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} | 5 | 2014-11-26T20:47:30Z | 2021-04-11T05:45:26Z | 2021-04-11T05:45:25Z | NONE | null | It seems that when using `concat` coerces `Series` of type `int` to type `float` if one of the `DataFrame`s being concatted is empty:
```
>>> df1 = pd.DataFrame([1],columns=['a'])
>>> df2 = pd.DataFrame(columns=['a'])
>>> df = pd.concat([df1, df2])
>>> df['a'].dtype
dtype('float64')
>>> df1['a'].dtype
dtype('int64')
```
While if both columns have ints, no coercion happens:
```
>>> df1 = pd.DataFrame([1],columns=['a'])
>>> df2 = pd.DataFrame([1],columns=['a'])
>>> df = pd.concat([df1, df2])
>>> df['a'].dtype
dtype('int64')
```
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} | 1 | 2014-11-27T02:48:17Z | 2014-11-27T14:08:28Z | 2014-11-27T02:48:28Z | CONTRIBUTOR | null | Clearly the default is `complete=True` for back-compat
but allows for a match starting at the beginning (has always been like this),
but doesn't require the string to ONLY match the format, IOW, can be extra stuff after the match.
Avoids having to do a regex replace first.
```
In [21]: s = Series(['19MAY11','19MAY11:00:00:00']*100000)
In [22]: %timeit pd.to_datetime(s.str.replace(':\S+$',''),format='%d%b%y')
1 loops, best of 3: 828 ms per loop
In [23]: %timeit pd.to_datetime(s,format='%d%b%y',compete=False)
1 loops, best of 3: 603 ms per loop
```
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} | 17 | 2014-11-27T02:48:55Z | 2014-12-05T14:33:51Z | 2014-12-05T14:32:51Z | CONTRIBUTOR | null | closes #8989
closes #8903
Clearly the default is exact=True for back-compat
but allows for a match starting at the beginning (has always been like this),
but doesn't require the string to ONLY match the format, IOW, can be extra stuff after the match.
Avoids having to do a regex replace first.
```
In [21]: s = Series(['19MAY11','19MAY11:00:00:00']*100000)
In [22]: %timeit pd.to_datetime(s.str.replace(':\S+$',''),format='%d%b%y')
1 loops, best of 3: 828 ms per loop
In [23]: %timeit pd.to_datetime(s,format='%d%b%y',exact=False)
1 loops, best of 3: 603 ms per loop
```
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} | 4 | 2014-11-27T05:24:43Z | 2015-05-01T16:05:38Z | 2015-05-01T16:05:32Z | NONE | null | The plot label appears when plotting a dataframe as a scatter plot but not as a line plot. Is this expected?
``` python
df = pd.DataFrame(np.random.rand(2, 4), columns=['a', 'b', 'c', 'd'])
ax = df.plot(kind = 'scatter', x='a', y='b',color='DarkBlue', label='Label')
ax = df.plot(kind = 'line', x='a', y='b',color='DarkBlue', label='Label')
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Darwin
OS-release: 14.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.1
nose: 1.3.1
Cython: 0.21.1
numpy: 1.9.1
scipy: 0.14.0
statsmodels: None
IPython: 2.3.1
sphinx: 1.2.3
patsy: None
dateutil: 2.2
pytz: 2014.9
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
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/8906 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8906/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8906/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8906/events | https://github.com/pandas-dev/pandas/issues/8906 | 50,289,524 | MDU6SXNzdWU1MDI4OTUyNA== | 8,906 | Partial indexing of a Panel | {
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} | 11 | 2014-11-27T13:06:46Z | 2018-07-06T20:21:06Z | 2017-07-10T21:06:18Z | NONE | null | See also: http://stackoverflow.com/questions/26736745/indexing-a-pandas-panel-counterintuitive-or-a-bug
These are actually two related(?) issues.
The first is that the DataFrame is transposed, when you index the major_indexer or minor_indexer:
```
from pandas import Panel
from numpy import arange
p = Panel(arange(24).reshape(2,3,4))
p.shape
Out[4]: (2, 3, 4)
p.iloc[0].shape # original order
Out[5]: (3, 4)
p.iloc[:,0].shape # I would expect (2,4), but it is transposed
Out[6]: (4, 2)
p.iloc[:,:,0].shape # also transposed
Out[7]: (3, 2)
p.iloc[:,0,:].shape # transposed (same as [6])
Out[8]: (4, 2)
```
This may be a design choice, but it seems counterintuitive to me and it is not in line with the way numpy indexing works.
On a related note, I would expect the following two commands to be equivalent:
```
p.iloc[1:,0,:].shape # Slicing item_indexer, then transpose
Out[9]: (4, 1)
p.iloc[1:,0].shape # Expected to get the same as [9], but slicing minor_indexer instead????
Out[10]: (3, 2)
```
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: nl_NL
pandas: 0.15.1
nose: 1.3.3
Cython: 0.20.1
numpy: 1.9.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.2.0
sphinx: 1.2.2
patsy: 0.2.1
dateutil: 1.5
pytz: 2014.9
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: 0.5.5
lxml: 3.3.5
bs4: 4.3.1
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
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} | 12 | 2014-11-27T14:54:46Z | 2014-11-30T15:11:42Z | 2014-11-29T22:03:08Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/8667
on master:
```
>>> from datetime import time
>>> from pandas.index import _SIZE_CUTOFF
>>> n = _SIZE_CUTOFF + 100
>>> idx = pd.date_range('2014-11-26', periods=n, freq='S')
>>> ts = pd.Series(np.random.randn(n), index=idx)
>>> key = time(15, 0)
>>> ts[key]
TypeError: 'datetime.time' object is not iterable
>>> ts.index.get_loc(key)
TypeError: unorderable types: int() > datetime.time()
```
above would work on master branch if `n` was smaller than `_SIZE_CUTOFF`.
`_SIZE_CUTOFF` is set [here](https://github.com/pydata/pandas/blob/e5fe75e8d40e6b94eabf54882a17ab05341c9929/pandas/index.pyx#L68)
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| null | 0 | 2014-11-27T16:18:12Z | 2021-04-11T05:46:12Z | null | NONE | null | When I use read_csv to read a file which has columns with the same name, the names are amended with .1, .2, etc, when mangle_dupe_cols=True in read_csv.
Wouldn't it make more sense to amend them as _1, _2, etc., so that columns of a dataframe can still be accessed with the dot syntax: df.name_1 works fine, but df.name.1 doesn't work.
Just a suggestion,
Mark
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} | 1 | 2014-11-27T17:38:36Z | 2018-06-18T22:34:30Z | 2018-06-18T22:34:30Z | NONE | null | xref #8832
Parsing of datetime strings ending with `Z0`or `Z00`should raise (this is currently only the case with `Z0`).
This is due to the fact that the parser in `np_datetime_strings.c` cannot raise its own errors since in tslib.pyx, function convert_ts_tsobject(), any ValueError is catched and then a fallback external parser (dateutil) is called.
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| null | 2 | 2014-11-27T18:56:42Z | 2014-11-28T03:26:52Z | 2014-11-28T03:26:52Z | CONTRIBUTOR | null | Here is a function that generates a heatmap:
http://nbviewer.ipython.org/github/herrfz/dataanalysis/blob/master/week3/svd_pca.ipynb
It works with numpy.arrays
Maybe you want to ask the author to contribute this useful function to pandas.
I think, its quite nice.
kind regards
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} | 1 | 2014-11-27T20:07:45Z | 2014-11-28T01:02:25Z | 2014-11-28T01:02:21Z | CONTRIBUTOR | null | xref #8805
Documentation suggests other types of join, but only 'left' is implemented.
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| null | 5 | 2014-11-27T22:52:50Z | 2014-11-28T22:50:25Z | 2014-11-28T00:57:38Z | CONTRIBUTOR | null | Hi,
I want to generate a DataFrame out of some data x0 and an index based some dates t0.
df = pd.DataFrame(x0)
Generates the dataframe containg all data correctly.
df = pd.DataFrame(x0,t0)
Generates a dataframe with the dates as index whereas 'Date' appears as first index and all data is cleared to NaNs.
type(x0)
pandas.core.frame.DataFrame
type(t0)
pandas.core.series.Series
(generated with >t0=pd.to_datetime(pm0['Date']) )
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| null | 8 | 2014-11-27T23:53:11Z | 2021-04-11T05:47:24Z | null | CONTRIBUTOR | null | I am using a DataFrame df with dates as index and some data with missing values.
All dates are from the same year 1999 and the data has some missing vales.
There is one record in the dates that is NaT.
When I use df.interploate().plot() a line is plotted between 1996 and 1716.
When NaT is committed the correct plot appears.
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} | 3 | 2014-11-28T04:41:41Z | 2014-12-03T22:38:40Z | 2014-12-03T22:38:33Z | CONTRIBUTOR | null | I need this function for some other work I have in a branch (to_gbq w/ a new table and to_gbq with staging in gcs). I saw issue #8325 and thought this might be a good intermediate step.
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| null | 9 | 2014-11-28T05:37:15Z | 2014-12-09T23:27:02Z | 2014-12-09T23:26:55Z | MEMBER | null | Fixes #8865 (Timestamp - Timestamp -> Timedelta)
This PR cleans up and extends `Timestamp` arithmetic similarly to my treatment for `Timedelta` in #8884.
It includes a new `to_datetime64()` method, and arithmetic now works between Timestamp and ndarrays. I also ensure comparison operations work properly between all of (Timestamp, Timedelta, NaT) and ndarrays.
Implementation notes: wide use of the `NotImplemented` singleton let me cleanup some complex logic. I also strove to reduce the tight-coupling of `Timestamp`/`Timedelta` to pandas itself by removing use of the `_typ` property in tslib (I honestly don't quite understand why it needs to exist) and by not treating series/index any differently from any other ndarray-like objects.
CC @jreback @jorisvandenbossche @immerrr
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} | 16 | 2014-11-28T11:17:53Z | 2018-10-25T11:46:30Z | 2018-10-25T11:46:30Z | NONE | null | I'm trying to use pandas to speed up timezone conversions on many datetimes and I can't get around `NonExistentTimeErrors`. Pandas `tz_localize()` seems to ignore the `ambiguous` argument for the non existent time case.
Example:
``` python
import pytz
import pandas
tz = pytz.timezone('Europe/Warsaw')
non_existent = datetime.datetime(2015, 3, 29, 2, 30)
tz.normalize(tz.localize(non_existent))
#2015-03-29 03:30:00+02:00
tz.normalize(tz.localize(non_existent, is_dst=False))
#2015-03-29 03:30:00+02:00
pandas.Timestamp(non_existent).tz_localize(tz)
# NonExistentTimeError: 2015-03-29 02:30:00
pandas.Timestamp(non_existent).tz_localize(tz, ambiguous=0)
# NonExistentTimeError: 2015-03-29 02:30:00
```
It would be nice if the `ambiguous` argument worked the same as `is_dst` in pytz. As it is now, it's impossible afaik to reliably localize a series of datetimes using pandas, since any of them might cause a `NonExistentTimeError` and there's no way of guiding pandas what to do with such datetimes.
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-39-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.1
nose: 1.3.4
Cython: None
numpy: 1.9.1
scipy: None
statsmodels: None
IPython: 2.3.1
sphinx: 1.2.2
patsy: None
dateutil: 2.2
pytz: 2013.9
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.9
apiclient: 1.3.1
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: 2.5.4 (dt dec pq3 ext)
```
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| null | 21 | 2014-11-28T22:06:20Z | 2020-08-03T11:44:05Z | 2015-10-05T10:34:22Z | CONTRIBUTOR | null | Having get_dummies() in Pandas is really nice, but to be useful for machine learning, it would need to be usable in a train / test framework (or "fit_transform" and "transform", with the sklearn terminology). Let me know if this needs more explanations.
So, I guess this is a wishlist bug report to add that functionality to Pandas. I can even create a pull request, if people agree this would be something useful to have in Pandas (and are willing to coach a bit and do code review for what would be my first contribution to this project).
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| null | 3 | 2014-11-28T22:38:41Z | 2014-11-29T00:40:43Z | 2014-11-28T22:44:41Z | CONTRIBUTOR | null | pandas.to_datetime() should not return different types.
Otherwise, all output should have methods like .head() and .tail() and .month()
---
import pandas as pd
import numpy as np
pd.options.display.mpl_style = 'default'
import matplotlib.pyplot as plt
x0['Month']=x0['Date'].month
data=np.random.rand(5)
data[4]=NaN
dfdata=pd.DataFrame({'Data':data})
times=['19990103','19990106','19990109','19990112','NaT']
dftimes = pd.to_datetime(times,format='%Y%m%d')
dftimes
> Out[10]:
> class 'pandas.tseries.index.DatetimeIndex'
dftimes.head
> AttributeError Traceback (most recent call last)
> <ipython-input-11-6c85e0098555> in <module>()
> ----> 1 dftimes.head
> AttributeError: 'DatetimeIndex' object has no attribute 'head'
---
'Download files'
import urllib
urllib.urlretrieve ("http://www.epa.gov/ttn/airs/airsaqs/detaildata/501files/Rd_501_88101_1999.Zip", "./data/Rd_501_88101_1999.zip")
'Unzip the files...'
import zipfile
import os.path
zfile = zipfile.ZipFile("./data/Rd_501_88101_1999.zip")
for name in zfile.namelist():
zfile.extract(name,'./data/')
import pandas as pd
import numpy as np
pd.options.display.mpl_style = 'default'
import matplotlib.pyplot as plt
%pylab inline
pm0=pd.io.parsers.read_table("./data/RD_501_88101_1999-0.txt","|",index_col=False,low_memory=False).drop(0)
x0=pm0[['Sample Value']].astype(float)
t0=pd.to_datetime(pm0['Date'],format='%Y%m%d')
print type(t0)
> class 'pandas.core.series.Series'
t0.head()
> 1 1999-01-03
> 2 1999-01-06
> 3 1999-01-09
> 4 1999-01-12
> 5 1999-01-15
> Name: Date, dtype: datetime64[ns]
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| null | 1 | 2014-11-29T12:01:14Z | 2014-12-03T22:40:43Z | 2014-11-29T12:32:55Z | CONTRIBUTOR | null | For consistency with [PEP8](https://www.python.org/dev/peps/pep-0008#id17):
```
Imports are always put at the top of the file, just after any module
comments and docstrings, and before module globals and constants.
```
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Merged via https://github.com/pydata/pandas/commit/ace3c46be0ebf12f0eb4b63f22843d76b67ccfde
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| null | 4 | 2014-11-29T12:37:50Z | 2014-11-29T22:29:04Z | 2014-11-29T14:26:28Z | NONE | null | As suggested in issue #8732 if there are no nulls, we skip the nan_safe bit.
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| null | 3 | 2014-11-29T13:19:45Z | 2014-11-29T16:48:19Z | 2014-11-29T16:48:19Z | CONTRIBUTOR | null | Option context no longer overrides options when used outside a `with`
statement.
Added test TestConfig.test_option_config_scope
Closes #8514
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} | 17 | 2014-11-29T13:33:44Z | 2014-12-02T23:31:44Z | 2014-12-02T23:31:30Z | CONTRIBUTOR | null | This is the proposed general gist of the changes. My ad-hoc testing suggests that this might work. If this is an acceptible design, I will proceed to make the formal test and change the docs (including docstrings)
Closes #8778
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} | 2 | 2014-11-29T14:03:47Z | 2014-12-03T22:37:49Z | 2014-12-03T22:37:35Z | CONTRIBUTOR | null | Fixes https://github.com/pydata/pandas/issues/8621
As mentioned in the github issue its a py3 only bug. I copied the test @rmorgans made
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| null | 3 | 2014-11-29T15:55:50Z | 2014-12-03T08:28:03Z | 2014-12-03T08:27:50Z | CONTRIBUTOR | null | Continued in #8972
This closes #8420, with code that supports searchsorted in Categorical, for both side='left'/'right' and with an optional sorter. The case of side='right' was tricky, as we look to the right only when matching the category name, not when matching the codes. The docstring and unit tests cover the various cases.
One thing I wasn't sure about was forcing scalar input to a Series. I copied this behaviour from Series.searchsorted to match its docstring examples. To avoid circular imports from series.py importing categorical.py, I got Series via a local import. Probably there is a better way to do this.
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} | 10 | 2014-11-29T16:32:11Z | 2014-12-03T22:38:46Z | 2014-12-03T22:33:53Z | CONTRIBUTOR | null | Ensure that we can pass an np.array as 'c' straight through to
matplotlib, this functionality was accidentally removed previously.
Add tests.
Closes #8852
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ERROR: Failure: SyntaxError (Non-ASCII character '\xc3' in file /home/fvia/bloom/pandas/pandas/tests/test_format.py on line 825, but no encoding declared; see http://python.org/dev/peps/pep-0263/ for details (test_format.py, line 825))
```
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| null | 1 | 2014-11-29T17:08:12Z | 2014-11-30T20:40:31Z | 2014-11-30T11:35:55Z | CONTRIBUTOR | null | $ nosetests pandas/core/common.py --with-doc -v
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} | 1 | 2014-11-29T19:06:29Z | 2018-08-14T10:49:11Z | 2018-08-14T10:49:11Z | MEMBER | null | Compare (on Python 2.7):
```
In [7]: pd.Timestamp('2000-01-01') > pd.Series(range(5))
TypeError: Cannot compare type 'Timestamp' with type 'int'
In [8]: pd.Timestamp('2000-01-01') > pd.DataFrame({'x': range(5)})
Out[8]:
x
0 True
1 True
2 True
3 True
4 True
```
Probably will fix this in the process of getting #8916 to pass, but I wanted to raise this as a separate issue in case this is intentional. Pretty sure it's not, but there was a test-case specifically introduced that relies on this behavior (see PR #4983).
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} | 2 | 2014-11-29T21:13:35Z | 2016-10-12T23:05:21Z | 2015-03-11T20:46:30Z | CONTRIBUTOR | null | - Anaconda has a conda recipe for pandas, but nice to include this inline
- ideally add a binstar recipe to check builds on linux/windows/mac
- nice little binstar build badges!
will close #7127 (and obviate need for #8630)
@cpcloud
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} | 7 | 2014-11-29T21:15:53Z | 2014-12-12T13:50:38Z | 2014-12-12T13:45:33Z | CONTRIBUTOR | null | We have a scheduled release of 0.15.2 for 12/8. So let's try to move pushable issues asap.
I was also thinking of having 0.16.0 be a somewhat shorter cycle, say 2 months. Pls lmk any objections.
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| null | 5 | 2014-11-29T21:17:54Z | 2015-05-09T15:58:22Z | 2015-05-09T15:58:22Z | CONTRIBUTOR | null | Hi,
This isn't ready for primetime yet, but I wanted to see if there was feedback, since it would be a new io module.
Basic usage:
```
import pandas.io.gcs as gcs
import pandas as pd
import numpy as np
df = pd.DataFrame({
'a': np.random.normal(size=1e4),
'b': np.random.normal(size=1e4)
})
gcs.to_gcs(df, "th-code", "pandas2.csv", "eighth-physics-623", to_gcs_kwargs={'resumable': True}, to_csv_kwargs={'index': False})
i = gcs.from_gcs("th-code", "pandas2.csv", "eighth-physics-623")
```
This borrows some of the connection patterns from `gbq`, though it can do service account auth in addition to OAuth2WebServer auth. Might be nice at some point to centralize google api connection stuff. If this is accepted the code to connect to google's APIs will be in 3 places.
I'm not sure everything this would need to be accepted, besides tests...
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} | 5 | 2014-11-29T21:42:38Z | 2014-11-30T16:09:14Z | 2014-11-30T16:09:10Z | CONTRIBUTOR | null | Discussion see here: https://github.com/pydata/pandas/issues/8559#issuecomment-64189570
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} | 6 | 2014-11-29T22:35:11Z | 2014-12-04T11:06:12Z | 2014-12-04T11:06:12Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/27164380/merging-pandas-dataframes-on-categorical-series)
```
In [1]: a = pd.Series(['a','b','c'],dtype="category")
In [2]: b = pd.Series(['a','b','c'],dtype="object")
In [3]: c = pd.Series(['a','b','cc'],dtype="object")
In [5]: a==b
TypeError: Cannot compare a Categorical for op <built-in function eq> with type <type 'numpy.ndarray'>. If you want to
compare values, use 'series <op> np.asarray(cat)'.
```
```
In [6]: A = pd.DataFrame({'A':a,'B':[1,2,3]})
In [7]: B = pd.DataFrame({'A':b,'C':[4,5,6]})
In [9]: A.merge(B,on='A')
Out[9]:
A B C
0 a 1 4
1 b 2 5
2 c 3 6
In [10]: A.merge(B,on='A').dtypes
Out[10]:
A object
B int64
C int64
dtype: object
In [11]: A.dtypes
Out[11]:
A category
B int64
dtype: object
In [12]: B.dtypes
Out[12]:
A object
C int64
dtype: object
```
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} | 3 | 2014-11-30T12:41:40Z | 2014-12-01T00:08:43Z | 2014-12-01T00:08:43Z | CONTRIBUTOR | null | Fix for https://github.com/pydata/pandas/issues/8765
The font size was only set on x axis if vertical or the y axis if horizontal.
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} | 6 | 2014-11-30T13:33:57Z | 2014-11-30T23:21:01Z | 2014-11-30T23:21:01Z | CONTRIBUTOR | null | Fixes an issue where resampling over multiple days causes a ValueError
when a number of days between the normalized first and normalized last
days is not a multiple of the frequency.
Added test TestSeries.test_resample
Closes #8683
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} | 2 | 2014-11-30T14:10:47Z | 2015-05-09T16:06:17Z | 2015-05-09T16:06:17Z | NONE | null | closes #4314
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} | 5 | 2014-11-30T14:21:03Z | 2014-12-17T22:14:26Z | 2014-12-17T22:14:26Z | CONTRIBUTOR | null | https://github.com/pydata/pandas/blob/master/pandas/tseries/index.py#L1768
a simple `np.int64(periods)` will suffice
(and tests of course)
This needs to guaranteed to be int64 as a result (e.g. if this is in32 it will have an internal overflow which will make it have a valid but weird result)
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} | 1 | 2014-11-30T15:26:04Z | 2014-12-05T02:11:13Z | 2014-12-05T02:11:13Z | MEMBER | null | Third example here: http://pandas-docs.github.io/pandas-docs-travis/cookbook.html#grouping
Test code:
```
df = pd.DataFrame({'animal': 'cat dog cat fish dog cat cat'.split(),
'size': list('SSMMMLL'),
'weight': [8, 10, 11, 1, 20, 12, 12],
'adult' : [False] * 5 + [True] * 2})
gb = df.groupby(['animal'])
def GrowUp(x):
avg_weight = sum(x[x.size == 'S'].weight * 1.5)
avg_weight += sum(x[x.size == 'M'].weight * 1.25)
avg_weight += sum(x[x.size == 'L'].weight)
avg_weight = avg_weight / len(x)
return pd.Series(['L',avg_weight,True], index=['size', 'weight', 'adult'])
expected_df = gb.apply(GrowUp)
```
On master this gives:
```
In [1]: pd.__version__
Out[1]: '0.15.1-112-g2e59e42'
In [5]: expected_df = gb.apply(GrowUp)
...
KeyError: False
```
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tests that need skipping on matplotlib 1.2.1
```
=====================================================================
ERROR: test_hist_df_coord (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/tests/test_graphics.py", line 2145, in test_hist_df_coord
ax = df.plot(kind='hist', bins=5, orientation='horizontal')
File "/home/travis/build/jreback/pandas/pandas/tools/plotting.py", line 2454, in plot_frame
**kwds)
File "/home/travis/build/jreback/pandas/pandas/tools/plotting.py", line 2294, in _plot
plot_obj.generate()
File "/home/travis/build/jreback/pandas/pandas/tools/plotting.py", line 920, in generate
self._make_plot()
File "/home/travis/build/jreback/pandas/pandas/tools/plotting.py", line 1949, in _make_plot
artists = plotf(ax, y, column_num=i, **kwds)
File "/home/travis/build/jreback/pandas/pandas/tools/plotting.py", line 1929, in plotf
bottom=bottom, **kwds)
File "/home/travis/miniconda/envs/pandas/lib/python2.7/site-packages/matplotlib/axes.py", line 8180, in hist
color=c, bottom=bottom)
TypeError: barh() got multiple values for keyword argument 'bottom'
======================================================================
ERROR: test_hist_kwargs (pandas.tests.test_graphics.TestSeriesPlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/tests/test_graphics.py", line 821, in test_hist_kwargs
ax = self.ts.plot(kind='hist', orientation='horizontal')
File "/home/travis/build/jreback/pandas/pandas/tools/plotting.py", line 2488, in plot_series
**kwds)
File "/home/travis/build/jreback/pandas/pandas/tools/plotting.py", line 2294, in _plot
plot_obj.generate()
File "/home/travis/build/jreback/pandas/pandas/tools/plotting.py", line 920, in generate
self._make_plot()
File "/home/travis/build/jreback/pandas/pandas/tools/plotting.py", line 1949, in _make_plot
artists = plotf(ax, y, column_num=i, **kwds)
File "/home/travis/build/jreback/pandas/pandas/tools/plotting.py", line 1929, in plotf
bottom=bottom, **kwds)
File "/home/travis/miniconda/envs/pandas/lib/python2.7/site-packages/matplotlib/axes.py", line 8180, in hist
color=c, bottom=bottom)
TypeError: barh() got multiple values for keyword argument 'bottom'
----------------------------------------------------------------------
Ran 194 tests in 1178.119s
```
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} | 1 | 2014-11-30T22:20:32Z | 2014-12-01T12:54:02Z | 2014-12-01T00:46:44Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/7331
on master:
```
>>> left
dates states
0 20140101 CA
1 20140102 NY
2 20140103 CA
>>> right
stateid states
0 1 CA
1 2 NY
>>> pd.merge(left, right, how='left', on='states', sort=False)
dates states stateid
0 20140101 CA 1
1 20140103 CA 1
2 20140102 NY 2
```
`DataFrame.join` already works fine:
```
>>> left.join(right.set_index('states'), on='states', how='left')
dates states stateid
0 20140101 CA 1
1 20140102 NY 2
2 20140103 CA 1
```
on branch:
```
>>> pd.merge(left, right, how='left', on='states', sort=False)
dates states stateid
0 20140101 CA 1
1 20140102 NY 2
2 20140103 CA 1
>>> pd.merge(left, right, how='left', on='states', sort=True)
dates states stateid
0 20140101 CA 1
1 20140103 CA 1
2 20140102 NY 2
```
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https://api.github.com/repos/pandas-dev/pandas/issues/8949 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8949/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8949/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8949/events | https://github.com/pandas-dev/pandas/pull/8949 | 50,474,326 | MDExOlB1bGxSZXF1ZXN0MjUyNDExNzk= | 8,949 | BLD/TST: skip mpl 1.2.1 tests (GH8947) | {
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} | 0 | 2014-11-30T23:00:05Z | 2014-11-30T23:37:41Z | 2014-11-30T23:37:41Z | CONTRIBUTOR | null | closes #8947
add SLOW build (optional)
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https://api.github.com/repos/pandas-dev/pandas/issues/8950 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8950/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8950/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8950/events | https://github.com/pandas-dev/pandas/pull/8950 | 50,498,434 | MDExOlB1bGxSZXF1ZXN0MjUyNTMxNjI= | 8,950 | Updating generic.py error message #8618 - New branch | {
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} | 7 | 2014-12-01T08:24:17Z | 2014-12-03T10:52:43Z | 2014-12-03T10:51:37Z | CONTRIBUTOR | null | closes #8015
I deleted the previous branch that was causing problems and submitted this.
This is my first attempt at a PR. I just made a little change to allow an error message. Would appreciate some feedback.
@jreback @jorisvandenbossche @hayd
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https://api.github.com/repos/pandas-dev/pandas/issues/8951 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8951/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8951/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8951/events | https://github.com/pandas-dev/pandas/issues/8951 | 50,521,688 | MDU6SXNzdWU1MDUyMTY4OA== | 8,951 | `DataFrame.iterrows()` breaks timezone on index | {
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| null | 2 | 2014-12-01T13:06:23Z | 2014-12-01T13:46:52Z | 2014-12-01T13:45:39Z | CONTRIBUTOR | null | Duplicate of #8890.
As far as I can tell, the Timestamps for the index generated by `iterrows()` are 5 hours behind where they should be in this example:
``` python
In [33]: idx = pd.date_range("2010-01-01 00:00:00-0500", freq='D', periods=3)
In [34]: df = pd.DataFrame([1,2,3], index=[idx])
In [35]: df # this looks correct
Out[35]:
0
2010-01-01 00:00:00-05:00 1
2010-01-02 00:00:00-05:00 2
2010-01-03 00:00:00-05:00 3
In [36]: [index for index, row in df.iterrows()] # but this looks wrong:
Out[36]:
[Timestamp('2009-12-31 19:00:00-0500', tz='pytz.FixedOffset(-300)', offset='D'),
Timestamp('2010-01-01 19:00:00-0500', tz='pytz.FixedOffset(-300)', offset='D'),
Timestamp('2010-01-02 19:00:00-0500', tz='pytz.FixedOffset(-300)', offset='D')]
```
I would have expected `iterrows()` to produce the same indices as this code:
``` python
In [38]: [df.index[i] for i in range(len(df))]
Out[38]:
[Timestamp('2010-01-01 00:00:00-0500', tz='pytz.FixedOffset(-300)', offset='D'),
Timestamp('2010-01-02 00:00:00-0500', tz='pytz.FixedOffset(-300)', offset='D'),
Timestamp('2010-01-03 00:00:00-0500', tz='pytz.FixedOffset(-300)', offset='D')]
```
The `row.name` is also incorrect:
``` python
In [37]: [row.name for index, row in df.iterrows()]
Out[37]:
[Timestamp('2009-12-31 19:00:00-0500', tz='pytz.FixedOffset(-300)', offset='D'),
Timestamp('2010-01-01 19:00:00-0500', tz='pytz.FixedOffset(-300)', offset='D'),
Timestamp('2010-01-02 19:00:00-0500', tz='pytz.FixedOffset(-300)', offset='D')]
```
But all is fine if we use a geographical timezone instead of a `pytz.FixedOffset`:
``` python
In [47]: idx = pd.date_range("2010-01-01 00:00:00", freq='D', periods=3, tz="America/New_York")
In [48]: df = pd.DataFrame([1,2,3], index=[idx])
In [49]: [index for index, row in df.iterrows()]
Out[49]:
[Timestamp('2010-01-01 00:00:00-0456', tz='America/New_York', offset='D'),
Timestamp('2010-01-02 00:00:00-0456', tz='America/New_York', offset='D'),
Timestamp('2010-01-03 00:00:00-0456', tz='America/New_York', offset='D')]
In [50]: df
Out[50]:
0
2010-01-01 00:00:00-04:56 1
2010-01-02 00:00:00-04:56 2
2010-01-03 00:00:00-04:56 3
```
Forgive me if I am using Pandas incorrectly!
Versions:
```
In [51]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-25-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.15.1
nose: 1.3.4
Cython: 0.21.1
numpy: 1.8.2
scipy: 0.14.0
statsmodels: None
IPython: 2.3.1
sphinx: 1.2.3
patsy: None
dateutil: 2.2
pytz: 2014.10
bottleneck: 0.6.0
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.2
openpyxl: 1.8.6
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.3.6
bs4: None
html5lib: 0.999
httplib2: 0.9
apiclient: None
rpy2: 2.3.8
sqlalchemy: None
pymysql: None
psycopg2: None
```
(it goes without saying that I'm a huge fan of Pandas!)
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https://api.github.com/repos/pandas-dev/pandas/issues/8952 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8952/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8952/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8952/events | https://github.com/pandas-dev/pandas/pull/8952 | 50,549,437 | MDExOlB1bGxSZXF1ZXN0MjUyODM2Nzc= | 8,952 | Doc for GH 8946 | {
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| null | 1 | 2014-12-01T17:17:45Z | 2014-12-02T12:15:59Z | 2014-12-02T12:15:59Z | CONTRIBUTOR | null | For https://github.com/pydata/pandas/pull/8946
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} | 48 | 2014-12-01T18:29:49Z | 2019-11-28T22:14:34Z | 2018-12-28T21:03:13Z | CONTRIBUTOR | null | I have been trying to insert ~30k rows into a mysql database using pandas-0.15.1, oursql-0.9.3.1 and sqlalchemy-0.9.4. Because the machine is as across the atlantic from me, calling `data.to_sql` was taking >1 hr to insert the data. On inspecting with wireshark, the issue is that it is sending an insert for every row, then waiting for the ACK before sending the next, and, long story short, the ping times are killing me.
However, following the instructions from [SQLAlchemy](http://docs.sqlalchemy.org/en/latest/core/dml.html#sqlalchemy.sql.expression.Insert.values), I changed
``` python
def _execute_insert(self, conn, keys, data_iter):
data = [dict((k, v) for k, v in zip(keys, row)) for row in data_iter]
conn.execute(self.insert_statement(), data)
```
to
``` python
def _execute_insert(self, conn, keys, data_iter):
data = [dict((k, v) for k, v in zip(keys, row)) for row in data_iter]
conn.execute(self.insert_statement().values(data))
```
and the entire operation completes in less than a minute. (To save you a click, the difference is between multiple calls to `insert into foo (columns) values (rowX)` and one massive `insert into foo (columns) VALUES (row1), (row2), row3)`). Given how often people are likely to use pandas to insert large volumes of data, this feels like a huge win that would be great to be included more widely.
Some challenges:
- Not every database supports multirow inserts (SQLite and SQLServer didn't in the past, though they do now). I don't know how to check for this via SQLAlchemy
- The MySQL server I was using didn't allow me to insert the data all in one go, I had to set the chunksize (5k worked fine, but I guess the full 30k was too much). If we made this the default insert, most people would have to add a chunk size (which might be hard to calculate, as it might be determined by the maximum packet size of the server).
The easiest way to do this, would be to add a `multirow=` boolean parameter (default `False`) to the `to_sql` function, and then leave the user responsible for setting the chunksize, but perhaps there's a better way?
Thoughts?
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} | 11 | 2014-12-01T21:06:12Z | 2015-04-28T12:00:07Z | 2015-04-28T12:00:07Z | CONTRIBUTOR | null | closes #7856
reports a problem with the application of max_colwidth to groupby.
The real problem is that max_colwidth is not applied to the index. This patch solves that.
I am not applying self.justify to the formatting of the index (as that would break a test). But maybe it makes more sense to change the test?
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} | 5 | 2014-12-01T22:06:13Z | 2021-01-10T14:53:04Z | null | CONTRIBUTOR | null | In a groupby/transform when some of the groups are missing, should the transformed values be set to missing (my preference), left unchanged, or should this be an error? Currently the behavior is inconsistent between Series and Frames, and between cythonized and non-cythonized transformations.
For a Series with a non-cythonized transformation, the values are left unchanged:
```
>>> import pandas as pd
>>> import numpy as np
>>> s = pd.Series([100, 200, 300, 400])
>>> s.groupby([1, 1, np.nan, np.nan]).transform(pd.Series.mean)
0 200
1 200
2 300
3 400
```
For a Series with cythonized functions, its an error (this changed between 0.14.1 and 0.15.0):
```
>>> s.groupby([1, 1, np.nan, np.nan]).transform(np.mean)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/pandas/core/groupby.py", line 2425, in transform
return self._transform_fast(cyfunc)
File "pandas/pandas/core/groupby.py", line 2466, in _transform_fast
return self._set_result_index_ordered(Series(values))
File "pandas/pandas/core/groupby.py", line 494, in _set_result_index_ordered
result.index = self.obj.index
File "pandas/pandas/core/generic.py", line 1948, in __setattr__
object.__setattr__(self, name, value)
File "pandas/src/properties.pyx", line 65, in pandas.lib.AxisProperty.__set__ (pandas/lib.c:41020)
File "pandas/pandas/core/series.py", line 262, in _set_axis
self._data.set_axis(axis, labels)
File "pandas/pandas/core/internals.py", line 2217, in set_axis
'new values have %d elements' % (old_len, new_len))
ValueError: Length mismatch: Expected axis has 2 elements, new values have 4 elements
```
For DataFrames, the results are opposite:
```
>>> f = pd.DataFrame({'a': s, 'b': s * 2})
>>> f
a b
0 100 200
1 200 400
2 300 600
3 400 800
>>> f.groupby([1, 1, np.nan, np.nan]).transform(np.sum)
a b
0 300 600
1 300 600
2 300 600
3 400 800
>>> f.groupby([1, 1, np.nan, np.nan]).transform(pd.DataFrame.sum)
Traceback (most recent call last):
File "pandas/pandas/core/groupby.py", line 3002, in transform
return self._transform_general(func, *args, **kwargs)
File "pandas/pandas/core/groupby.py", line 2968, in _transform_general
return self._set_result_index_ordered(concatenated)
File "pandas/pandas/core/groupby.py", line 494, in _set_result_index_ordered
result.index = self.obj.index
File "pandas/pandas/core/generic.py", line 1948, in __setattr__
object.__setattr__(self, name, value)
File "pandas/src/properties.pyx", line 65, in pandas.lib.AxisProperty.__set__ (pandas/lib.c:41020)
File "pandas/pandas/core/generic.py", line 406, in _set_axis
self._data.set_axis(axis, labels)
File "pandas/pandas/core/internals.py", line 2217, in set_axis
'new values have %d elements' % (old_len, new_len))
ValueError: Length mismatch: Expected axis has 2 elements, new values have 4 elements
```
```
>>> print(pd.__version__)
0.15.1-125-ge463818
```
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} | 13 | 2014-12-01T22:33:01Z | 2014-12-04T00:08:20Z | 2014-12-04T00:08:20Z | CONTRIBUTOR | null | The documentation for `.loc` at http://pandas.pydata.org/pandas-docs/version/0.15.1/indexing.html#different-choices-for-indexing should mention that `.iloc` also takes a boolean array instead of insisting that it is "strictly integer position based".
```
.iloc is strictly integer position based (from 0 to length-1 of the axis), will raise IndexError if an indexer is requested and it is out-of-bounds, except slice indexers which allow out-of-bounds indexing. (this conforms with python/numpy slice semantics). Allowed inputs are:
An integer e.g. 5
A list or array of integers [4, 3, 0]
A slice object with ints 1:7
```
Unlike `.loc` immediately above, no mention is made of "A boolean array"
This is incorrect as [later in the same document](http://pandas.pydata.org/pandas-docs/version/0.15.1/indexing.html#indexing-with-isin) an example uses `.iloc` and `isin` to retrieve elements from a Series. isin returns a list of booleans (well, strictly it is a numpy.ndarray, but converting to a list works as well)
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} | 5 | 2014-12-02T00:11:05Z | 2014-12-04T00:08:00Z | 2014-12-03T00:20:09Z | CONTRIBUTOR | null | Closes #8944
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} | 10 | 2014-12-02T08:59:57Z | 2014-12-04T11:30:51Z | 2014-12-04T11:30:36Z | NONE | null | Added a context manager to HDFStore
closes #8791
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} | 1 | 2014-12-02T09:00:48Z | 2014-12-02T10:52:26Z | 2014-12-02T10:52:26Z | NONE | null | closes #8933
Refactored _is_sequence to is_sequence
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| null | 4 | 2014-12-02T09:29:11Z | 2014-12-03T02:16:30Z | 2014-12-03T02:16:30Z | NONE | null | Hi all,
I've updated to version 0.15.1 from 0.14.x (Python3.4) and the following code fails in the latest version if the blank line (third line) in `data` has a tab or whitespace (I think it should be ignored when `skiprows` is used):
``` Python
import io
import pandas as pd
data = """Lat=0 Lon=0
Whatever - Not very important
YYYYMMDD HHMM M(m/s) D(deg) T(C) De(k/m3) PRE(hPa) RiNumber RH(%)
19840101 0000 21.7 237 9.1 1.23 996.3 0.09 87.4
19840101 0100 22.4 239 9.4 1.23 995.7 0.10 87.2
19840101 0200 22.5 240 9.5 1.23 995.2 0.11 87.5"""
pd.read_csv(io.StringIO(data), delim_whitespace = True, skiprows = 4)
```
The output with a whitespace or tab in the third line (blank line part of the header) is:
<table>
<thead>
<tr style="text-align: right;">
<th></th>
<th>YYYYMMDD</th>
<th>HHMM</th>
<th>M(m/s)</th>
<th>D(deg)</th>
<th>T(C)</th>
<th>De(k/m3)</th>
<th>PRE(hPa)</th>
<th>RiNumber</th>
<th>RH(%)</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td> 19840101</td>
<td> 0</td>
<td> 21.7</td>
<td> 237</td>
<td> 9.1</td>
<td> 1.23</td>
<td> 996.3</td>
<td> 0.09</td>
<td> 87.4</td>
</tr>
<tr>
<th>1</th>
<td> 19840101</td>
<td> 100</td>
<td> 22.4</td>
<td> 239</td>
<td> 9.4</td>
<td> 1.23</td>
<td> 995.7</td>
<td> 0.10</td>
<td> 87.2</td>
</tr>
<tr>
<th>2</th>
<td> 19840101</td>
<td> 200</td>
<td> 22.5</td>
<td> 240</td>
<td> 9.5</td>
<td> 1.23</td>
<td> 995.2</td>
<td> 0.11</td>
<td> 87.5</td>
</tr>
</tbody>
</table>
And the output without the whitespace and/or a tab is the expected:
<table>
<thead>
<tr style="text-align: right;">
<th></th>
<th>19840101</th>
<th>0000</th>
<th>21.7</th>
<th>237</th>
<th>9.1</th>
<th>1.23</th>
<th>996.3</th>
<th>0.09</th>
<th>87.4</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td> 19840101</td>
<td> 100</td>
<td> 22.4</td>
<td> 239</td>
<td> 9.4</td>
<td> 1.23</td>
<td> 995.7</td>
<td> 0.10</td>
<td> 87.2</td>
</tr>
<tr>
<th>1</th>
<td> 19840101</td>
<td> 200</td>
<td> 22.5</td>
<td> 240</td>
<td> 9.5</td>
<td> 1.23</td>
<td> 995.2</td>
<td> 0.11</td>
<td> 87.5</td>
</tr>
</tbody>
</table>
`pd.show_versions()` provides the following output:
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.1.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: x86
processor: x86 Family 6 Model 37 Stepping 5, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.15.1
nose: 1.3.4
Cython: None
numpy: 1.9.1
scipy: 0.14.0
statsmodels: 0.6.0
IPython: 3.0.0-dev
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.1
pytz: 2014.9
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.0
openpyxl: None
xlrd: 0.9.3
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: 2.4.4
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
```
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} | 48 | 2014-12-02T10:15:17Z | 2015-08-26T01:45:36Z | 2015-08-26T01:45:36Z | MEMBER | null | Opening an issue to discuss this: **should we think about moving the functionality to read online data sources to a separate package?**
This was mentioned recently here https://github.com/pydata/pandas/issues/8842#issuecomment-63456052 and https://github.com/pydata/pandas/pull/8631#issuecomment-61805926
Some reasons why we would want to move it:
- This often needs much more urgent releases than pandas to fix changes in the remote sources. So with separate release cycle, this can speed up propagation of bugfixes to users
- More general: to limit the scope of pandas as the core project, and to encourage that some people take ownership of this subproject (which is easier as a separate project?), publicize it separately, ..
Some questions that come to mind:
- What should we move? (everything in [remote_data.rst](http://pandas.pydata.org/pandas-docs/stable/remote_data.html)?)
- Everything in `io.data`? (the `DataReader` function for Yahoo and Google Finance, FRED and Fama/French, and the `Options` class
- Also `io.wb` (certain World Bank data) and `io.ga` (google analytics interface)?
- Should we move it all to one separate project, or to multiple?
- Are there outside of pandas already some packages like this? (possibilities to merge with that/collaborate/..)
- Who would be interested to take this up? To be a maintainer of such a package?
Pinging some of the people have worked on these subpackages (certainly add others if you know):
@dstephens99 @MichaelWS @kdiether @cpcloud @vincentarelbundock @jnmclarty
@jreback @immerrr @TomAugspurger @hayd @jtratner
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| null | 14 | 2014-12-02T10:50:54Z | 2021-08-27T17:14:32Z | null | NONE | null | Would it be possible to add a parameter to pd.merge/DataFrame.join which accepts a function that does the actual merging of the two joined rows.
```
def my_joiner(left,right):
left.a = right.a if left.a == NaN else left.a
left.b = right.b if left.cnt < 3 else left.b
...
return left
pd.merge(df1,df2,joiner=my_joiner)
```
As I often use merge/join to impute missing or statistically unreliable data I find myself writeing code like the following over and over again:
```
df3 = pd.merge(df1,df2)
df3['a'] = df3.a_x.fillna(df3.a_y)
df3['b'] = df3.apply(lambda x: x.b_x if x.cnt > 3 else x.b_y)
...
df3 = df3.drop([a_x, a_y, b_x, b_y])
```
Which is rather cumbersome and cluttered.
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} | 2 | 2014-12-02T11:24:25Z | 2015-04-04T18:51:44Z | 2015-04-04T18:51:44Z | CONTRIBUTOR | null | this is the schema from astropy
https://github.com/astropy/astropy/wiki/Issue-labeling-convention
Numpy/scipy are considering this
might make issue navigation easier
making this issue for comments on these labels (I am for accepting them almost as is)
---
`package-novice` - accessible to someone who has no knowledge of astropy internals and can be done without this knowledge.
`package-intermediate` - accessible to someone who has at least good experience in using the package and understands the issue well.
`package-expert` - accessible only to people intimately familiar with the sub-package/functionality.
`effort-low` - a few hours at most
`effort-medium` - a few days at most
`effort-high` - longer term
The effort should be indicated for the package-... level specified (so the effort may be lower for a more experienced developer). We also use the following tags:
`domain-specialist` - indicates that knowledge of the specific domain (e.g. cosmology, coordinate transformation) is needed.
the we should doc these on the contributors page
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} | 9 | 2014-12-02T11:45:28Z | 2014-12-05T05:15:44Z | 2014-12-05T01:59:06Z | NONE | null | closes #8866
The bug was caused by a couple of things, firstly when specifying only a level, the axis default was None. This meant it failed to create the internal grouper breaking on line 254 of groupby.py:
``` python
ax = obj._get_axis(self.axis)
```
Secondly, the aggregate function is set up to take a basegrouper class whereas for datetime resampling we need to pass a datetime index. I split the `_set_grouper` method to return a datetime index when a resampling frequency is specified and a `BaseGrouper` class otherwise. I opted to split that out into two functions to reduce complexity with much of the `_set_basegrouper` function being borrowed from the `_get_grouper` function. Let me know if there's a better way to fix this.
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} | 0 | 2014-12-02T12:50:12Z | 2014-12-03T14:27:36Z | 2014-12-03T14:27:36Z | CONTRIBUTOR | null | Ref: http://stackoverflow.com/q/27240365/190597
```
s = pd.Series(pd.date_range('2008-09-15', periods=10, freq='m'))
s.loc[10] = pd.NaT
s.cummin()
ValueError: Could not convert object to NumPy datetime
```
- validate for `cummax` & `timedelta64[ns]`
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} | 1 | 2014-12-02T14:53:07Z | 2015-03-07T23:17:36Z | 2015-03-07T23:17:36Z | NONE | null | Hello,
```
import pandas.io.data as web
import datetime
symbol = "AAPL"
end = datetime.datetime.now()
start = end - datetime.timedelta(days=10)
df = web.DataReader("AAPL", 'google', start, end)
df.index.name
```
is `'\xef\xbb\xbfDate'` it should be only `'Date'`
\xef\xbb\xbf characters are invisible !
A quick and dirty solution is to change
```
def _retry_read_url(url, retry_count, pause, name):
for _ in range(retry_count):
time.sleep(pause)
# kludge to close the socket ASAP
try:
with urlopen(url) as resp:
lines = resp.read()
except _network_error_classes:
pass
else:
rs = read_csv(StringIO(bytes_to_str(lines)), index_col=0,
parse_dates=True)[::-1]
# Yahoo! Finance sometimes does this awesome thing where they
# return 2 rows for the most recent business day
if len(rs) > 2 and rs.index[-1] == rs.index[-2]: # pragma: no cover
rs = rs[:-1]
return rs
raise IOError("after %d tries, %s did not "
"return a 200 for url %r" % (retry_count, name, url))
```
to
```
def _retry_read_url(url, retry_count, pause, name):
for _ in range(retry_count):
time.sleep(pause)
# kludge to close the socket ASAP
try:
with urlopen(url) as resp:
lines = resp.read()
except _network_error_classes:
pass
else:
rs = read_csv(StringIO(bytes_to_str(lines)), index_col=0,
parse_dates=True)[::-1]
rs.index.name = 'Date'
# Yahoo! Finance sometimes does this awesome thing where they
# return 2 rows for the most recent business day
if len(rs) > 2 and rs.index[-1] == rs.index[-2]: # pragma: no cover
rs = rs[:-1]
return rs
raise IOError("after %d tries, %s did not "
"return a 200 for url %r" % (retry_count, name, url))
```
so adding rs.index.name = 'Date'
fix the problem but it could have side effect for other datasource
Kind regards
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} | 6 | 2014-12-02T15:33:27Z | 2014-12-06T15:10:53Z | 2014-12-06T15:10:53Z | CONTRIBUTOR | null | ```
======================================================================
ERROR: test_missing_value_conversion (pandas.io.tests.test_stata.TestStata)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.15.1+git125-ge463818/debian/tmp/usr/lib/python3/dist-packages/pandas/io/tests/test_stata.py", line 651, in test_missing_value_conversion
parsed_113 = read_stata(self.dta17_113, convert_missing=True)
File "/tmp/buildd/pandas-0.15.1+git125-ge463818/debian/tmp/usr/lib/python3/dist-packages/pandas/io/stata.py", line 69, in read_stata
order_categoricals)
File "/tmp/buildd/pandas-0.15.1+git125-ge463818/debian/tmp/usr/lib/python3/dist-packages/pandas/io/stata.py", line 1278, in data
missing_value = StataMissingValue(um)
File "/tmp/buildd/pandas-0.15.1+git125-ge463818/debian/tmp/usr/lib/python3/dist-packages/pandas/io/stata.py", line 646, in __init__
self._str = self.MISSING_VALUES[value]
KeyError: 2147483647
```
doesn't happen on amd64
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.3.2.final.0
python-bits: 32
OS: Linux
OS-release: 3.2.0-4-amd64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: C
pandas: 0.15.1.dev
nose: 1.3.0
Cython: 0.19
numpy: 1.7.1
scipy: 0.12.0
statsmodels: None
IPython: None
sphinx: 1.1.3
patsy: None
dateutil: 2.0
pytz: 2012c
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.2.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.2.0
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 2 | 2014-12-02T16:26:14Z | 2014-12-03T14:28:48Z | 2014-12-03T14:24:58Z | NONE | null | Copied from #7858:
I am still getting this bug (pandas 0.15.1, numpy 1.9.1, Stata 13.1 on Windows 7). The written DTA file still stores the strings as str244 even though the strings themselves have length 1. This also means that when they're read back into pandas the DataFrame looks the same. I think the only way to detect it from within Pandas is to look at the size of the DTA file itself.
``` python
df = pd.DataFrame(['a', 'b', 'c'], columns=['alpha'])
df.to_stata('test.dta')
df2 = pd.read_stata('test.dta')
assert (df['alpha'] == df2['alpha']).min()
```
But when loading in Stata:
``` stata
. use test
. describe
Contains data from D:\data\Pollution\test.dta
obs: 3
vars: 2 02 Dec 2014 10:30
size: 744
--------------------------------------------------------------------------------------------
storage display value
variable name type format label variable label
--------------------------------------------------------------------------------------------
index long %12.0g
alpha str244 %1s
--------------------------------------------------------------------------------------------
Sorted by:
. compress
index was long now byte
alpha was str244 now str1
(738 bytes saved)
. describe
Contains data from D:\data\Pollution\test.dta
obs: 3
vars: 2 02 Dec 2014 10:30
size: 6
--------------------------------------------------------------------------------------------
storage display value
variable name type format label variable label
--------------------------------------------------------------------------------------------
index byte %12.0g
alpha str1 %9s
--------------------------------------------------------------------------------------------
Sorted by:
```
I don't know how critical this is since there are workarounds. You can either use `compress; save, replace` in Stata after every use of pandas or, if the 244 problem makes the DTA exceed your memory limit (which causes quite the system error lightshow as I just experienced), you could pass it through a CSV first.
It's just a matter of convenience.
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} | 4 | 2014-12-02T19:18:18Z | 2014-12-04T00:54:23Z | 2014-12-04T00:08:00Z | CONTRIBUTOR | null | resolves #8956 Trivial documentation addition of boolean array as option for .iloc
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| null | 5 | 2014-12-02T20:41:29Z | 2015-05-14T15:49:37Z | 2015-05-09T16:17:22Z | NONE | null | Closes #8732
In most cases it looks like, we need to iterate over array
and coerce each element. This is so that the appropriate
exception can be raised, or we can deal with nulls.
So the original case of casting ints to strings, has to
work the way it does, unless we change the underlying behaviour.
So when astype(str) is called on ints. Then each element
is first cast as a string then made into a numpy object.
If we relied on numpy it wouldn't cast it to string, just
return it as an object. This breaks existing behaviour.
It is possible to bypass iterating over the array, when we are
coercing to int. Assuming that there are no NaNs and the type
of the array is a numeric.
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} | 4 | 2014-12-02T23:59:50Z | 2014-12-05T13:36:25Z | 2014-12-05T13:36:25Z | CONTRIBUTOR | null | closes #8420
Continuation of #8928
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} | 16 | 2014-12-03T01:21:26Z | 2014-12-08T07:42:42Z | 2014-12-08T07:42:33Z | CONTRIBUTOR | null | Closes #8778
This infers dtype from non-null values for insertion into SQL database. See #8778 . I had to alter @tiagoantao test a bit.
Like @tiagoantao, I skipped writing tests for legacy MySQL. Support for this will be removed soon, right?
As a side note, `lib.infer_dtype` throws an exception for categorical data -- probably shouldn't happen, right?
@jorisvandenbossche
@jahfet
@tiagoantao
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} | 0 | 2014-12-03T01:50:27Z | 2014-12-03T02:53:18Z | 2014-12-03T02:53:18Z | CONTRIBUTOR | null | ```
In [13]: df = pd.DataFrame({'a':pd.Series(['A','B'], dtype='category')})
In [15]: lib.infer_dtype(df['a'])
TypeError: Cannot convert Categorical to numpy.ndarray
```
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} | 0 | 2014-12-03T02:14:19Z | 2014-12-03T02:53:18Z | 2014-12-03T02:53:18Z | CONTRIBUTOR | null | closes #8974
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} | 5 | 2014-12-03T02:45:20Z | 2020-02-11T19:00:35Z | 2020-02-11T19:00:34Z | CONTRIBUTOR | null | xref #8956
This should raise a `ValueError`.
```
In [18]: s = Series(np.arange(5,1,-1))
In [19]: s
Out[19]:
0 5
1 4
2 3
3 2
dtype: int64
In [20]: s.iloc[[True,False,True]]
Out[20]:
0 5
2 3
dtype: int64
```
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} | 8 | 2014-12-03T03:07:24Z | 2015-01-18T22:42:19Z | 2014-12-03T14:24:58Z | CONTRIBUTOR | null | Fixes bug where StataWriter always writes strings with
a size of 244.
closes #8969
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} | 4 | 2014-12-03T07:11:34Z | 2015-01-02T16:29:45Z | 2015-01-02T16:29:45Z | CONTRIBUTOR | null | Hi All!
I've been using .nth(), and found a couple issues (though it's extremely useful in general!). In particular, I couldn't find any help documentation (in python) for the function.
Also, I ran into issues with I had a two-column groupby. for example:
```
df = pd.DataFrame([[1,2,3],[1,5,6], [7,8,6]], columns = ['col1','col2','col3'])
df.groupby('col1').nth([0,1])
```
Is ok
```
In [23]: df.groupby(['col1', 'col2'], as_index = False).nth([0,1])
Out[23]:
col1 col2 col3
0 1 2 3
1 1 5 6
2 7 8 6
In [24]: df.groupby(['col1', 'col2']).nth([0,1])
ValueError: Length of new names must be 1, got 2
```
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} | 7 | 2014-12-03T09:07:13Z | 2014-12-07T00:07:23Z | 2014-12-07T00:07:23Z | NONE | null | Hello,
Google Finance DataReader returns columns with object type instead of float64
```
In [112]: import pandas.io.data as web
In [113]: import datetime
In [114]: start = datetime.datetime(2010, 1, 1)
In [115]: end = datetime.datetime(2013, 1, 27)
In [116]: f=web.DataReader("F", 'google', start, end)
In [117]: f.dtypes
Out[117]:
Open object
High object
Low object
Close float64
Volume int64
dtype: object
```
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} | 7 | 2014-12-03T12:40:22Z | 2015-01-18T22:42:16Z | 2014-12-06T15:10:53Z | CONTRIBUTOR | null | Force conversion to integer for missing values when
they must be integer to avoid hash errors on 32 bit
platforms.
closes #8968
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} | 0 | 2014-12-03T17:47:39Z | 2014-12-03T21:56:45Z | 2014-12-03T21:56:45Z | CONTRIBUTOR | null | as reported by @xdliao in #8752, skipping a row after a row with trailing spaces fails to create the expected dataframe
data = """A B C \nD E F \nH I J \n1 2 3 \n4 5 6 \n"""
pd.read_csv(StringIO(data), skiprows=2, delim_whitespace=True) and
pd.read_csv(StringIO(data), skiprows=[0,1], delim_whitespace=True) work as expected, but the dataframes returned by
pd.read_csv(StringIO(data), skiprows=[0,2], delim_whitespace=True) and
pd.read_csv(StringIO(data), skiprows=[1,2], delim_whitespace=True) are incorrect
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} | 6 | 2014-12-03T19:28:50Z | 2017-01-11T07:57:21Z | 2017-01-11T07:57:21Z | NONE | null | xref #6710
I have a CSV whose lines may have 11 or 18 fields. I only need to read the first 6 fields, so I use "usecols=range(6)". Even with the limited number of columns, I get the exception:
ValueError: Expected 11 fields in line 776483, saw 18
The csv_reader should completely disregard the unused fields.
Small test case:
```
csv = '19,29,39\n'*2 + '10,20,30,40\n'
df = pd.read_csv(io.StringIO(csv), engine='python', header=None, usecols=list(range(3)))
```
It also affects the C engine.
Discussed at the users mailing list at https://groups.google.com/d/topic/pydata/vjhFpHtgnvw/discussion
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} | 7 | 2014-12-03T19:47:26Z | 2015-01-26T00:04:38Z | 2015-01-26T00:04:28Z | CONTRIBUTOR | null | Working with the SQL code, I realized that the legacy code does not properly validate / escape passed in table and column names.
E.g.:
```
n [116]:
import pandas as pd
import pandas.io.sql as sql
import sqlite3
df = pd.DataFrame({'a':[1,2],'b':[2,3]})
con = sqlite3.connect(':memory:')
db = sql.SQLiteDatabase(con, 'sqlite')
t=sql.SQLiteTable('a; DELETE FROM contacts; INSERT INTO b', db, frame=df)
print t.insert_statement()
```
results in:
```
INSERT INTO a; DELETE FROM contacts; INSERT INTO b ([index],[a],[b]) VALUES (?,?,?)
```
This fixes the issues.
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| null | 5 | 2014-12-03T20:19:12Z | 2015-05-09T15:59:39Z | 2015-05-09T15:59:39Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/5289
Is this all we need to do?
Or am I missing something?
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} | 3 | 2014-12-03T22:51:12Z | 2014-12-04T10:43:27Z | 2014-12-04T08:27:53Z | MEMBER | null | Closes #8315
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} | 2 | 2014-12-03T23:41:15Z | 2014-12-05T14:34:26Z | 2014-12-05T14:34:26Z | MEMBER | null | xref #8904
In the format string, `%f` means microseconds (https://docs.python.org/2/library/datetime.html#strftime-and-strptime-behavior), but this means the nanoseconds cannot be read in using a `format` string:
```
In [11]: pd.to_datetime("2012-01-01 09:00:00.000000001", format="%Y-%m-%d %H:%M:%S.%f")
->
ValueError: unconverted data remains: 001
```
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} | 0 | 2014-12-04T01:36:00Z | 2014-12-04T02:47:31Z | 2014-12-04T02:47:31Z | CONTRIBUTOR | null | closes #8890
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} | 3 | 2014-12-04T02:06:36Z | 2021-11-14T02:17:44Z | 2021-11-14T02:17:44Z | NONE | null | I can't seem to be able to parse dates of multiheader files... Here's an example illustrating the problem I'm facing:
``` python
import pandas as pd
from io import StringIO
csv = """date,time,value
YYYY/MM/DD,HH:MM,Smp
2014/12/01,00:00,1
2014/12/01,01:00,2"""
pd.read_csv(StringIO(csv), header=[0,1], parse_dates=[['date','time']])
# ValueError: 'date' is not in list
```
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| null | 4 | 2014-12-04T09:09:00Z | 2014-12-07T15:36:32Z | 2014-12-07T15:36:32Z | NONE | null | Hello
i create a new store and i am trying to append it.
all is right when i did :
store.append("table", dataframe)
but when i did the whole thing with data_columns=dataframe.columns or data_columns=True
i can write the first append and then nothing ....
no more data input
tore.append("table", dataframe,data_columns=dataframe.columns )
any help ?
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} | 1 | 2014-12-04T10:02:45Z | 2014-12-04T11:02:41Z | 2014-12-04T11:02:32Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/27284460/how-to-sort-categorical-data-in-a-pandas-dataframe?noredirect=1#comment43038362_27284460
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} | 2 | 2014-12-04T12:13:52Z | 2014-12-07T15:15:23Z | 2014-12-07T15:15:23Z | CONTRIBUTOR | null | A student of mine ran into a confusing problem which ended up being due to an asymmetry in `DataFrame.__setattr__` and `DataFrame.__getattr__` when an attribute and a column have the same name. Here is a short example session:
``` python
import pandas as pd
print(pd.__version__) # '0.14.1'
data = pd.DataFrame({'x':[1, 2, 3]})
# try to create a new column, making a common mistake
data.y = 2 * data.x
# oops! That didn't create a column.
# we need to do it this way instead
data['y'] = 2 * data.x
# update the attribute, and it updates the column, not the attribute
data.y = 0
print(data['y']) # [0, 0, 0]
# print the attribute, and it prints the attribute, not the column
print(data.y) # [2, 4, 6]
print(data['y']) # [0, 0, 0]
```
The confusion derived from the fact that in this situation, `data.__getattr__('y')` refers to the _attribute_, while `data.__setattr__('y', val)` refers to the _column_.
I understand that using attributes to access columns is not recommended, but the asymmetry in this corner case led to a lot of confusion! It would be better if `__getattr__` and `__setattr__` would always refer to the same object.
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} | 3 | 2014-12-04T13:31:48Z | 2020-09-27T11:52:29Z | 2020-09-27T11:52:29Z | CONTRIBUTOR | null | From #8946:
If `cat > scalar` is allowed and `cat == list` also because it basically is doing a comparison of each line as if it was the scalar case, then by that logic, `cat > list` should also be allowed: each row in that comparison would treat the element from the list as a scalar.
On the other hand a scalar comparison with the categorical makes only sense if the scalar can be treated as a category (for any other value, it's basically a "not of the same type" comparison, which would raise on python3), so the scalar must be in `categories` and this should not work:
```
In[4]: df = pd.DataFrame({"a":[1,3,3,3,np.nan]})
In[6]: df["b"] = df.a.astype("category")
In[7]: df.b
Out[7]:
0 1
1 3
2 3
3 3
4 NaN
Name: b, dtype: category
Categories (2, float64): [1 < 3]
In[8]: df.b > 2
Out[8]:
0 False
1 True
2 True
3 True
4 False
Name: b, dtype: bool
```
Oh, one more thing: according to that thought, `df.b == 2` (-> The "equality" case) should also NOT work, because `2` is not in `categories` and therefore a "different type".
Current code results in this:
```
In [5]: df.b==2
Out[5]:
0 False
1 False
2 False
3 False
4 False
Name: b, dtype: bool
```
this is actually consistent (e.g. it returns False). On a comparison it shouldn't raise so this is a reasonable result. I think this is de-facto like the following and is useful.
```
In [7]: Series(['a','b','c'])==2
Out[7]:
0 False
1 False
2 False
dtype: bool
```
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} | 8 | 2014-12-04T16:11:53Z | 2015-06-27T01:51:57Z | 2015-06-25T13:45:32Z | CONTRIBUTOR | null | see e.g. https://buildd.debian.org/status/fetch.php?pkg=pandas&arch=sparc&ver=0.15.1%2Bgit125-ge463818-1&stamp=1417703234&file=log
```
======================================================================
FAIL: test_to_records (pandas.tests.test_categorical.TestCategoricalAsBlock)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/«BUILDDIR»/pandas-0.15.1+git125-ge463818/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_categorical.py", line 2393, in test_to_records
tm.assert_almost_equal(result,expected)
File "das/src/testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2745)
File "das/src/testing.pyx", line 93, in pandas._testing.assert_almost_equal (pandas/src/testing.c:1830)
File "das/src/testing.pyx", line 143, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2618)
AssertionError: (0L, 'a') != (0L, 'a')
```
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| null | 7 | 2014-12-04T16:14:32Z | 2014-12-05T20:40:23Z | 2014-12-04T23:31:19Z | CONTRIBUTOR | null | ```
======================================================================
FAIL: test_where (pandas.tests.test_expressions.TestExpressions)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/«BUILDDIR»/pandas-0.15.1+git125-ge463818/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_expressions.py", line 341, in test_where
testit()
File "/«BUILDDIR»/pandas-0.15.1+git125-ge463818/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_expressions.py", line 333, in testit
assert_array_equal(result,expected)
File "/usr/lib/python2.7/dist-packages/numpy/testing/utils.py", line 718, in assert_array_equal
verbose=verbose, header='Arrays are not equal')
File "/usr/lib/python2.7/dist-packages/numpy/testing/utils.py", line 644, in assert_array_compare
raise AssertionError(msg)
AssertionError:
Arrays are not equal
(mismatch 5.235%)
x: array([[-0.46795435, -1.35639322, 0. , 0. ],
[-0.03855877, -1.09147549, 0. , -2. ],
[ 1.10923046, -0.46480593, 1. , 0. ],...
y: array([[-0.46795435, -1.35639322, 0. , 0. ],
[-0.03855877, -1.09147549, 0. , -2. ],
[ 1.10923046, -0.46480593, 1. , 0. ],...
```
interestingly doesn't fail on s390x or powerpc so not strictly a BE problem
see https://buildd.debian.org/status/fetch.php?pkg=pandas&arch=sparc&ver=0.15.1%2Bgit125-ge463818-1&stamp=1417703234 for more info
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| null | 2 | 2014-12-04T16:21:50Z | 2014-12-04T23:32:34Z | 2014-12-04T23:32:23Z | CONTRIBUTOR | null | The following behavior surprised me. Of course there might be a good reason for it. Thought I'd report, feel free to close if it's not appropriate.
``` Python
In [1]: from pandas import DataFrame, Series
In [2]: s = Series([1, 2, 3], name='foo')
In [3]: DataFrame(s, columns=['bar'])
Out[3]:
Empty DataFrame
Columns: [bar]
Index: []
```
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} | 6 | 2014-12-04T17:27:03Z | 2015-10-09T13:15:29Z | 2015-10-09T13:15:29Z | CONTRIBUTOR | null | ``` python
pd.DataFrame({"A": [1]}).squeeze() # -> 1
pd.DataFrame({"A": []}).squeeze() # Noop
```
The second one is genuinely surprising; I would have expected it to return an empty Series with the name set to `A`. But it is consisent with numpy (`np.array([[], []]).squeeze()`) so I've marked is as a doc issue.
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} | 6 | 2014-12-04T18:07:54Z | 2014-12-07T21:01:06Z | 2014-12-07T21:01:06Z | NONE | null | In earlier version (0.14 and earlier), Timestamp('now', tz=xxx) will return time now in xxx time zone.
In 0.15.1, it returns the time in UTC but tagged with xxx time zone.
Example:
In version 0.12.0:
> > > import pandas
> > > pandas.version.version
> > > '0.12.0'
> > > pandas.Timestamp('now', tz='US/Central')
> > > Timestamp('2014-12-04 12:06:03-0600', tz='US/Central')
> > > pandas.Timestamp('now')
> > > Timestamp('2014-12-04 18:06:11', tz=None)
In version 0.15.1:
> > > import pandas
> > > pandas.version.version
> > > '0.15.1'
> > > pandas.Timestamp('now', tz='US/Central')
> > > Timestamp('2014-12-04 18:07:29-0600', tz='US/Central')
> > > pandas.Timestamp('now')
> > > Timestamp('2014-12-04 18:07:37')
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} | 1 | 2014-12-04T20:57:24Z | 2014-12-04T23:22:45Z | 2014-12-04T23:22:29Z | NONE | null | Although it's _my_ desired behavior, I realize perhaps this is not _the_ desired behavior, but I think it's reasonable to bring up.
If I want to concat two data frames and their column names are the same, the order is preserved:
```
>>> df1 = pd.DataFrame(columns=["A","1"])
>>> pd.concat([df1,df1])
Empty DataFrame
Columns: [A, 1]
Index: []
```
However, if they are not, it seems they are sorted:
```
>>> df2 = pd.DataFrame(columns=["A","2"])
>>> pd.concat([df1,df2])
Empty DataFrame
Columns: [1, 2, A]
Index: []
```
Would it be reasonable to try to preserve the order? So the output would be:
```
>>> pd.concat([df1,df2])
Empty DataFrame
Columns: [A, 1, 2]
Index: []
```
I realize that, in an ordered list, `"A"` doesn't come before `"1"` or `"2"`, but in both `df1` and `df2`, `"A"` _does_ come before `"1"` and `"2"`.
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