)
Out[3]:
2013-11-03 00:00:00-05:00 NaN
2013-11-03 01:00:00-05:00 NaN
2013-11-03 01:00:00-06:00 NaN
2013-11-03 02:00:00-06:00 NaN
2013-11-03 03:00:00-06:00 NaN
2013-11-03 04:00:00-06:00 NaN
Freq: H, dtype: float64
In [4]: pd.Series(index=idx).shift(freq='H')
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-4-19ee418c9aa1> in <module>()
----> 1 pd.Series(index=idx).shift(freq='H')
/Users/afni/homebrew/Cellar/python/2.7.5/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/generic.pyc in shift(self, periods, freq, axis, **kwds)
3290 new_data = self._data.shift(periods=periods, axis=block_axis)
3291 else:
-> 3292 return self.tshift(periods, freq, **kwds)
3293
3294 return self._constructor(new_data).__finalize__(self)
/Users/afni/homebrew/Cellar/python/2.7.5/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/generic.pyc in tshift(self, periods, freq, axis, **kwds)
3386 else:
3387 new_data = self._data.copy()
-> 3388 new_data.axes[block_axis] = index.shift(periods, offset)
3389
3390 return self._constructor(new_data).__finalize__(self)
/Users/afni/homebrew/Cellar/python/2.7.5/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/tseries/index.pyc in shift(self, n, freq)
855 end = self[-1] + n * self.offset
856 return DatetimeIndex(start=start, end=end, freq=self.offset,
--> 857 name=self.name, tz=self.tz)
858
859 def repeat(self, repeats, axis=None):
/Users/afni/homebrew/Cellar/python/2.7.5/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, closed, **kwds)
204 return cls._generate(start, end, periods, name, freq,
205 tz=tz, normalize=normalize, closed=closed,
--> 206 infer_dst=infer_dst)
207
208 if not isinstance(data, (np.ndarray, ABCSeries)):
/Users/afni/homebrew/Cellar/python/2.7.5/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/tseries/index.pyc in _generate(cls, start, end, periods, name, offset, tz, normalize, infer_dst, closed)
369 if tz is not None and inferred_tz is not None:
370 if not inferred_tz == tz:
--> 371 raise AssertionError("Inferred time zone not equal to passed "
372 "time zone")
373
AssertionError: Inferred time zone not equal to passed time zone
```
Playing with `dateutil` and `pytz` timezones makes me despair of repairing that assertion to be anything sane. Is it actually needed, or can we just turn it into a warning?
Observations:
- two `pytz.DstTzInfo`s of the same zoneinfo zone that have been `.normalize()`ed to different times may compare different
- a `pytz.*TzInfo` has a `zone` member containing the name of the zoneinfo zone from which it was constructed
- a `dateutil.tzfile` has no public members beyond the `tzinfo` API, which is insufficient to tell whether two time zones are equal
- a `pytz` time zone and a `dateutil` time zone appear to compare different under all circumstances, regardless of whether they represent the same time zone
- the various `dateutil` time zone classes have no common base class within the `dateutil` package
- `pytz` zones constructed from different names for the same zoneinfo zone (e.g. `UTC` and `Etc/UTC`) compare different
Ugh.
from #8817
```
import pandas as pd
import pytz
import datetime
dt = datetime.datetime(2014, 11, 14, 0)
dt_est = pytz.timezone('EST').localize(dt)
s = pd.Series(data=[1], index=[dt_est])
s.shift(0, freq='h') # 2014-11-14 00:00:00-05:00 (seems okay)
s.shift(-1, freq='h') # 2014-11-13 18:00:00-05:00 (expected 2014-11-13 23:00:00)
s.shift(1, freq='h') # 2014-11-13 20:00:00-05:00 (expected 2014-11-14 01:00:00)
s.shift(-1, freq='s') # 2014-11-13 18:59:59-05:00 (same with other freq)
```
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} | 13 | 2014-10-23T18:19:04Z | 2016-09-01T07:28:07Z | 2016-09-01T07:28:07Z | CONTRIBUTOR | null | Fixing this will be easier once numpy/numpy#5222 is fixed.
``` python
In [1]: import pandas as pd
In [2]: pd.Series([0, pd.tslib.iNaT], dtype='m8[ns]')
Out[2]:
0 0 days
1 NaT
dtype: timedelta64[ns]
In [3]: _.median()
Out[3]:
0 -53375 days, 23:53:38.427388
dtype: timedelta64[ns]
```
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} | 28 | 2014-10-23T21:14:23Z | 2014-12-01T08:24:47Z | 2014-12-01T08:13:17Z | CONTRIBUTOR | null | This is my first attempt at a PR. I just made a little change to allow an error message. Would appreciate some feedback.
closes https://github.com/pydata/pandas/issues/8015
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| null | 4 | 2014-10-23T21:56:54Z | 2015-05-09T15:58:46Z | 2015-05-09T15:58:46Z | CONTRIBUTOR | null | This fix is just a band-aid-- a real fix will have to wait for PEP 431 to unite all the time zone implementations.
Also refine a bare `except:` in the same method.
Fixes #8616
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| null | 2 | 2014-10-23T23:25:44Z | 2014-11-10T14:28:58Z | 2014-10-27T18:34:56Z | NONE | null | I want to use [Sourcegraph code search and code review](https://sourcegraph.com) with pandas. A project maintainer needs to enable it to set up a webhook so the code is up-to-date there.
Could you please enable pandas on @Sourcegraph by going to https://sourcegraph.com/github.com/pydata/pandas and clicking on Settings? (It should only take 15 seconds.)
Thank you!
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} | 12 | 2014-10-24T04:05:07Z | 2014-12-03T22:37:35Z | 2014-12-03T22:37:35Z | NONE | null | I have an issue using to_csv on a DataFrame object. It has a large number of columns d.shape = (3,454731).
``` python
Python 3.4.2 (default, Oct 8 2014, 13:44:52)
[GCC 4.9.1 20140903 (prerelease)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.14.22-1-lts
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_AU.UTF-8
pandas: 0.15.0-16-g7012d71
nose: 1.3.4
Cython: 0.21.1
numpy: 1.9.0
scipy: 0.14.0
statsmodels: None
IPython: 2.3.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.2
pytz: 2014.7
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.2
openpyxl: 1.8.6
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: None
html5lib: 0.999
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: 2.5.4 (dt dec pq3 ext)
>>> d=pd.read_msgpack('test.mpk')
>>> d.shape
(3, 454731)
>>> d.to_csv('test.csv')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python3.4/site-packages/pandas/util/decorators.py", line 88, in wrapper
return func(*args, **kwargs)
File "/usr/lib/python3.4/site-packages/pandas/core/frame.py", line 1154, in to_csv
formatter.save()
File "/usr/lib/python3.4/site-packages/pandas/core/format.py", line 1400, in save
self._save()
File "/usr/lib/python3.4/site-packages/pandas/core/format.py", line 1492, in _save
chunks = int(nrows / chunksize) + 1
ZeroDivisionError: division by zero
>>> d.T.to_csv('test.csv')
>>>
```
Not sure what's going on here - I've written a nosetest here (any tips for improvements in my test?)
https://github.com/rmorgans/pandas/commit/f3d0a9e629866fb0d4bcfdde13b631997efa7e5f
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} | 1 | 2014-10-24T04:56:35Z | 2014-10-30T11:20:23Z | 2014-10-24T18:36:38Z | CONTRIBUTOR | null | Fixes #8589.
I centralized a bit of logic into tile.py's `_bins_to_cuts()` so that this change could be made in one place, hope that's okay.
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} | 0 | 2014-10-24T07:18:59Z | 2014-10-31T22:35:39Z | 2014-10-31T22:35:39Z | NONE | null | ``` python
x=pd.DataFrame([[1,'John P. Doe'],[2,'Jane Dove'],[1,'John P. Doe']],
columns=['person_id','person_name'])
x['person_name']=pd.Categorical(x.person_name) # doing this breaks transform
g=x.groupby(['person_id'])
g.transform(lambda x:x)
AttributeError: 'ObjectBlock' object has no attribute '_holder'
```
using `drop_duplicates` inside apply (I often need this):
``` python
g.apply(lambda x: x.drop_duplicates('person_name'))
SystemError: numpy/core/src/multiarray/iterators.c:370: bad argument to internal function
```
and iloc is not what I expected:
``` python
x.iloc[0].person_name # ok
x.person_name.iloc[0] # err
TypeError: Argument 'arr' has incorrect type (expected numpy.ndarray, got Series)
```
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} | 4 | 2014-10-24T08:10:06Z | 2014-10-31T12:50:09Z | 2014-10-31T12:50:09Z | NONE | null | ``` python
In [29]: from sqlalchemy import create_engine
...: engine = create_engine('sqlite://')
...: df=pd.DataFrame([[1,'John P. Doe'],[2,'Jane Dove'],[1,'John P. Doe']],
...: columns=['person_id','person_name'])
...: df.to_sql('data1',engine)
...: df['person_name']=pd.Categorical(df.person_name)
...: df.to_sql('data2',engine)
...: print pd.read_sql_table('data1',engine)
...: print pd.read_sql_table('data2',engine)
index person_id person_name
0 0 1 John P. Doe
1 1 2 Jane Dove
2 2 1 John P. Doe
index person_id person_name
0 0 1 J
1 1 2 o
2 2 1 h
```
using relational db to store catagorical columns in seperare tables would be very cool, and rebuilding the frame in pandas by JOIN from the multiple tables would save time on the wire. also memory if categorical was build directly.
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} | 12 | 2014-10-24T18:42:12Z | 2017-04-14T13:32:37Z | 2017-04-14T13:32:37Z | CONTRIBUTOR | null | xref #8595
something like: `Interval(left=(-1.5,'open),right=(0,'closed'))`
reprs to `(-1.5,0]`
can be used immediately in the index values for `pd.cut` and such (as an object index)
evenutally can form the basis for `IntervalIndex`
cc @shoyer
cc @JanSchulz
cc @rosnfeld
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} | 24 | 2014-10-24T20:13:53Z | 2014-11-11T12:58:20Z | 2014-10-28T00:03:02Z | NONE | null | `to_records()` and the df constructor are broken for data containing categoricals when created with `dtype='category'` or `astype('category')`.
Broken (`TypeError: data type not understood`):
``` python
pd.DataFrame(list('abc'),dtype='category')
df=pd.DataFrame(list('abc'), columns=['A'])
df['A'] = df['A'].astype('category')
df.to_records()
```
Works:
``` python
pd.Series(list('abc'), dtype='category')
pd.DataFrame(list('abc'), dtype=pd.core.common.CategoricalDtype)
df=pd.DataFrame(list('abc'), columns=['A'])
df['A'] = df['A'].astype(pd.core.common.CategoricalDtype)
df.to_records()
pd.Series(list('abc'), dtype='category').to_frame().to_records()
```
`to_frame` seems to remove the category dtype though.
Pandas version: 0.15.0-20-g2737f5a
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} | 13 | 2014-10-24T20:27:02Z | 2016-07-21T15:43:00Z | 2015-11-18T11:47:32Z | CONTRIBUTOR | null | So huge win to perform .str operations on the `.categories` of a `Categorical` (versus actually doing these on an object array), when you have << number of categories relative to the number of objects.
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} | 5 | 2014-10-24T22:10:05Z | 2020-11-18T18:21:51Z | 2020-11-18T18:21:51Z | NONE | null | There seems no direct way to return to the original dtype and the [documentation](http://pandas-docs.github.io/pandas-docs-travis/categorical.html#object-creation) recommends: _"To get back to the original Series or numpy array, use Series.astype(original_dtype) or np.asarray(categorical)"_
That's slow and a `decode` or `decat` method would be trivial:
``` python
df=pd.DataFrame(np.random.choice(list(u'abcde'), 4e6).reshape(1e6, 4),
columns=list(u'ABCD'))
for col in df.columns: df[col] = df[col].astype('category')
%timeit for col in df.columns: df[col].astype('unicode')
1 loops, best of 3: 1.06 s per loop
%timeit for col in df.columns: cats=df[col].cat.categories; cats[df[col].cat.codes]
10 loops, best of 3: 33.2 ms per loop
```
I was working with ~10 categories (partially longer strings) on a 20 mio rows dataset where the difference was even bigger (unfortunately can't reproduce it with dummy data) and using `astype` felt rather buggy (minutes) than only a performance issue.
Given the current limitations on exporting categorical data, having a fast `decode` method would be very convenient. Since category codes are most often strings an optional parameter for direct character set encoding would also be good to have for such a method.
``` python
%timeit for col in df.columns: df[col].astype('unicode').str.encode('latin1')
1 loops, best of 3: 3.95 s per loop
%timeit for col in df.columns: cats=pd.Series(df[col].cat.categories).str.encode('latin1'); cats[df[col].cat.codes]
10 loops, best of 3: 74.5 ms per loop
```
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} | 6 | 2014-10-25T01:19:32Z | 2014-10-26T19:01:10Z | 2014-10-25T01:27:11Z | MEMBER | null | I'm not entirely sure why (perhaps just the trouble of implementing it), but categoricals do not currently support arithmetic:
> In contrast to statistical categorical variables, categorical data might have an order (e.g. ‘strongly agree’ vs ‘agree’ or ‘first observation’ vs. ‘second observation’), but numerical operations (additions, divisions, ...) are not possible.
Similarly to string operations (#8627), arithmetic with scalars could very efficient transform categoricals into new categoricals.
In contrast, array + array operations should probably just return a normal array.
CC @JanSchultz @jreback
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} | 1 | 2014-10-25T03:38:49Z | 2015-06-17T00:52:18Z | 2015-06-17T00:52:18Z | CONTRIBUTOR | null | http://docs.travis-ci.com/user/multi-os/
http://docs.travis-ci.com/user/osx-ci-environment/
so might be nice to have say a. 2.7 and 3.3/4 build on osx
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} | 31 | 2014-10-25T04:46:36Z | 2014-11-10T15:25:50Z | 2014-11-05T11:41:57Z | CONTRIBUTOR | null | This should fix #8612.
Will need a sample of a during the week HTML to make sure that the underlying price and quote time work with that format. I'll update for that on Monday.
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} | 2 | 2014-10-25T07:46:11Z | 2016-12-17T14:32:41Z | 2014-10-25T12:51:38Z | NONE | null | ``` python
df=pd.DataFrame([[1,'John P. Doe'],[2,'Jane Dove'],[1,'John P. Doe']])
df[1]=pd.Categorical(df[1])
pd.read_msgpack(df.to_msgpack())
KeyError: 100
```
#8626, #8624
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| null | 3 | 2014-10-25T13:53:55Z | 2014-10-25T15:00:16Z | 2014-10-25T14:08:33Z | NONE | null | It would be great to generally apply graceful degradation for export of categorical data instead of raising exceptions.
Currently this is only the case for `to_sql` and `to_csv`, where the categories are exported, while `to_pickle` is the only option to persist categorical data
For Stata and HDF it is:
- `to_hdf`: `NotImplementedError: cannot store a category dtype`
- `to_stata`: `ValueError: Data type category not currently understood. Please report an error to the developers.`
As long as a backend does not support categoricals or the conversion is not yet implemented, why not generally export categories as a fallback? With the separately discussed decode method (#8628) this would be easy. If the same rigor (backend supports data type natively or fail) would be applied to CSV-IO we could only export string dtypes to CSV.
Thinking one step further, the `to_...` functions could have an optional parameter named something like `convert_cat` with options:
- None: either try to export as a categorical (pickle, potentially HDF, Stata) or raise exception
- 'category': only export categories (decode method)
- 'code': export s.cat.codes
- 'mapping' or 'emulate': export code:category mapping in one/two columns or separate table/frame/... with the code-category mapping.
The last option would probably need additional parameters to control the technical implementation (e.g. table name for mapping or suffixes as for join/merge, ...)
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} | 0 | 2014-10-25T15:45:59Z | 2014-10-30T11:20:23Z | 2014-10-25T23:35:27Z | CONTRIBUTOR | null | closes #8608
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| null | 2 | 2014-10-25T18:39:18Z | 2014-10-26T17:11:41Z | 2014-10-26T17:11:41Z | NONE | null | I'm running into issues with a particular Excel file (which I have exported to CSV) that seems to have some characters that can't be dealt with in IPython notebook. Wasn't sure whether to post here or on IPython's tracker. The file imports fine, but on the notebook, I get this error when I try to display it:
```
---------------------------------------------------------------------------
UnicodeDecodeError Traceback (most recent call last)
<ipython-input-31-a83dda38f1a4> in <module>()
----> 1 pd.read_csv('data/Study Characteristics.csv')
/usr/local/lib/python2.7/site-packages/IPython/core/displayhook.pyc in __call__(self, result)
242 self.update_user_ns(result)
243 self.log_output(format_dict)
--> 244 self.finish_displayhook()
245
246 def flush(self):
/usr/local/lib/python2.7/site-packages/IPython/kernel/zmq/displayhook.pyc in finish_displayhook(self)
66 sys.stdout.flush()
67 sys.stderr.flush()
---> 68 self.session.send(self.pub_socket, self.msg, ident=self.topic)
69 self.msg = None
70
/usr/local/lib/python2.7/site-packages/IPython/kernel/zmq/session.pyc in send(self, stream, msg_or_type, content, parent, ident, buffers, track, header, metadata)
647 if self.adapt_version:
648 msg = adapt(msg, self.adapt_version)
--> 649 to_send = self.serialize(msg, ident)
650 to_send.extend(buffers)
651 longest = max([ len(s) for s in to_send ])
/usr/local/lib/python2.7/site-packages/IPython/kernel/zmq/session.pyc in serialize(self, msg, ident)
551 content = self.none
552 elif isinstance(content, dict):
--> 553 content = self.pack(content)
554 elif isinstance(content, bytes):
555 # content is already packed, as in a relayed message
/usr/local/lib/python2.7/site-packages/IPython/kernel/zmq/session.pyc in <lambda>(obj)
83 # disallow nan, because it's not actually valid JSON
84 json_packer = lambda obj: jsonapi.dumps(obj, default=date_default,
---> 85 ensure_ascii=False, allow_nan=False,
86 )
87 json_unpacker = lambda s: jsonapi.loads(s)
/usr/local/lib/python2.7/site-packages/zmq/utils/jsonapi.pyc in dumps(o, **kwargs)
38 kwargs['separators'] = (',', ':')
39
---> 40 s = jsonmod.dumps(o, **kwargs)
41
42 if isinstance(s, unicode):
/usr/local/Cellar/python/2.7.6_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/json/__init__.pyc in dumps(obj, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, encoding, default, sort_keys, **kw)
248 check_circular=check_circular, allow_nan=allow_nan, indent=indent,
249 separators=separators, encoding=encoding, default=default,
--> 250 sort_keys=sort_keys, **kw).encode(obj)
251
252
/usr/local/Cellar/python/2.7.6_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/json/encoder.pyc in encode(self, o)
208 if not isinstance(chunks, (list, tuple)):
209 chunks = list(chunks)
--> 210 return ''.join(chunks)
211
212 def iterencode(self, o, _one_shot=False):
UnicodeDecodeError: 'ascii' codec can't decode byte 0xc2 in position 19900: ordinal not in range(128)
```
I've tried using encoder options of various kinds when importing the CSV, but nothing seems to work. Running Pandas '0.15.0-20-g2737f5a' on OS X 10.10 and IPython '3.0.0-dev' built from master.
[Here is the spreadsheet in question](http://d.pr/f/12i3G) incase it helps.
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| null | 3 | 2014-10-25T20:23:37Z | 2014-10-26T10:04:48Z | 2014-10-26T10:04:48Z | NONE | null | I have this dataset
```
datetime,CGI,numRecords,countryCode
2014-03-30 01:00:00,222-01-00149-19464,1,39
2014-03-30 02:00:00,222-01-00149-19464,3,39
2014-03-30 03:00:00,222-01-00149-19464,1,39
```
I have this code
```
import pandas as pd
import numpy as np
calls_df = pd.read_csv('222-01-00149-19464.csv', sep=',', encoding="utf-8-sig")
calls_df.datetime = calls_df.datetime.astype('datetime64[s]')
calls_df = calls_df.set_index(['datetime'], drop=False)
calls_df = calls_df.sort(['datetime'])
calls_df[calls_df.datetime == datetime.datetime(2014, 3, 30, 1)].head()
```
But the result is this:

Why?
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| null | 9 | 2014-10-25T23:43:34Z | 2014-10-27T17:09:52Z | 2014-10-26T19:10:38Z | NONE | null | ``` python
In [120]: pd.Timestamp('2014-10-26 01:01:20').tz_localize('Asia/Irkutsk')
---------------------------------------------------------------------------
AmbiguousTimeError: Cannot infer dst time from Timestamp('2014-10-26 01:01:20'), try using the 'ambiguous' argument
# ok
In [121]: pd.Timestamp('2014-10-26 01:01:20').tz_localize('Asia/Irkutsk',ambiguous=pd.NaT)
Out[121]: Timestamp('2014-10-26 01:01:20+0900', tz='Asia/Irkutsk')
In [129]: x=pd.DataFrame([1],index=[pd.Timestamp('2014-10-26 01:01:20')])
...: x.index
Out[129]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2014-10-26 01:01:20]
Length: 1, Freq: None, Timezone: None
# so this should work?
In [130]: x.index.tz_localize('Asia/Irkutsk',ambiguous=pd.NaT)
AmbiguousTimeError: Cannot infer dst time from Timestamp('2014-10-26 01:01:20'), try using the 'ambiguous' argument
```
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| null | 3 | 2014-10-26T05:31:40Z | 2014-10-29T02:31:53Z | 2014-10-29T02:31:53Z | NONE | null | Hey I'm having some weird behavior in the notebook... I just hacked together some code that supposed to poll a temp/humidity sensor 60 times and then plot the temp against time. Instead of getting a nicely formatted x axis I'm just getting a straight line, and then all the dates from 2010 to 2016 as the x axis. Any ideas? Here's a screencap.

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} | 19 | 2014-10-26T19:39:28Z | 2015-06-02T19:25:19Z | 2015-05-15T20:26:36Z | CONTRIBUTOR | null | #
## ERROR: test_string_index_alias_tz_aware (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
Traceback (most recent call last):
File "/var/tmp/portage/dev-python/pandas-0.15.0/work/pandas-0.15.0-python3_3/lib/pandas/tseries/tests/test_timezones.py", line 606, in test_string_index_alias_tz_aware
self.assertAlmostEqual(result, ts[2])
File "/usr/lib64/python3.3/unittest/case.py", line 665, in assertAlmostEqual
if first == second:
File "/var/tmp/portage/dev-python/pandas-0.15.0/work/pandas-0.15.0-python3_3/lib/pandas/core/generic.py", line 692, in **nonzero**
.format(self.**class**.**name**))
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
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} | 61 | 2014-10-26T21:35:23Z | 2021-04-11T05:23:09Z | null | CONTRIBUTOR | null | *update for 2019-10-07: We have a StringDtype extension dtype. It's memory model is the same as the old implementation, an object-dtype ndarray of strings. The next step is to store & process it natively*.
---
xref #8627
xref #8643, #8350
Since we introduced `Categorical` in 0.15.0, I think we have found 2 main uses.
1) as a 'real' Categorical/Factor type to represent a limited of subset of values that the column can take on
2) as a memory saving representation for object dtypes.
I could see introducting a `dtype='string'` where `String` is a slightly specialized sub-class of `Categroical`, with 2 differences compared to a 'regular' Categorical:
- it allows unions of arbitrary other string types, currently `Categorical` will complain if you do this:
```
In [1]: df = DataFrame({'A' : Series(list('abc'),dtype='category')})
In [2]: df2 = DataFrame({'A' : Series(list('abd'),dtype='category')})
In [3]: pd.concat([df,df2])
ValueError: incompatible levels in categorical block merge
```
Note that this works if they are `Series` (and prob should raise as well, side -issue)
But, if these were both 'string' dtypes, then its a simple matter to combine (efficiently).
- you can restrict the 'sub-dtype' (e.g. the dtype of the categories) to `string/unicode` (iow, don't allow numbers / arbitrary objects), makes the constructor a bit simpler, but more importantly, you now have a 'real' non-object string dtype.
I don't think this would be that complicated to do. The big change here would be to essentially convert any object dtypes that are strings to `dtype='string'` e.g. on reading/conversion/etc. might be a perf issue for some things, but I think the memory savings greatly outweigh.
We would then have a 'real' looking object dtype (and `object` would be relegated to actual python object types, so would be used much less).
cc @shoyer
cc @JanSchulz
cc @jorisvandenbossche
cc @mwiebe
thoughts?
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} | 2 | 2014-10-26T21:37:29Z | 2014-11-09T22:10:39Z | 2014-11-09T22:10:39Z | CONTRIBUTOR | null | xref #8640
As this is inconcistent with how DataFrame concat's work (its a different code-path), so needs updating
```
s = Series(list('abc'),dtype='category')
s2 = Series(list('abd'),dtype='category')
# this should raise
pd.concat([s,s2])
# this should be a category
pd.concat([s,s])
```
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| null | 2 | 2014-10-26T22:18:22Z | 2014-10-26T22:26:33Z | 2014-10-26T22:26:28Z | NONE | null | I'm trying to remove the duplicates contained in this following text file:
https://www.dropbox.com/s/mx3zqot352gdrhq/hg19.txt?dl=0
After reading this into a dataframe, and calling drop_duplicates, the dataframe still has the same number of rows. Though clearly, there are many duplicates contained in the file.
I am using a Mac with 8 gigs of RAM. I'm not sure why this is occurring.
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} | 4 | 2014-10-26T23:45:41Z | 2016-11-11T11:13:28Z | 2016-11-11T11:13:28Z | CONTRIBUTOR | null | this should actually be relatively straightforward.
Incorporating this library: https://github.com/libdydn/dynd-python
as an optional dep, allows native Integer NA support (implemented
very similarly to how NaT is represented in `datetime64[ns]` dtypes ATM.
`dynd-python` are the python bindings for `libdynd` and have an almost 100% numpy
compatibility (they use the buffer interface so should be perf-wise pretty costless to do this).
tangentially related to #8350
cc @mwiebe
cc @shoyer
cc @jorisvandenbossche
cc @cpcloud
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} | 0 | 2014-10-27T00:57:48Z | 2014-10-30T11:20:23Z | 2014-10-27T12:13:31Z | CONTRIBUTOR | null | closes #8607
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} | 2 | 2014-10-27T01:56:30Z | 2014-10-30T11:19:44Z | 2014-10-29T01:12:58Z | CONTRIBUTOR | null | The `pandas.concat` routine's behaviour is slightly at odds with its documentation (and error messages). The documentation states that the first parameter (objs) accepts "list or dict of Series, DataFrame, ..." but the routine is rather more forgiving and appears to accept lists, tuples, dicts, and generator objects (very useful!); hence my impression (during usage) was that it accepted "iterables" generally. Unfortunately it turns out this isn't the case; attempting to concatenate a deque of DataFrame objects results in the following:
``` python
from collections import deque
import pandas as pd
df = pd.DataFrame.from_dict({'a': [1, 2, 3], 'b': [4, 5, 6]})
d = deque((df, df, df))
pd.concat(d)
```
```
----> 1 pd.concat(d)
/home/dave/arcticenv/lib/python3.4/site-packages/pandas/tools/merge.py in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)
720 keys=keys, levels=levels, names=names,
721 verify_integrity=verify_integrity,
--> 722 copy=copy)
723 return op.get_result()
724
/home/dave/arcticenv/lib/python3.4/site-packages/pandas/tools/merge.py in __init__(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy)
735 raise TypeError('first argument must be a list-like of pandas '
736 'objects, you passed an object of type '
--> 737 '"{0}"'.format(type(objs).__name__))
738
739 if join == 'outer':
TypeError: first argument must be a list-like of pandas objects, you passed an object of type "deque"
```
The error message states that the first argument "must be a list-like of pandas objects" (which is slightly different to the documentation, and closer to the actual implementation's behaviour). Given that a deque is iterable but not indexable (similar to a generator expression) it seems to fulfil the criteria of being "list-like".
Digging into the implementation, the test that's failing appears to be the first line of `_Concatenator.__init__` in `pandas.tools.merge` which reads as follows (in my installation):
``` python
if not isinstance(objs, (list,tuple,types.GeneratorType,dict,TextFileReader)):
raise TypeError('first argument must be a list-like of pandas '
'objects, you passed an object of type '
'"{0}"'.format(type(objs).__name__))
```
So it appears the actual set of iterables accepted by `pandas.concat` is lists, tuples, generator expressions, dicts, and instances of `TextFileReader`. I suggest that it might be better to check for (and act upon) special cases and otherwise assume that `objs` is a suitable iterable of `DataFrame` objects. In other words, get rid of that check entirely, and add a couple of checks for "expected" special cases (such as a user mistakenly passing a `DataFrame` as objs; there's already a check in place for dicts a bit further on).
The conversion of `objs` to a list-comprehension later on (below `if keys is None`) should raise a `TypeError` in the case that it isn't iterable so the change shouldn't cause much impact (i.e. in the case of a non-iterable passed as `objs`, it'll raise an exception of the same class as the existing code).
If this sounds reasonable, I'm happy to provide a pull request?
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} | 21 | 2014-10-27T02:37:54Z | 2014-12-04T02:46:50Z | 2014-12-04T02:46:50Z | CONTRIBUTOR | null | ```
======================================================================
FAIL: test_precise_conversion (pandas.io.tests.test_parsers.TestCParserLowMemory)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.15.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_parsers.py", line 3204, in test_precise_conversion
self.assertTrue(sum(precise_errors) <= sum(normal_errors))
AssertionError: False is not true
```
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} | 13 | 2014-10-27T02:41:14Z | 2014-12-05T13:45:38Z | 2014-12-05T13:45:38Z | CONTRIBUTOR | null | ```
======================================================================
FAIL: test_boxplot (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.15.0/debian/tmp/usr/lib/python3/dist-packages/pandas/tests/test_graphics.py", line 1821, in test_boxplot
subplots=True, logy=True)
File "/usr/lib/python3.4/contextlib.py", line 66, in __exit__
next(self.gen)
File "/tmp/buildd/pandas-0.15.0/debian/tmp/usr/lib/python3/dist-packages/pandas/util/testing.py", line 1627, in assert_produces_warning
% extra_warnings)
AssertionError: Caused unexpected warning(s): ['DeprecationWarning'].
```
```
python3.4 ci/print_versions.py; \
cd build/; xvfb-run -a -s "-screen 0 1280x1024x24 -noreset" \
python3.4 /usr/bin/nosetests -s -v -A 'not network and not disabled' --exclude test_bar_log pandas;
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.0-4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: C
LANG: C
pandas: 0.15.0
nose: 1.3.4
Cython: 0.20.1post0
numpy: 1.8.2
scipy: 0.14.0
statsmodels: None
IPython: None
sphinx: 1.2.3
patsy: None
dateutil: 2.0
pytz: 2012c
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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```
======================================================================
ERROR: test_hist_df (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.15.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_graphics.py", line 2061, in test_hist_df
axes = df.plot(kind='hist', rot=50, fontsize=8, orientation='horizontal')
File "/tmp/buildd/pandas-0.15.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tools/plotting.py", line 2453, in plot_frame
**kwds)
File "/tmp/buildd/pandas-0.15.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tools/plotting.py", line 2293, in _plot
plot_obj.generate()
File "/tmp/buildd/pandas-0.15.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tools/plotting.py", line 920, in generate
self._make_plot()
File "/tmp/buildd/pandas-0.15.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tools/plotting.py", line 1949, in _make_plot
artists = plotf(ax, y, column_num=i, **kwds)
File "/tmp/buildd/pandas-0.15.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tools/plotting.py", line 1929, in plotf
bottom=bottom, **kwds)
File "/usr/lib/pymodules/python2.7/matplotlib/axes.py", line 8180, in hist
color=c, bottom=bottom)
TypeError: barh() got multiple values for keyword argument 'bottom'
```
```
NSTALLED VERSIONS
------------------
commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.0-4-amd64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: C
pandas: 0.15.0
nose: 1.3.0
Cython: 0.19
numpy: 1.7.1
scipy: 0.12.0
statsmodels: 0.5.0
IPython: None
sphinx: 1.1.3
patsy: 0.3.0
dateutil: 1.5
pytz: 2012c
bottleneck: None
tables: 2.4.0
numexpr: 2.0.1
matplotlib: 1.2.1
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.4
xlsxwriter: None
lxml: None
bs4: 4.2.0
html5lib: 0.95-dev
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/8649 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8649/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8649/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8649/events | https://github.com/pandas-dev/pandas/issues/8649 | 46,865,110 | MDU6SXNzdWU0Njg2NTExMA== | 8,649 | UnboundLocalError: local variable 'store' referenced before assignment | {
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} | 1 | 2014-10-27T02:45:30Z | 2014-10-30T11:19:44Z | 2014-10-27T12:37:35Z | CONTRIBUTOR | null | in ensure_clean_store on ubuntu 13.10 triggered by many tests, e.g.
```
======================================================================
ERROR: test_unicode_index (pandas.io.tests.test_pytables.TestHDFStore)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.15.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_pytables.py", line 4506, in test_unicode_index
compat_assert_produces_warning(PerformanceWarning,f)
File "/tmp/buildd/pandas-0.15.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_pytables.py", line 121, in compat_assert_produces_warning
f()
File "/tmp/buildd/pandas-0.15.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_pytables.py", line 4504, in f
self._check_roundtrip(s, tm.assert_series_equal)
File "/tmp/buildd/pandas-0.15.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_pytables.py", line 4122, in _check_roundtrip
with ensure_clean_store(self.path, 'w', **options) as store:
File "/usr/lib/python2.7/contextlib.py", line 17, in __enter__
return self.gen.next()
File "/tmp/buildd/pandas-0.15.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_pytables.py", line 77, in ensure_clean_store
safe_close(store)
UnboundLocalError: local variable 'store' referenced before assignment
======================================================================
ERROR: test_unimplemented_dtypes_table_columns (pandas.io.tests.test_pytables.TestHDFStore)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.15.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_pytables.py", line 1949, in test_unimplemented_dtypes_table
_columns
with ensure_clean_store(self.path) as store:
File "/usr/lib/python2.7/contextlib.py", line 17, in __enter__
return self.gen.next()
File "/tmp/buildd/pandas-0.15.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_pytables.py", line 77, in ensure_clean_store
safe_close(store)
UnboundLocalError: local variable 'store' referenced before assignment
======================================================================
ERROR: test_versioning (pandas.io.tests.test_pytables.TestHDFStore)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.15.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_pytables.py", line 416, in test_versioning
with ensure_clean_store(self.path) as store:
File "/usr/lib/python2.7/contextlib.py", line 17, in __enter__
return self.gen.next()
File "/tmp/buildd/pandas-0.15.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_pytables.py", line 77, in ensure_clean_store
safe_close(store)
UnboundLocalError: local variable 'store' referenced before assignment
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.0-4-amd64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: C
pandas: 0.15.0
nose: 1.3.0
Cython: 0.19
numpy: 1.7.1
scipy: 0.12.0
statsmodels: 0.5.0
IPython: None
sphinx: 1.1.3
patsy: 0.3.0
dateutil: 1.5
pytz: 2012c
bottleneck: None
tables: 2.4.0
numexpr: 2.0.1
matplotlib: 1.2.1
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.4
xlsxwriter: None
lxml: None
bs4: 4.2.0
html5lib: 0.95-dev
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/8650 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8650/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8650/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8650/events | https://github.com/pandas-dev/pandas/issues/8650 | 46,865,188 | MDU6SXNzdWU0Njg2NTE4OA== | 8,650 | AttributeError: 'module' object has no attribute 'open_file' | {
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} | 2 | 2014-10-27T02:47:33Z | 2014-10-30T11:19:44Z | 2014-10-27T12:37:35Z | CONTRIBUTOR | null | ```
======================================================================
ERROR: test_frame_select_complex2 (pandas.io.tests.test_pytables.TestHDFStore)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.15.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_pytables.py", line 3686, in test_frame_select_complex2
parms.to_hdf(pp,'df',mode='w',format='table',data_columns=['A'])
File "/tmp/buildd/pandas-0.15.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/core/generic.py", line 896, in to_hdf
return pytables.to_hdf(path_or_buf, key, self, **kwargs)
File "/tmp/buildd/pandas-0.15.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/pytables.py", line 293, in to_hdf
complib=complib) as store:
File "/usr/lib/python2.7/contextlib.py", line 17, in __enter__
return self.gen.next()
File "/tmp/buildd/pandas-0.15.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/pytables.py", line 274, in get_store
store = HDFStore(path, **kwargs)
File "/tmp/buildd/pandas-0.15.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/pytables.py", line 423, in __init__
self.open(mode=mode, **kwargs)
File "/tmp/buildd/pandas-0.15.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/pytables.py", line 553, in open
self._handle = tables.open_file(self._path, self._mode, **kwargs)
AttributeError: 'module' object has no attribute 'open_file'
```
on the same old ubuntu 13.10
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.0-4-amd64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: C
pandas: 0.15.0
nose: 1.3.0
Cython: 0.19
numpy: 1.7.1
scipy: 0.12.0
statsmodels: 0.5.0
IPython: None
sphinx: 1.1.3
patsy: 0.3.0
dateutil: 1.5
pytz: 2012c
bottleneck: None
tables: 2.4.0
numexpr: 2.0.1
matplotlib: 1.2.1
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.4
xlsxwriter: None
lxml: None
bs4: 4.2.0
html5lib: 0.95-dev
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 0 | 2014-10-27T12:09:24Z | 2014-10-30T11:20:23Z | 2014-10-27T12:37:35Z | CONTRIBUTOR | null | closes #8649
closes #8650
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} | 0 | 2014-10-27T13:14:02Z | 2014-10-30T11:20:23Z | 2014-10-28T00:03:02Z | CONTRIBUTOR | null | BUG: coerce Categorical in record array creation (GH8626)
BUG: Categorical not created properly with to_frame() from Series (GH8626)
BUG: handle astype with passed pd.Categorical (GH8626)
closes #8626
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} | 5 | 2014-10-27T16:44:21Z | 2014-11-06T12:47:56Z | 2014-11-06T12:44:42Z | NONE | null | Hi all, I'm experiencing an exception when calling `resample('D', how='mean')` on a Series or Dataframe whose index (datetime-based) ends just on a DST change date.
As a workaround, I'm currently try/except-ing the ValueError and eventually adding a np.nan datapoint at the end of the series (effectively "walking over" the DST change date). At that point I call `resample` which completes correctly and filters away the np.nan datapoint anyway.
Snippet [1] reproduces the bug, calling `resample` on a short series which ends on Oct 26th, which was the DST change date for Europe/Rome. Raised exception is [2]. If relevant, [3] shows the installed versions info.
[1]
``` python
import datetime
import pytz as tz
import pandas as pd
rome = tz.timezone('Europe/Rome')
dr = []
for i in range(2):
dp = datetime.datetime(2014, 10, 25) + datetime.timedelta(days=i)
dr.append(rome.localize(dp))
series = {}
for i, ddr in enumerate(dr):
series[ddr] = i * 10
s1 = pd.Series(series)
s1 = s1.resample('D', how='mean')
```
[2]
```
File "resample_this.py", line 18, in <module>
s1 = s1.resample('D', how='mean')
File "pandas/core/generic.py", line 2980, in resample
return sampler.resample(self).__finalize__(self)
File "pandas/tseries/resample.py", line 85, in resample
rs = self._resample_timestamps()
File "pandas/tseries/resample.py", line 314, in _resample_timestamps
result = grouped.aggregate(self._agg_method)
File "pandas/core/groupby.py", line 2265, in aggregate
return getattr(self, func_or_funcs)(*args, **kwargs)
File "pandas/core/groupby.py", line 693, in mean
return self._python_agg_general(f)
File "pandas/core/groupby.py", line 1125, in _python_agg_general
return self._wrap_aggregated_output(output)
File "pandas/core/groupby.py", line 2332, in _wrap_aggregated_output
return Series(output, index=index, name=name)
File "pandas/core/series.py", line 212, in __init__
data = SingleBlockManager(data, index, fastpath=True)
File "pandas/core/internals.py", line 3326, in __init__
ndim=1, fastpath=True)
File "pandas/core/internals.py", line 2060, in make_block
placement=placement)
File "pandas/core/internals.py", line 75, in __init__
len(self.values), len(self.mgr_locs)))
ValueError: Wrong number of items passed 2, placement implies 1
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Darwin
OS-release: 13.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.15.0
nose: None
Cython: None
numpy: 1.9.0
scipy: None
statsmodels: None
IPython: None
sphinx: 1.2.3
patsy: None
dateutil: 1.5
pytz: 2014.7
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: 2.1.1
xlrd: None
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.4.0
bs4: 4.3.2
html5lib: None
httplib2: 0.9
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 6 | 2014-10-27T21:50:58Z | 2014-10-30T11:20:23Z | 2014-10-28T11:02:41Z | MEMBER | null | Closes #8614
- this adds a warning that you now have to use `to_pydatetime` as direct plotting with a DatetimeIndex does not work anymore (see #8614)
- I also removed for now the note on speed and explanation of the registered formatters. @agijsberts could you shed some light on this?
- "_The speed up for large data sets only applies to pandas 0.14.0 and later._" Why only for pandas 0.14 or later? And from where does this speed-up come from?
- "_thereby extending date and time support to practically all plot types available in matplotlib_" -> but if you plot directly with matplotlib, I think you don't use the pandas registered formatters? So that sentence seems not fully correct, is that possible? And isn't that the reason for the possible speed-up (matplotlib defaults formatter being faster as pandas' formatter)?
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| null | 6 | 2014-10-27T23:24:58Z | 2014-10-28T11:23:00Z | 2014-10-28T00:37:54Z | NONE | null | Hi,
Is this a known issue?
Time offset on 22nd of Sept should be -4 hours in New York as below:
```
pandas.Timestamp('2014-09-22 09:30:00-0400', tz='EST5EDT').tz_convert('EST5EDT')
Timestamp('2014-09-22 09:30:00-0400', tz='EST5EDT')
```
but when providing this twice (-0400 and EST4EDT), the result becomes -0500:
```
pandas.Timestamp('2014-09-22 09:30:00-0400', tz='EST5EDT')
Timestamp('2014-09-22 08:30:00-0500', tz='EST5EDT')
```
Thanks,
Mark
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-37-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.14.1
nose: 1.3.0
Cython: 0.19.2
numpy: 1.8.1
scipy: 0.13.2
statsmodels: 0.5.0
IPython: 2.0.0
sphinx: None
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.2
bottleneck: 0.8.0
tables: 3.0.0
numexpr: 2.3
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: 0.8
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
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| null | 6 | 2014-10-28T03:52:12Z | 2014-10-28T15:32:31Z | 2014-10-28T15:32:31Z | NONE | null | Folks, when using a concat() in an SQL query, the column header in pandas comes back as the full concat statement.
Not sure if this is sensible, but I thought noteworthy.
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} | 6 | 2014-10-28T13:21:48Z | 2014-11-02T19:15:27Z | 2014-11-02T19:15:27Z | CONTRIBUTOR | null | Reproduction steps:
``` python
In [1]: cat = pd.Categorical([1,2,3])
In [2]: cat > cat[0]
Out[2]: array([False, True, True], dtype=bool)
In [3]: cat[0] < cat
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-5c4bb0678680> in <module>()
----> 1 cat[0] < cat
/home/dshpektorov/sources/pandas/pandas/core/categorical.pyc in f(self, other)
52 msg = "Cannot compare a Categorical for op {op} with type {typ}. If you want to \n" \
53 "compare values, use 'np.asarray(cat) <op> other'."
---> 54 raise TypeError(msg.format(op=op,typ=type(other)))
55
56 f.__name__ = op
TypeError: Cannot compare a Categorical for op __gt__ with type <type 'numpy.ndarray'>. If you want to compare values, use 'np.asarray(cat) <op> other'.
```
The problem is that when `cat[0]` is the first operand (`type == np.int64`) it orchestrates the execution and the first step is casting it to a rank-0 array (it becomes `array(1)` rather than `np.int64(1)`) and at that point it ceases to pass `np.isscalar` check and corresponding branch of execution is skipped.
The fix is trivial, feel free to beat me to it.
On a broader scale, we might want to review all uses of `np.isscalar` and see if they can also benefit from accepting rank-0 arrays. If that's true, we might want to add `pd.isscalar` function and use it everywhere instead.
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| null | 6 | 2014-10-28T14:42:51Z | 2020-11-19T21:13:15Z | 2020-11-19T21:13:14Z | NONE | null | Catchall for `rolling_*` issues:
- [x] #12537 exclude nuiscance columns
- [ ] #12536 fully support timedeltas
- [ ] #4130 (return type of `rolling_apply`)
- [x] #3185 (stop using `np.apply_along_axis`)
- [x] #5071, #12950 (pass frames, operate 2-D)
- [x] #9413 `rolling_quantile`
- [ ] #9481 `rolling_idxmax`
- [ ] #10759 rolling_qcut
- [ ] #11446, #20773 casting in `.apply`
- [x] #12595 casting in all window functions (min/max are good)
- [x] #10702 namespacing (though prob just create a `Rolling` object), #11603
- [x] #12535 testing of datetimelike with nans
- [x] #12541 count should be integer dtype (and deal with infs)
- [ ] #15095 Table-wise
---
Hi all,
I intended to apply a function that gives on each day a ranking based on means calculated from previous n-day's data. The natural way is to use pd.rolling_apply. A toy example:
```
In [93]: df = pd.DataFrame(np.random.randint(10, size=20).reshape(4, 5))
In [94]: df
Out[94]:
0 1 2 3 4
0 2 0 0 2 0
1 9 5 5 6 1
2 2 3 6 8 8
3 5 1 2 9 0
In [95]: import bottleneck as bn
In [96]: bn.nanrankdata(df.mean())
Out[96]: array([ 4. , 1.5, 3. , 5. , 1.5])
```
Up to now, it is cool. Then:
```
In [97]: pd.rolling_apply(df, 2, lambda x: bn.nanrankdata(bn.nanmean(x, axis=0)))
Out[97]:
0 1 2 3 4
0 NaN NaN NaN NaN NaN
1 1 1 1 1 1
2 1 1 1 1 1
3 1 1 1 1 1
```
This is clearly wrong. Is this a bug?
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| null | 8 | 2014-10-28T15:17:25Z | 2014-10-31T11:10:47Z | 2014-10-30T10:55:49Z | NONE | null | Hello. I tried to apply a function to a dataframe and it changes the column types. See this example: http://nbviewer.ipython.org/urls/dl.dropboxusercontent.com/u/1118905/Calls-bug.ipynb
Am I doing something wrong? Is it a bug?
pandas 0.15.0
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} | 5 | 2014-10-28T16:35:15Z | 2014-11-27T02:52:38Z | 2014-11-27T02:52:38Z | CONTRIBUTOR | null | given this input file with linefeeds indicated by <lf>
```
skip1<lf>
skip2<lf>
0 1 2<lf>
3 4 5<lf>
```
reading with read_csv() in pandas 0.15.0-42-g20be789 and python 3.4.2 works
```
df = pd.read_csv('test.txt', skiprows=2, delim_whitespace=True, header=None)
df
0 1 2
0 0 1 2
1 3 4 5
```
If I add a space after skip1 so the skipped lines are
```
skip1 <lf>
skip2<lf>
```
then read_csv() throws an error
`CParserError: Error tokenizing data. C error: Expected 1 fields in line 4, saw 3`
Adding 1 to skiprows
`df = pd.read_csv('test.txt', skiprows=3, delim_whitespace=True, header=None)`
does not throw an exception and gives the expected DataFrame
Reading with skiprows=2 and without header=None does not throw an exception and produces a DataFrame with a multiindex
```
skip2
0 1 2
3 4 5
```
If there is a space after skip2 so the skipped lines are
```
skip1<lf>
skip2 <lf>
```
then
`df = pd.read_csv('test.txt', skiprows=2, delim_whitespace=True, header=None)`
does not throw an exception but it does not include the 0 1 2 row in the DataFrame
If there are spaces after skip1 and skip2 so the skipped lines are
```
skip1 <lf>
skip2 <lf>
```
then
`df = pd.read_csv('test.txt', skiprows=2, delim_whitespace=True, header=None)`
throws the CParserError exception but
`df = pd.read_csv('test.txt', skiprows=3, delim_whitespace=True, header=None)`
does not and returns the expected DataFrame
I would expect skiprows to skip the number of lines specified whether or not there are trailing spaces in those lines.
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} | 7 | 2014-10-28T19:06:14Z | 2020-07-12T12:31:24Z | 2020-07-12T12:31:23Z | NONE | null | I'm grouping rows by column investor id. within apply, when a group has more than 1 row, i'm copying a value in the second row in the group into the a column of the first row in that group. I'm using groupby with apply but the assembled result is incorrect - all rows except row 1 show the same investor B - seems like a bug?
```
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: df=pd.DataFrame({'InvestorID':[6,6,17,17,19,19,30,40],
...: 'Investor':['AAA','AAA','BBB','BBB','CCC','CCC','EEE','FFF'],
...: 'CurrentPeriodEnd':['7/31/2014','5/31/2014','7/31/2014','5/31/2014','7/31/2014','5/31/2014','7/31/2014','7/31/2014'],
...: 'PriorPeriodEnd':np.nan*8})
In [4]: df
Out[4]:
CurrentPeriodEnd Investor InvestorID PriorPeriodEnd
0 7/31/2014 AAA 6 NaN
1 5/31/2014 AAA 6 NaN
2 7/31/2014 BBB 17 NaN
3 5/31/2014 BBB 17 NaN
4 7/31/2014 CCC 19 NaN
5 5/31/2014 CCC 19 NaN
6 7/31/2014 EEE 30 NaN
7 7/31/2014 FFF 40 NaN
```
this function updates the first positional row in a group with values from the second row
where a group has 2 rows or more
```
In [5]: def f(x):
...: if len(x) >1:
...: x.iloc[0,x.columns.get_loc('PriorPeriodEnd')]=x.iloc[1,x.columns.get_loc('CurrentPeriodEnd')]
...: return x.iloc[0]
In [6]: df.groupby(['InvestorID']).apply(f).reset_index(drop=True)
```
this is not an expected result - the investor column should not have repeating BBB values
```
Out[6]:
CurrentPeriodEnd Investor InvestorID PriorPeriodEnd
0 7/31/2014 AAA 6 5/31/2014
1 7/31/2014 BBB 17 5/31/2014
2 7/31/2014 BBB 19 5/31/2014
3 7/31/2014 BBB 30 5/31/2014
4 7/31/2014 BBB 40 5/31/2014
```
the function below copies the first row first before making updates and it works as expected
```
In [7]: def f(x):
...: top_row=pd.Series(x.iloc[0])
...: if len(x) >1:
...: top_row.iloc[x.columns.get_loc('PriorPeriodEnd')]=x.iloc[1,x.columns.get_loc('CurrentPeriodEnd')]
...: return top_row
```
this is what the result should look like
```
In [8]: df.groupby(['InvestorID']).apply(f).reset_index(drop=True)
Out[8]:
CurrentPeriodEnd Investor InvestorID PriorPeriodEnd
0 7/31/2014 AAA 6 5/31/2014
1 7/31/2014 BBB 17 5/31/2014
2 7/31/2014 CCC 19 5/31/2014
3 7/31/2014 EEE 30 NaN
4 7/31/2014 FFF 40 NaN
```
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} | 14 | 2014-10-28T19:32:07Z | 2014-10-30T11:20:23Z | 2014-10-29T23:45:11Z | CONTRIBUTOR | null | closes #8428.
Adds a flag which when True returns a DataFrame with columns being the index of the lists generated by the string splitting operation. When False, it returns a 1D numpy array, as before. Defaults to false to not break compatibility.
In the case with no splits, returns a single column DataFrame rather than squashing to a Series.
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} | 7 | 2014-10-28T21:49:58Z | 2016-01-04T02:17:59Z | 2016-01-04T02:17:59Z | CONTRIBUTOR | null | I messed up and used `=` instead of `==` in a `query`.
``` python
df = pd.DataFrame({'a': [1, 2, 3], 'b': ['a', 'b', 'c']})
df.query('a=1')
```
That raises a ValueError. But `df` was modified.
``` python
In [15]: df
Out[15]:
a b
0 1 a
1 1 b
2 1 c
```
versions:
```
pandas: 0.15.0-6-g403f38d
bottleneck: None
tables: None
numexpr: 2.3.1
```
Can't look right now.
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| null | 9 | 2014-10-28T22:04:34Z | 2014-10-29T20:53:52Z | 2014-10-29T20:53:52Z | NONE | null | I am seeing a new but consistently repeatable segmentation fault in our application test suite, running CPython 2.7.8, when calling `_uuid_generate_time(_buffer)` from uuid.py (somewhere inside the kombu library).
The salient difference that makes this segfault occur is if I import pandas before the test run. If I don't, all is well. If I import pandas (and do nothing else differently), the segfault occurs later on (after the pandas import completes without problems), when I call `.apply_async()` on a task in celery, leading kombu to generate a UUID.
This seems to be a matter of the pandas import leaving libuuid1 in some broken state. In particular, it's _not_ a matter of multiple concurrent threads at the time of the segfault; there is only ever one thread, as confirmed by tracing via `threading.settrace` (no output) and by inspecting the core in gdb:
```
$ gdb /var/pythonenv/JENKINSBUILD/bin/python ../test/core
GNU gdb (GDB) 7.4.1-debian
Copyright (C) 2012 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law. Type "show copying"
and "show warranty" for details.
This GDB was configured as "i486-linux-gnu".
For bug reporting instructions, please see:
<http://www.gnu.org/software/gdb/bugs/>...
Reading symbols from /var/pythonenv/JENKINSBUILD/bin/python...done.
warning: core file may not match specified executable file.
[New LWP 19262]
warning: Can't read pathname for load map: Input/output error.
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib/i386-linux-gnu/i686/cmov/libthread_db.so.1".
Core was generated by `/var/pythonenv/JENKINSBUILD/bin/python /var/pythonenv/JENKINSBUILD/bin/unit2 di'.
Program terminated with signal 11, Segmentation fault.
#0 0xb75da5ad in rewind () from /lib/i386-linux-gnu/i686/cmov/libc.so.6
(gdb) info threads
Id Target Id Frame
* 1 Thread 0xb75748d0 (LWP 19262) 0xb75da5ad in rewind () from /lib/i386-linux-gnu/i686/cmov/libc.so.6
(gdb) bt
#0 0xb75da5ad in rewind () from /lib/i386-linux-gnu/i686/cmov/libc.so.6
#1 0xb6872dcf in __uuid_generate_time () from /lib/i386-linux-gnu/libuuid.so.1
#2 0xb6873417 in ?? () from /lib/i386-linux-gnu/libuuid.so.1
#3 0xb688e622 in ffi_call_SYSV () at /usr/local/pythonz/build/CPython-2.7.8/Modules/_ctypes/libffi/src/x86/sysv.S:65
#4 0xb688e3eb in ffi_call (cif=cif@entry=0xbfa6c9e8, fn=fn@entry=0xb68734c0 <uuid_generate_time>, rvalue=rvalue@entry=0xbfa6c950, avalue=avalue@entry=0xbfa6c930)
at /usr/local/pythonz/build/CPython-2.7.8/Modules/_ctypes/libffi/src/x86/ffi.c:411
#5 0xb6886baa in _call_function_pointer (argcount=1, resmem=0xbfa6c950, restype=0x0, atypes=0x87bd050, avalues=0xbfa6c930, pProc=0xb68734c0 <uuid_generate_time>, flags=4353)
at /usr/local/pythonz/build/CPython-2.7.8/Modules/_ctypes/callproc.c:836
#6 _ctypes_callproc (pProc=pProc@entry=0xb68734c0 <uuid_generate_time>, argtuple=argtuple@entry=0xb2fbc2ac, flags=4353, argtypes=argtypes@entry=0x0, restype=restype@entry=0xb69533dc, checker=checker@entry=0x0)
at /usr/local/pythonz/build/CPython-2.7.8/Modules/_ctypes/callproc.c:1183
#7 0xb687df4b in PyCFuncPtr_call (self=0xb6941234, inargs=0xb2fbc2ac, kwds=0x0) at /usr/local/pythonz/build/CPython-2.7.8/Modules/_ctypes/_ctypes.c:3965
#8 0x080660f3 in PyObject_Call (func=func@entry=0xb6941234, arg=arg@entry=0xb2fbc2ac, kw=kw@entry=0x0) at Objects/abstract.c:2529
#9 0x080e2517 in do_call (nk=<optimized out>, na=<optimized out>, pp_stack=0xbfa6cbc4, func=0xb6941234) at Python/ceval.c:4251
#10 call_function (oparg=<optimized out>, pp_stack=0xbfa6cbc4) at Python/ceval.c:4056
#11 PyEval_EvalFrameEx (f=f@entry=0xb2f5983c, throwflag=throwflag@entry=0) at Python/ceval.c:2679
```
With `sys.settrace` I've narrowed the fault down to this `_uuid_generate_time` call, the last trace events being:
```
call, /var/pythonenv/JENKINSBUILD/lib/python2.7/site-packages/celery/app/base.py:615
line, /var/pythonenv/JENKINSBUILD/lib/python2.7/site-packages/celery/app/base.py:617
call, /var/pythonenv/JENKINSBUILD/lib/python2.7/site-packages/kombu/common.py:61
line, /var/pythonenv/JENKINSBUILD/lib/python2.7/site-packages/kombu/common.py:62
call, /usr/local/pythonz/pythons/CPython-2.7.8/lib/python2.7/uuid.py:476
line, /usr/local/pythonz/pythons/CPython-2.7.8/lib/python2.7/uuid.py:486
line, /usr/local/pythonz/pythons/CPython-2.7.8/lib/python2.7/uuid.py:489
line, /usr/local/pythonz/pythons/CPython-2.7.8/lib/python2.7/uuid.py:490
line, /usr/local/pythonz/pythons/CPython-2.7.8/lib/python2.7/uuid.py:493
line, /usr/local/pythonz/pythons/CPython-2.7.8/lib/python2.7/uuid.py:495
line, /usr/local/pythonz/pythons/CPython-2.7.8/lib/python2.7/uuid.py:496
line, /usr/local/pythonz/pythons/CPython-2.7.8/lib/python2.7/uuid.py:497
call, /usr/local/pythonz/pythons/CPython-2.7.8/lib/python2.7/uuid.py:457
line, /usr/local/pythonz/pythons/CPython-2.7.8/lib/python2.7/uuid.py:459
call, /usr/local/pythonz/pythons/CPython-2.7.8/lib/python2.7/ctypes/__init__.py:52
line, /usr/local/pythonz/pythons/CPython-2.7.8/lib/python2.7/ctypes/__init__.py:57
line, /usr/local/pythonz/pythons/CPython-2.7.8/lib/python2.7/ctypes/__init__.py:64
line, /usr/local/pythonz/pythons/CPython-2.7.8/lib/python2.7/ctypes/__init__.py:65
line, /usr/local/pythonz/pythons/CPython-2.7.8/lib/python2.7/ctypes/__init__.py:66
line, /usr/local/pythonz/pythons/CPython-2.7.8/lib/python2.7/ctypes/__init__.py:67
return, /usr/local/pythonz/pythons/CPython-2.7.8/lib/python2.7/ctypes/__init__.py:67
line, /usr/local/pythonz/pythons/CPython-2.7.8/lib/python2.7/uuid.py:460
```
and then the segfault.
This is a CPython 2.7.8 built locally using [pythonz](https://github.com/saghul/pythonz) on Debian Wheezy, and with pandas 0.15.0 installed, inside a virtualenv. Some relevant system package version info:
```
libc-bin 2.13-38+deb7u6
libc-dev-bin 2.13-38+deb7u6
libc6:i386 2.13-38+deb7u6
libc6-dev:i386 2.13-38+deb7u6
libc6-i686:i386 2.13-38+deb7u6
libuuid1:i386 2.20.1-5.3
```
Anything else I can provide that's useful?
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| null | 15 | 2014-10-28T22:05:18Z | 2015-01-29T16:55:47Z | 2014-10-30T22:12:47Z | NONE | null | I've downloaded large dataset from http://www.sigkdd.org/kdd-cup-2009-customer-relationship-prediction.
orange_large_train.data.chunk1.zip (52.7 Mbytes)
orange_large_train.data.chunk2.zip (52.7 Mbytes)
orange_large_train.data.chunk3.zip (52.6 Mbytes)
orange_large_train.data.chunk4.zip (52.5 Mbytes)
orange_large_train.data.chunk5.zip (52.6 Mbytes)
Pandas started to crash after I merged together chunks 1 to 4. Tested with pandas 0.15 on Windows and Linux.
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} | 3 | 2014-10-28T22:22:59Z | 2014-11-29T22:03:08Z | 2014-11-29T22:03:08Z | NONE | null | Slicing a timeseries with time results in a TypeError if the timeseries contains over 1,000,000 entries.
```
import pandas as pd
from datetime import time
ser = pd.Series(index=pd.date_range('2000-1-1', periods=1000000, freq=pd.datetools.offsets.Second()))
ser.head(999999)[time(15,0)] # this is fine
ser[time(15,0)] # this fails
```
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} | 6 | 2014-10-28T22:28:44Z | 2014-10-30T17:39:16Z | 2014-10-29T01:12:58Z | CONTRIBUTOR | null | closes #8645
Enhances `pd.concat` to work with any iterable (except specifically undesired ones like pandas objects and strings). A new test is included covering tuples, lists, generator expressions, deques, and custom sequences, and all existing tests still pass. Finally, a "what's new" entry is included (not sure this last is correct - but [pull requests guidelines](https://github.com/pydata/pandas/blob/master/CONTRIBUTING.md) mentioned it)
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} | 4 | 2014-10-29T00:28:27Z | 2014-10-30T11:19:44Z | 2014-10-29T01:31:56Z | NONE | null | Dataframe with 2 columns: A and B. If values in B are larger than values in A - replace those values with values of A. I used to do this by doing df.B[df.B > df.A] = df.A, however recent upgrade of pandas started giving a SettingWithCopyWarning when encountering this chained assignment. Official documentation recommends using .loc.
Okay, I said, and did it through df.loc[df.B > df.A, 'B'] = df.A and it all works fine, unless column B has all values of NaN. Then something weird happens:
``` python
In [1]: df = pd.DataFrame({'A': [1, 2, 3],'B': [np.NaN, np.NaN, np.NaN]})
In [2]: df
Out[2]:
A B
0 1 NaN
1 2 NaN
2 3 NaN
In [3]: df.loc[df.B > df.A, 'B'] = df.A
In [4]: df
Out[4]:
A B
0 1 -9223372036854775808
1 2 -9223372036854775808
2 3 -9223372036854775808
```
Now, if even one of B's elements satisfies the condition (larger than A), then it all works fine:
``` python
In [1]: df = pd.DataFrame({'A': [1, 2, 3],'B': [np.NaN, 4, np.NaN]})
In [2]: df
Out[2]:
A B
0 1 NaN
1 2 4
2 3 NaN
In [3]: df.loc[df.B > df.A, 'B'] = df.A
In [4]: df
Out[4]:
A B
0 1 NaN
1 2 2
2 3 NaN
```
But if none of B's elements satisfy, then all NaNs get replaces with -9223372036854775808:
``` python
In [1]: df = pd.DataFrame({'A':[1,2,3],'B':[np.NaN,1,np.NaN]})
In [2]: df
Out[2]:
A B
0 1 NaN
1 2 1
2 3 NaN
In [3]: df.loc[df.B > df.A, 'B'] = df.A
In [4]: df
Out[4]:
A B
0 1 -9223372036854775808
1 2 1
2 3 -9223372036854775808
```
Am I doing something wrong, or this is a bug?
pandas: 0.15.0
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| null | 2 | 2014-10-29T00:30:54Z | 2014-10-29T00:34:48Z | 2014-10-29T00:34:48Z | CONTRIBUTOR | null | From [this StackOverflow question](http://stackoverflow.com/questions/26620647/replace-values-in-a-dataframe-column-based-on-condition), something strange occurs when assigning to a `.loc` subset, when none of the rows match:
``` python
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, 2, 3],'B': [np.NaN, np.NaN, np.NaN]})
df
Out[6]:
A B
0 1 NaN
1 2 NaN
2 3 NaN
df.dtypes
Out[10]:
A int64
B float64
dtype: object
# This loc shouldn't match any rows
df.loc[df.B > df.A, 'B'] = df.A
df
Out[8]:
A B
0 1 -9223372036854775808
1 2 -9223372036854775808
2 3 -9223372036854775808
# Why has this become int? Is this expected
# behaviour for this assignment
df.dtypes
Out[12]:
A int64
B int64
dtype: object
```
As long as some of the rows in the `.loc` match, this seems to work okay:
``` python
df = pd.DataFrame({'A': [1, 2, 3],'B': [np.NaN, 4, np.NaN]})
df.loc[df.B > df.A, 'B'] = df.A
df
Out[4]:
A B
0 1 NaN
1 2 2
2 3 NaN
```
I don't know why the `B` column gets converted to int64. Is the weird value in the B column the result of the `np.nan` bit pattern being inserted into an `int64` series?
My `pd.show_versions()`:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 30 Stepping 5, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.15.0
nose: 1.3.4
Cython: 0.21
numpy: 1.9.0
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.2.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 1.5
pytz: 2014.7
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.2
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
```
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} | 0 | 2014-10-29T01:10:58Z | 2014-10-30T11:20:24Z | 2014-10-29T01:31:56Z | CONTRIBUTOR | null | closes #8669
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} | 1 | 2014-10-29T03:48:02Z | 2014-10-30T11:20:24Z | 2014-10-29T11:39:26Z | CONTRIBUTOR | null | closes #6961 this was probably fixed by #7907 but just adding an explicit test and release note.
(in a sense, it should really be the 0.15.0 release note, but that release has shipped...)
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| null | 4 | 2014-10-29T09:13:33Z | 2018-07-06T22:10:45Z | 2018-07-06T22:10:45Z | NONE | null | Hi,
I want to truncate (or even drop) an existing table in SQL server. I didn't find any dedicated function in pandas.io.sql. So I tried to create an empty data frame just to call:
```
df1 = pd.DataFrame()
df1.to_sql(out_tbl, engine, if_exists='replace', index=False)
```
This code does drop the table but then generates exception from sqlalchemy as it tries to re-create an empty table. I can catch and ignore it, and the next call to_sql() with if_exists='append' will create the table correctly, but this is pretty ugly.
Another way I tried is to clear all the rows from the original data frame, leaving the columns:
```
df1 = df.drop(df.index)
df1.to_sql(out_tbl, engine, if_exists='replace', index=False)
```
This almost works: it truncates the table but then insert a single record with all fields as NULL...
You can see the full description at http://stackoverflow.com/questions/26205057/how-can-i-truncate-a-table-using-pandas#comment41130846_26205057
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} | 1 | 2014-10-29T16:29:47Z | 2016-12-30T09:39:39Z | 2016-12-30T09:39:39Z | NONE | null | It looks like when you do a binary operation with a numpy array and a pandas series, the Pandas series is cast as the same type as the numpy array. In the case where the array is `ints` but the series is `floats` this can have odd results in Python 2.x with division. This was surprising to me because, element-wise, a float/int computation would upcast the denominator to a float. But not here in this vectorized case:
```
import numpy as np
import pandas as pd
j = np.array([0] * 5)
k = np.random.randn(5)
print j / k
print (j / pd.Series(k)).values
print (pd.Series(j) / k).values
print (pd.Series(j) / pd.Series(k)).values
####output####
[-0. 0. -0. -0. 0.]
[ inf inf inf inf inf]
[-0. 0. -0. -0. 0.]
[-0. 0. -0. -0. 0.]
```
So in the two "pure" cases (array and array, series and series) the ints are upcast to floats and we get 0's everywhere. In the 2nd mixed case (3rd case overall), everything gets converted to floats to match the array and everything goes fine. But in the 1st mixed case (2nd case overall) everything gets converted to ints to match the array and so you get what was, at least to me, an unexpected infinite result.
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} | 0 | 2014-10-29T22:45:27Z | 2016-02-12T17:40:27Z | 2014-10-29T22:56:36Z | CONTRIBUTOR | null | xref #8669
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} | 10 | 2014-10-29T23:01:54Z | 2014-12-13T21:52:27Z | 2014-10-30T11:19:17Z | CONTRIBUTOR | null | closes #8456
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} | 1 | 2014-10-30T00:08:18Z | 2018-07-08T14:33:38Z | 2018-07-06T22:08:31Z | CONTRIBUTOR | null | The `result_type` argument of `Series.str.split` has a default value of `'series'` for compatibility purposes. This should be deprecated in favor of the default `'frame'`.
See issue #8428 , PR #8663 for introduction of the argument and discussion of default value.
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} | 2 | 2014-10-30T00:24:33Z | 2014-11-27T02:52:38Z | 2014-11-27T02:52:38Z | NONE | null | I'm reading the file available at ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_mlo.txt. The data start on line 73.
If I use the default C engine with `read_table` I have to specify `skiprows=85` to properly load the table:
``` python
pd.read_table(
'co2_mm_mlo.txt.', sep=r'\s+', header=None, skiprows=85, engine='c',
names=['year', 'month', 'dec_year', 'average', 'interpolated', 'trend', 'days'])
```
But if I use the Python engine then the expected `skiprows=72` works:
``` python
pd.read_table(
'co2_mm_mlo.txt.', sep=r'\s+', header=None, skiprows=72, engine='python',
names=['year', 'month', 'dec_year', 'average', 'interpolated', 'trend', 'days'])
```
The resulting DataFrame is expected to have 679 rows, but has 691 rows and data from the header if I use `skiprows=72` with the C engine.
I've confirmed this behavior on Mac OS X Yosemite with Pandas 0.15.0 and a checkout of master@5cf3d85a7d4c448519fa08f918a114209cfbdf2b.
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} | 27 | 2014-10-30T06:15:51Z | 2014-11-06T11:04:00Z | 2014-11-02T22:41:28Z | MEMBER | null | Fixes #7860
The first commit adds `Index.is_monotonic_decreasing` and `Index.is_monotonic_increasing` (alias for `is_monotonic`).
`is_monotonic` will have a performance degradation (still O(n) time) in cases where the Index is decreasing monotonic. If necessary, we could work around this, but I think we can probably get away with this because the fall-back options are much slower and in many cases (e.g., for slice indexing) the next thing we'll want to know is if it's decreasing monotonic, anyways.
Next up will be handling indexing monotonic decreasing indexes with reversed slices, e.g., `s.loc[30:10]`.
CC @jreback @hugadams @immerrr (thank you, specialities wiki!)
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} | 10 | 2014-10-30T07:53:55Z | 2014-12-12T19:40:08Z | 2014-12-12T19:40:08Z | NONE | null | I have a file with over 100Million rows. When I do
pd.read_csv(filename, skiprows=100000000, iterator=True)
python crashes with a memory error. I have 32 gigs of memory and python eats up all that memory!!
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} | 4 | 2014-10-30T14:46:32Z | 2014-10-31T12:50:09Z | 2014-10-31T12:50:09Z | MEMBER | null | Closes #8624
Use .get_values() instead of .values on the block,
this ensures that NonConsolidatable blocks (non-dense
blocks like categorical or sparse) are densified
instead of just returning the raw .values.
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} | 4 | 2014-10-30T15:39:49Z | 2014-11-30T23:21:01Z | 2014-11-30T23:21:01Z | CONTRIBUTOR | null | When running `df.resample('2200L', how='sum', label='right')` with `df`:
```
2014-10-14 23:06:07.440000 6.44000
2014-10-14 23:06:07.761000 5.09600
2014-10-14 23:06:08.215000 6.44000
2014-10-14 23:06:08.486000 6.44000
2014-10-14 23:06:08.509000 5.20800
2014-10-14 23:06:08.789000 4.02842
2014-10-14 23:06:10.795000 5.65600
2014-10-14 23:06:11.618000 6.21600
2014-10-14 23:06:12.177000 6.21600
2014-10-14 23:06:14.620000 5.10720
2014-10-14 23:06:16.698000 5.95840
2014-10-14 23:06:16.745000 6.44000
2014-10-14 23:06:20.548000 6.21600
2014-10-14 23:06:20.549000 6.44000
2014-10-14 23:06:20.551000 5.95840
2014-10-14 23:06:23.206000 6.44000
2014-10-14 23:06:29.977000 6.44000
2014-10-14 23:06:35.307000 5.20800
2014-10-15 23:00:00 NaN
2014-10-15 23:00:02.200000 NaN
Name: spend, dtype: float64
```
I got:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-20-71895ab1ef27> in <module>()
----> 1 R_inst = loaded_sorted.tail(200).head(20).resample('2200L', how='sum', label='right')
/Users/josh/anaconda3/envs/py34/lib/python3.4/site-packages/pandas/core/generic.py in resample(self, rule, how, axis, fill_method, closed, label, convention, kind,
loffset, limit, base)
2978 fill_method=fill_method, convention=convention,
2979 limit=limit, base=base)
-> 2980 return sampler.resample(self).__finalize__(self)
2981
2982 def first(self, offset):
/Users/josh/anaconda3/envs/py34/lib/python3.4/site-packages/pandas/tseries/resample.py in resample(self, obj)
83
84 if isinstance(ax, DatetimeIndex):
---> 85 rs = self._resample_timestamps()
86 elif isinstance(ax, PeriodIndex):
87 offset = to_offset(self.freq)
/Users/josh/anaconda3/envs/py34/lib/python3.4/site-packages/pandas/tseries/resample.py in _resample_timestamps(self, kind)
273 axlabels = self.ax
274
--> 275 self._get_binner_for_resample(kind=kind)
276 grouper = self.grouper
277 binner = self.binner
/Users/josh/anaconda3/envs/py34/lib/python3.4/site-packages/pandas/tseries/resample.py in _get_binner_for_resample(self, kind)
121 kind = self.kind
122 if kind is None or kind == 'timestamp':
--> 123 self.binner, bins, binlabels = self._get_time_bins(ax)
124 elif kind == 'timedelta':
125 self.binner, bins, binlabels = self._get_time_delta_bins(ax)
/Users/josh/anaconda3/envs/py34/lib/python3.4/site-packages/pandas/tseries/resample.py in _get_time_bins(self, ax)
182
183 # general version, knowing nothing about relative frequencies
--> 184 bins = lib.generate_bins_dt64(ax_values, bin_edges, self.closed, hasnans=ax.hasnans)
185
186 if self.closed == 'right':
/Users/josh/anaconda3/envs/py34/lib/python3.4/site-packages/pandas/lib.so in pandas.lib.generate_bins_dt64 (pandas/lib.c:17928)()
ValueError: Values falls after last bin
```
with `Python 3.4.1 :: Anaconda 2.1.0 (x86_64)` and:
```
Cython==0.21
DataShape==0.3.0
Flask==0.10.1
Jinja2==2.7.3
MarkupSafe==0.23
Pillow==2.5.1
PyYAML==3.11
Pygments==1.6
SQLAlchemy==0.9.7
Sphinx==1.2.3
Theano==0.6.0
Werkzeug==0.9.6
XlsxWriter==0.5.7
abstract-rendering==0.5.1
appnope==0.1.0
argcomplete==0.8.1
arrow==0.4.4
astropy==0.4.2
beautifulsoup4==4.3.2
binstar==0.7.1
bitarray==0.8.1
blaze==0.6.3
blz==0.6.2
bokeh==0.6.1
boto==2.34.0
cffi==0.8.6
colorama==0.3.1
configobj==5.0.6
cryptography==0.5.4
cytoolz==0.7.0
decorator==3.4.0
docutils==0.12
future==0.13.1
greenlet==0.4.4
h5py==2.3.1
ipython==2.2.0
itsdangerous==0.24
jdcal==1.0
jedi==0.8.1-final0
## FIXME: could not find svn URL in dependency_links for this package:
joblib==0.8.3-r1
llvmpy==0.12.7
lpsolve55==5.5.2.0
lxml==3.4.0
matplotlib==1.4.0
mock==1.0.1
multipledispatch==0.4.7
networkx==1.9.1
nltk==3.0.0
nose==1.3.4
numba==0.14.0
numexpr==2.3.1
numpy==1.9.0
openpyxl==1.8.5
pandas==0.15.0
parse==1.6.4
patsy==0.3.0
ply==3.4
psutil==2.1.1
psycopg2==2.5.4
py==1.4.25
pyOpenSSL==0.14
pycosat==0.6.1
pycparser==2.10
pycrypto==2.6.1
pycurl==7.19.5
pyflakes==0.8.1
pymc==3.0
pyparsing==2.0.1
pytest==2.6.3
python-dateutil==2.1
pytz==2014.7
pyzmq==14.3.1
qds-sdk==1.2.2
redis==2.9.1
requests==2.4.3
rope-py3k==0.9.4-1
runipy==0.1.1
scikit-image==0.10.1
scikit-learn==0.15.2
scipy==0.14.0
seaborn==0.5.dev
six==1.8.0
sockjs-tornado==1.0.1
spyder==2.3.1
statsmodels==0.5.0
sympy==0.7.5
tables==3.1.1
toolz==0.7.0
tornado==4.0.2
ujson==1.33
xlrd==0.9.3
```
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https://api.github.com/repos/pandas-dev/pandas/issues/8684 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8684/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8684/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8684/events | https://github.com/pandas-dev/pandas/issues/8684 | 47,297,645 | MDU6SXNzdWU0NzI5NzY0NQ== | 8,684 | Indexing a time-series changes the index type | {
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| null | 9 | 2014-10-30T17:02:02Z | 2016-04-29T01:48:11Z | 2016-04-29T01:48:11Z | CONTRIBUTOR | null | The following code changes the index type for my series:
``` python
import arrow
start_time = arrow.get(2014, 10, 15, hour = 19, tzinfo='US/Eastern')
end_time = start_time.replace(seconds=400)
ts = pd.date_range(start=start_time.isoformat(), end=end_time.isoformat(), freq='2200L')
my_series = pd.read_pickle(...)
my_series_sampled = my_series.ix[ts]
```
where the pickle file for `my_series` can be found on [this pickle file](https://drive.google.com/file/d/0B1_X48YFVS7STkh4bnB3ZndRYjg/view?usp=sharing).
More specifically:
For the input `my_series.head()`:
```
2014-10-14 19:00:05.861000-04:00 6.4400
2014-10-14 19:00:07.094000-04:00 6.4400
2014-10-14 19:00:07.109000-04:00 5.9584
2014-10-14 19:00:07.211000-04:00 6.2160
2014-10-14 19:00:07.410000-04:00 6.4400
Name: spend, dtype: float64
```
the output: `my_series_sampled.head()` is:
```
1413414000000000000 NaN
1413414002200000000 NaN
1413414004400000000 NaN
1413414006600000000 NaN
1413414008800000000 NaN
Name: spend, dtype: float64
```
This is with `Python 3.4.1 :: Anaconda 2.1.0 (x86_64)` and:
```
Cython==0.21
DataShape==0.3.0
Flask==0.10.1
Jinja2==2.7.3
MarkupSafe==0.23
Pillow==2.5.1
PyYAML==3.11
Pygments==1.6
SQLAlchemy==0.9.7
Sphinx==1.2.3
Theano==0.6.0
Werkzeug==0.9.6
XlsxWriter==0.5.7
abstract-rendering==0.5.1
appnope==0.1.0
argcomplete==0.8.1
arrow==0.4.4
astropy==0.4.2
beautifulsoup4==4.3.2
binstar==0.7.1
bitarray==0.8.1
blaze==0.6.3
blz==0.6.2
bokeh==0.6.1
boto==2.34.0
cffi==0.8.6
colorama==0.3.1
configobj==5.0.6
cryptography==0.5.4
cytoolz==0.7.0
decorator==3.4.0
docutils==0.12
future==0.13.1
greenlet==0.4.4
h5py==2.3.1
ipython==2.2.0
itsdangerous==0.24
jdcal==1.0
jedi==0.8.1-final0
## FIXME: could not find svn URL in dependency_links for this package:
joblib==0.8.3-r1
llvmpy==0.12.7
lpsolve55==5.5.2.0
lxml==3.4.0
matplotlib==1.4.0
mock==1.0.1
multipledispatch==0.4.7
networkx==1.9.1
nltk==3.0.0
nose==1.3.4
numba==0.14.0
numexpr==2.3.1
numpy==1.9.0
openpyxl==1.8.5
pandas==0.15.0
parse==1.6.4
patsy==0.3.0
ply==3.4
psutil==2.1.1
psycopg2==2.5.4
py==1.4.25
pyOpenSSL==0.14
pycosat==0.6.1
pycparser==2.10
pycrypto==2.6.1
pycurl==7.19.5
pyflakes==0.8.1
pymc==3.0
pyparsing==2.0.1
pytest==2.6.3
python-dateutil==2.1
pytz==2014.7
pyzmq==14.3.1
qds-sdk==1.2.2
redis==2.9.1
requests==2.4.3
rope-py3k==0.9.4-1
runipy==0.1.1
scikit-image==0.10.1
scikit-learn==0.15.2
scipy==0.14.0
seaborn==0.5.dev
six==1.8.0
sockjs-tornado==1.0.1
spyder==2.3.1
statsmodels==0.5.0
sympy==0.7.5
tables==3.1.1
toolz==0.7.0
tornado==4.0.2
ujson==1.33
xlrd==0.9.3
```
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https://api.github.com/repos/pandas-dev/pandas/issues/8685 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8685/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8685/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8685/events | https://github.com/pandas-dev/pandas/issues/8685 | 47,307,337 | MDU6SXNzdWU0NzMwNzMzNw== | 8,685 | v0.15.0 Can't read csv.gz from url | {
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} | 6 | 2014-10-30T18:11:22Z | 2015-08-20T13:25:31Z | 2015-08-20T13:25:31Z | NONE | null | ```
import pandas as pd
pd.read_csv('https://raw.githubusercontent.com/YeoLab/shalek2013/master/expression.csv.gz', compression='gzip', index_col=0)
```
```
---------------------------------------------------------------------------
CParserError Traceback (most recent call last)
<ipython-input-17-2e5c16b1e504> in <module>()
----> 1 pd.read_csv('https://raw.githubusercontent.com/YeoLab/shalek2013/master/expression.csv.gz', compression='gzip', index_col=0)
/usr/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in parser_f(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, na_fvalues, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, float_precision, nrows, iterator, chunksize, verbose, encoding, squeeze, mangle_dupe_cols, tupleize_cols, infer_datetime_format, skip_blank_lines)
461 skip_blank_lines=skip_blank_lines)
462
--> 463 return _read(filepath_or_buffer, kwds)
464
465 parser_f.__name__ = name
/usr/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in _read(filepath_or_buffer, kwds)
237
238 # Create the parser.
--> 239 parser = TextFileReader(filepath_or_buffer, **kwds)
240
241 if (nrows is not None) and (chunksize is not None):
/usr/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in __init__(self, f, engine, **kwds)
551 self.options['has_index_names'] = kwds['has_index_names']
552
--> 553 self._make_engine(self.engine)
554
555 def _get_options_with_defaults(self, engine):
/usr/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in _make_engine(self, engine)
688 def _make_engine(self, engine='c'):
689 if engine == 'c':
--> 690 self._engine = CParserWrapper(self.f, **self.options)
691 else:
692 if engine == 'python':
/usr/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in __init__(self, src, **kwds)
1050 kwds['allow_leading_cols'] = self.index_col is not False
1051
-> 1052 self._reader = _parser.TextReader(src, **kwds)
1053
1054 # XXX
/usr/local/lib/python2.7/site-packages/pandas/parser.so in pandas.parser.TextReader.__cinit__ (pandas/parser.c:4693)()
/usr/local/lib/python2.7/site-packages/pandas/parser.so in pandas.parser.TextReader._get_header (pandas/parser.c:6091)()
/usr/local/lib/python2.7/site-packages/pandas/parser.so in pandas.parser.TextReader._tokenize_rows (pandas/parser.c:8119)()
/usr/local/lib/python2.7/site-packages/pandas/parser.so in pandas.parser.raise_parser_error (pandas/parser.c:20349)()
CParserError: Error tokenizing data. C error: Calling read(nbytes) on source failed. Try engine='python'.
```
But the local file works:

I have v0.15.0
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} | 1 | 2014-10-30T18:51:25Z | 2015-01-13T08:09:37Z | 2015-01-13T08:09:37Z | NONE | null | One pedantic little thing I keep noticing: the docs for .loc say repeatedly that it is "strictly label-based", but it also accepts an array of booleans. I think it's good for .loc to have accept booleans, but it's misleading and confusing for the docs to say (in bold, no less) that it accepts _only_ labels found in the index, and then immediately contradict themselves by saying it also accepts arrays of booleans. Booleans aren't labels found in the index.
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} | 3 | 2014-10-30T20:28:58Z | 2014-11-02T07:44:47Z | 2014-10-31T13:48:48Z | CONTRIBUTOR | null | `pd.Timestamp` is one of types that don't pass `np.isscalar` test previously
used in _cat_compare_op, `pd.lib.isscalar` should be used instead.
``` python
In [1]: cat = pd.Categorical(pd.date_range('2014', periods=5))
In [2]: cat > cat[0]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-2-77883e4783fe> in <module>()
----> 1 cat > cat[0]
/home/dshpektorov/sources/pandas/pandas/core/categorical.py in f(self, other)
52 msg = "Cannot compare a Categorical for op {op} with type {typ}. If you want to \n" \
53 "compare values, use 'np.asarray(cat) <op> other'."
---> 54 raise TypeError(msg.format(op=op,typ=type(other)))
55
56 f.__name__ = op
TypeError: Cannot compare a Categorical for op __gt__ with type <class 'pandas.tslib.Timestamp'>. If you want to
compare values, use 'np.asarray(cat) <op> other'.
```
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} | 10 | 2014-10-30T20:34:21Z | 2015-01-16T15:10:44Z | 2015-01-16T15:10:44Z | CONTRIBUTOR | null | e.g. 64bit big-endian
https://buildd.debian.org/status/fetch.php?pkg=pandas&arch=s390x&ver=0.15.0-1&stamp=1414699796
and others
https://buildd.debian.org/status/package.php?p=pandas
(some have other failures as well, yet to find a moment to "analyze", but e.g. hurd-i386 fails test_precise_conversion)
could/should all stata tests be skipped on big-endians?
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} | 1 | 2014-10-31T00:29:44Z | 2014-10-31T19:04:34Z | 2014-10-31T19:04:34Z | CONTRIBUTOR | null | ```
In [189]: ser = pd.Series([np.datetime64('nat')])
In [190]: ser
Out[190]:
0 NaT
dtype: datetime64[ns]
In [191]: ser.dt.hour
Out[191]:
0 -1
dtype: int64
```
While -1 is not a valid hour it is a very valid integer and could (has) caused hard to track bugs.
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| null | 2 | 2014-10-31T09:26:47Z | 2014-11-01T13:59:17Z | 2014-10-31T12:24:36Z | NONE | null | This is related to the closing of HDFStore stores: <a href="https://github.com/pydata/pandas/issues/4409"> API: HDFStore closing is buggy #4409 </a>, from which I understand that it is possible to have multiple open stores pointing to the same file, and that closing one of the stores does not close all the others, so one can still put things in others.
But what happens if I start off with the following in a session
<pre><code>df = pd.DataFrame(np.arange(10))
store1 = pd.HDFStore('test.h5')
store2 = pd.HDFStore('test.h5')
some_unmemorable_name = pd.HDFStore('test.h5')
</code></pre>
and I do some stuff with these stores, such as
<pre><code>store1.append('df1', df)
store2.append('df2', df)
store1.close()
store2.close()
</code></pre>
then, I spend some time doing a bunch of stuff unrelated to the stores, after which I want to save stuff to 'test.h5' again but to overwrite if it's already there. At this point, I have already forgotten about not having closed <code>some_unmemorable_name</code>. If I open the file with <code>mode = 'w'</code> now,
<pre><code>In [63]: store = pd.HDFStore('test.h5', mode = 'w')
/nuwa_cluster/opt/anaconda3-2.0.1/lib/python3.4/site-packages/tables/file.py in open_file(filename, mode, title, root_uep, filters, **kwargs)
313 raise ValueError(
314 "The file '%s' is already opened. Please "
--> 315 "close it before reopening in write mode." % filename)
316
317 # Finally, create the File instance, and return it
ValueError: The file 'test.h5' is already opened. Please close it before reopening in write mode.
</pre></code>
and there is no way to close the opened store because it was opened so long ago that I cannot recall its name. Deleting the file doesn't help because the current session is still 'aware' of the opened store. Restarting the session means that everything will have to be done again. If there is way to be able to get the names of all currently opened stores, then they can all be closed.
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} | 3 | 2014-10-31T12:47:35Z | 2014-11-05T14:46:49Z | 2014-11-05T14:34:26Z | MEMBER | null | Closes #8614
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} | 2 | 2014-10-31T12:58:26Z | 2019-01-04T07:00:57Z | 2019-01-04T07:00:26Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/26660286/dealing-with-dst-time-with-set-levels-function-multiindex-pandas)
```
In [91]: def works(df):
....: return df.reset_index(level=1,drop=True).set_index(df.index.get_level_values(1).tz_localize('Europe/Paris', ambiguous = 'infer'),append=True).iloc[7151:7160]
....:
In [92]: def breaks(df):
....: return df.set_index(df.index.set_levels(df.index.get_level_values(1).tz_localize('Europe/Paris', ambiguous = 'infer'),level=1)).iloc[7151:7160]
....:
In [93]: array = [('s001', d) for d in pd.date_range(start='01/01/2014', end='01/01/2015', freq='H')] + [('s002', d) for d in pd.date_range(start='01/01/2014', end='01/01/2015', freq='H')]
In [94]: index = pd.MultiIndex.from_tuples(array, names=['sce', 'DATES'])
In [95]: df = pd.DataFrame(np.random.randn(len(index)), index=index)
In [96]: df = df.append(df.query('DATES == "26/10/2014 02:00:00"'))
In [97]: df = df.query('DATES <> "30/03/2014 02:00:00"')
In [98]: df = df.sort()
In [99]: works(df)
Out[99]:
0
sce DATES
s001 2014-10-26 00:00:00+02:00 -0.833819
2014-10-26 01:00:00+02:00 -1.190427
2014-10-26 02:00:00+02:00 -1.210017
2014-10-26 02:00:00+01:00 -1.210017
2014-10-26 03:00:00+01:00 0.763599
2014-10-26 04:00:00+01:00 -1.055695
2014-10-26 05:00:00+01:00 -0.912766
2014-10-26 06:00:00+01:00 0.373625
2014-10-26 07:00:00+01:00 0.631287
In [100]: breaks(df)
Out[100]:
0
sce DATES
s001 2014-10-26 01:00:00+02:00 -0.833819
2014-10-26 02:00:00+02:00 -1.190427
2014-10-26 02:00:00+01:00 -1.210017
2014-10-26 02:00:00+01:00 -1.210017
2014-10-26 03:00:00+01:00 0.763599
2014-10-26 04:00:00+01:00 -1.055695
2014-10-26 05:00:00+01:00 -0.912766
2014-10-26 06:00:00+01:00 0.373625
2014-10-26 07:00:00+01:00 0.631287
```
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} | 3 | 2014-10-31T16:22:55Z | 2014-10-31T20:11:29Z | 2014-10-31T19:04:34Z | CONTRIBUTOR | null | BUG: millisecond Timestamp/DatetimeIndex accessor fixed
closes #8689
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} | 4 | 2014-10-31T17:00:33Z | 2015-01-29T18:21:45Z | 2015-01-29T18:21:45Z | CONTRIBUTOR | null | This results in an API change, so holding off till later
https://github.com/jreback/pandas/tree/milliseconds
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} | 5 | 2014-10-31T17:52:37Z | 2014-11-17T08:31:24Z | 2014-11-17T08:31:12Z | NONE | null | I'm importing data from an Excel spreadsheet and I'd like to generate a CREATE TABLE statement for PostgreSQL that matches the data I've imported from Excel.
Here's what I've got so far:
``` python
>>> import pandas as pd
>>> from sqlalchemy import create_engine
>>> import psycopg2
>>> basedata = pd.read_excel('mydata.xlsx')
>>> engine = create_engine('postgresql://myusername@localhost:5432/mydb')
>>> pd.io.sql.get_schema(basedata, 'basedata', con=engine)
```
Unfortunately the CREATE TABLE statement generated uses a column type of "DATETIME" that does not exist in PostgreSQL. I suspect that the engine object is being ignored somehow and the DDL I'm getting is for sqlite, not PostgreSQL. However, I can't get pandas to build on my Mac so that I can expand the tests to validate my assumption.
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} | 7 | 2014-10-31T19:07:24Z | 2015-06-11T12:48:43Z | 2015-06-11T12:42:48Z | CONTRIBUTOR | null | So this as expected. Though I its pretty straightforward to add support for this directly to a Timedelta, after the notion is the same, _though_ a `time` object can support a time-zone so not sure if this is odd (or maybe we should add an optional tz to Timedelta).
```
In [5]: from datetime import time
In [6]: time(secIn [8]: pd.Timedelta(time(second=1))
ValueError: Value must be Timedelta, string, integer, float, timedelta or convertible
In [9]: pd.Timedelta('1s')
Out[9]: Timedelta('0 days 00:00:01')
```
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https://api.github.com/repos/pandas-dev/pandas/issues/8699 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8699/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8699/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8699/events | https://github.com/pandas-dev/pandas/pull/8699 | 47,436,040 | MDExOlB1bGxSZXF1ZXN0MjM3MDA2ODU= | 8,699 | BUG: incorrect serialization of a CustomBusinessDay in HDF5 (GH8591) | {
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} | 0 | 2014-10-31T19:34:33Z | 2014-10-31T20:18:45Z | 2014-10-31T20:18:44Z | CONTRIBUTOR | null | closes #8591
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https://api.github.com/repos/pandas-dev/pandas/issues/8700 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8700/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8700/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8700/events | https://github.com/pandas-dev/pandas/pull/8700 | 47,446,437 | MDExOlB1bGxSZXF1ZXN0MjM3MDcxODc= | 8,700 | BUG: various Categorical fixes (GH8623) | {
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} | 1 | 2014-10-31T21:29:04Z | 2014-11-01T13:11:51Z | 2014-10-31T22:35:39Z | CONTRIBUTOR | null | BUG: bug in selecting from a Categorical with iloc (GH8623)
BUG: bug in groupby-transform with a Categorical (GH8623)
BUG: bug in duplicated/drop_duplicates with a Categorical (GH8623)
closes #8623
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https://api.github.com/repos/pandas-dev/pandas/issues/8701 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8701/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8701/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8701/events | https://github.com/pandas-dev/pandas/pull/8701 | 47,509,854 | MDExOlB1bGxSZXF1ZXN0MjM3MjEwNDI= | 8,701 | ENH: provide a null_counts keyword to df.info() to force showing of null counts | {
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} | 0 | 2014-11-01T16:27:22Z | 2014-11-02T18:57:21Z | 2014-11-02T18:57:21Z | CONTRIBUTOR | null | Allows run-time control of showing of null-counts with `df.info()` (similar to what we allow with for example memory_usage in that this will override the default options for that call)
```
In [2]: df = DataFrame(1,columns=range(10),index=range(10))
In [3]: df.iloc[1,1] = np.nan
```
Default for a small frame (currently) is to show the non-null counts
```
In [5]: df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 10 entries, 0 to 9
Data columns (total 10 columns):
0 10 non-null float64
1 9 non-null float64
2 10 non-null float64
3 10 non-null float64
4 10 non-null float64
5 10 non-null float64
6 10 non-null float64
7 10 non-null float64
8 10 non-null float64
9 10 non-null float64
dtypes: float64(10)
memory usage: 880.0 bytes
# allow it to be turned off
In [6]: df.info(null_counts=False)
<class 'pandas.core.frame.DataFrame'>
Int64Index: 10 entries, 0 to 9
Data columns (total 10 columns):
0 float64
1 float64
2 float64
3 float64
4 float64
5 float64
6 float64
7 float64
8 float64
9 float64
dtypes: float64(10)
memory usage: 880.0 bytes
```
When you have a big frame, right now you need to set max_info_rows to control this option
```
In [7]: pd.set_option('max_info_rows',5)
In [8]: df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 10 entries, 0 to 9
Data columns (total 10 columns):
0 float64
1 float64
2 float64
3 float64
4 float64
5 float64
6 float64
7 float64
8 float64
9 float64
dtypes: float64(10)
memory usage: 880.0 bytes
# force it to show
In [9]: df.info(null_counts=True)
<class 'pandas.core.frame.DataFrame'>
Int64Index: 10 entries, 0 to 9
Data columns (total 10 columns):
0 10 non-null float64
1 9 non-null float64
2 10 non-null float64
3 10 non-null float64
4 10 non-null float64
5 10 non-null float64
6 10 non-null float64
7 10 non-null float64
8 10 non-null float64
9 10 non-null float64
dtypes: float64(10)
memory usage: 880.0 bytes
```
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