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} | 23 | 2015-10-12T20:11:24Z | 2016-02-15T19:22:24Z | 2016-02-12T03:10:54Z | CONTRIBUTOR | null | closes #11043
I modified the visualization documentation to use plot.<kind> instead of plot(kind=<kind>) in the examples as explained by [issue 11043](https://github.com/pydata/pandas/issues/11043).
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| null | 18 | 2015-10-12T21:40:48Z | 2021-03-25T20:38:06Z | 2021-03-25T20:38:06Z | CONTRIBUTOR | null | In below screenshot, I am scanning the database for a categorical called `classification_id` with the value `50ef44b795e6e42cd2000001` but I am getting a data-row where the categorical has the value 50ef44b795e6e42cd6000001`.
How is this possible? Note that my list of categorical is huge, more than 4 million entries, with 12 million total rows. (Yes, on average, each classification_id appears 3 times.)
<img width="931" alt="screenshot 2015-10-12 15 31 25" src="https://cloud.githubusercontent.com/assets/69774/10439330/7755e45a-70f6-11e5-9dc6-26ee7359d95a.png">
On a side note: The display of this one row of numpy in the line with `.values` at the end takes a lot of time, possibly due to large size of the Categorical, can that be avoided somehow?
Here's my required meta-data for the bug report:
pandas Version: 0.17.0
## INSTALLED VERSIONS
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.0
nose: 1.3.7
pip: 7.1.2
setuptools: 18.3.2
Cython: None
numpy: 1.10.0
scipy: 0.16.0
statsmodels: None
IPython: 4.1.0-dev
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.6
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.8
pymysql: None
psycopg2: None
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} | 1 | 2015-10-12T22:03:18Z | 2015-11-13T15:13:07Z | 2015-11-13T15:13:07Z | CONTRIBUTOR | null | xref #11304
```
items = [str(i) for i in range(40000)]
s = pd.Series(items, dtype='category')
df = pd.DataFrame({'C':s, 'data':np.random.randn(40000)})
data = df[df.C=='20']
str(data.C)
```
This renders the string of all of the categories before chopping them, see [here](https://github.com/pydata/pandas/blob/master/pandas/core/categorical.py#L1392)
This should check for a large number of categories first, then render the chopped portions.
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| null | 4 | 2015-10-13T05:40:09Z | 2016-05-27T01:53:08Z | 2015-10-14T11:44:49Z | NONE | null | I am trying to get [Google Analytics](http://pandas.pydata.org/pandas-docs/version/0.17.0/remote_data.html#remote-data-ga) pulling in data but am experiencing many problems. I'll outline them here.
**My Setup**: I am using Pandas '0.17.0' and Python 3.4.2 32-bit.
1. The Google Analytics console credentials screen no longer has "Installed application" application type as an option. At this point in time, even [Google's documentation](https://developers.google.com/analytics/devguides/reporting/core/v3/quickstart/installed-py) is not up-to-date. Options are now:
- **API key**
- Server key
- Browser key
- Android key
- iOS key
- **OAuth 2.0 client ID**
- Web application
- Android
- Chrome App
- iOS
- PlayStation 4
- Other
- **Service account**
- JSON
- P12
The feedback here is, pandas documentation says to choose "Installed Application" but now that Google has changed things, what should a user setup given that "Installed Application" is no longer an option? My best guess is OAuth 2.0 client ID > Other. This will produce a JSON for download.
2. As instruction say, I downloaded JSON file renamed it to 'client_secrets.json' and moved it to `C:\Python34\Lib\site-packages\pandas\io` on my machine.
3. I then try to import as follows `import pandas.io.ga as ga`. Error given: `ImportError: No module named 'apiclient'`. No problem. I understand this to mean I don't have the Google Analytics modules. So, `pip install --upgrade google-api-python-client` then try to import again.
4. Again, `import pandas.io.ga as ga`. Error given: `ImportError: No module named 'gflags'`. OK, so I need a module named gflags apparently. After seeing [this post on StackOverflow](http://stackoverflow.com/questions/29148413/execution-of-google-perflkit-failing-due-to-syntax-error-in-packages-gflags-py), it appears gflags might not support Python 3.X.
5. I do a search for gflags and learn it's some sort of [command-line flags module](https://github.com/google/python-gflags) that hasn't been updated in 4 years (at the time of this writing). OK... let's give it a shot I guess. `pip install python-gflags`. That ended up installing `python-gflags-2.0`.
6. Once again, `import pandas.io.ga as ga`. Error:
`File "C:\Python34\lib\site-packages\gflags.py", line 1091 except gflags_validators.Error, e:`. So I did some more research and found this post describing the [except gflags_validators.Error, e](https://github.com/google/python-gflags/issues/20) error. This kind of confirms that `gflags` is for Python 2.X (not that I needed that confirmation since it clearly states it on the [pypi page](https://pypi.python.org/pypi/python-gflags)). I guess I could run 2to3 but I'll try to fix them manually.
- Line 1091: `except gflags_validators.Error, e:` ---to--- `except gflags_validators.Error as e:`
- Line 1270: `except getopt.GetoptError, e:` ---to--- `except getopt.GetoptError as e:`
- Line 1573: `except IOError, e_msg:` ---to--- `except IOError as e_msg:`
- Line 1886: `except ValueError, e:` ---to--- `except ValueError as e:`
- Lines 2399, 2401, 2402, 2429, 2431, 2432 ---- > Wrap print statements in parentheses.
7EVEN. Trying again, `import pandas.io.ga as ga` and it works with no errors.
8IGHT. Great. Now I can finally try GA:
``` python
>>> ga.read_ga(
account_id = "23659189",
property_id = "UA-23659189-1",
metrics = ['users', 'pageviews'],
dimensions = ['dayOfWeek'],
start_date = "2014-01-01",
end_date = "2014-08-01",
)
Traceback (most recent call last):
File "<pyshell#10>", line 7, in <module>
end_date = "2014-08-01",
File "C:\Python34\lib\site-packages\pandas\io\ga.py", line 104, in read_ga
reader = GAnalytics(**reader_kwds)
File "C:\Python34\lib\site-packages\pandas\io\ga.py", line 173, in __init__
self._service = self._init_service(secrets)
File "C:\Python34\lib\site-packages\pandas\io\ga.py", line 185, in _init_service
http = self.authenticate(secrets)
File "C:\Python34\lib\site-packages\pandas\io\ga.py", line 145, in authenticate
return auth.authenticate(flow, self.token_store)
File "C:\Python34\lib\site-packages\pandas\io\auth.py", line 108, in authenticate
credentials = tools.run(flow, storage)
AttributeError: 'module' object has no attribute 'run'
```
9INE. I look in `"C:\Python34\lib\site-packages\pandas\io\auth.py"` and notice line 108 is `credentials = tools.run(flow, storage)`. `tools` is imported by `import oauth2client.tools as tools`, so I open up `oauth2client.tools` and go to line 115 which has a function named `run_flow`. I don't see a function named `run`. The doc strings seem to also reference the function `run()` so perhaps this name has changed. So, in line 108 of `pandas\io\auth.py` I change `credentials = tools.run(flow, storage)` to `credentials = tools.run_flow(flow, storage)`. The problem now is that run_flow has a required parameter `flags` _and I have no idea what to do next_... **I'm pretty sure `flags` is supposed to be used in a command-line fashion but I'm trying to use it within Python IDLE and by calling ga.read_ga().**
``` python
def run_flow(flow, storage, flags, http=None):
"""Core code for a command-line application.
The ``run()`` function is called from your application and runs
through all the steps to obtain credentials. It takes a ``Flow``
argument and attempts to open an authorization server page in the
user's default web browser. The server asks the user to grant your
application access to the user's data. If the user grants access,
the ``run()`` function returns new credentials. The new credentials
are also stored in the ``storage`` argument, which updates the file
associated with the ``Storage`` object.
It presumes it is run from a command-line application and supports the
following flags:
``--auth_host_name`` (string, default: ``localhost``)
Host name to use when running a local web server to handle
redirects during OAuth authorization.
``--auth_host_port`` (integer, default: ``[8080, 8090]``)
Port to use when running a local web server to handle redirects
during OAuth authorization. Repeat this option to specify a list
of values.
``--[no]auth_local_webserver`` (boolean, default: ``True``)
Run a local web server to handle redirects during OAuth
authorization.
The tools module defines an ``ArgumentParser`` the already contains the
flag definitions that ``run()`` requires. You can pass that
``ArgumentParser`` to your ``ArgumentParser`` constructor::
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter,
parents=[tools.argparser])
flags = parser.parse_args(argv)
Args:
flow: Flow, an OAuth 2.0 Flow to step through.
storage: Storage, a ``Storage`` to store the credential in.
flags: ``argparse.Namespace``, The command-line flags. This is the
object returned from calling ``parse_args()`` on
``argparse.ArgumentParser`` as described above.
http: An instance of ``httplib2.Http.request`` or something that
acts like it.
Returns:
Credentials, the obtained credential.
"""
```
Am I doing something wrong? Should I be changing `run()` to `run_flow()`? If it's supposed to be `run_flow()`, what do I change the `flags` argument to in `pandas\io\auth.py`?
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} | 2 | 2015-10-13T11:24:31Z | 2015-11-23T05:17:15Z | 2015-10-14T00:09:21Z | CONTRIBUTOR | null | not supported / tested in quite some time. this should be spun off into a separate package.
let's deprecate at least in 0.17.1.
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} | 0 | 2015-10-13T11:43:48Z | 2015-10-13T13:57:40Z | 2015-10-13T13:57:40Z | CONTRIBUTOR | null | closes #11302
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| null | 1 | 2015-10-13T12:18:19Z | 2017-09-19T18:52:04Z | 2017-09-19T18:52:04Z | NONE | null | While working with cyclic data, Pandas failed to calculate the right limits for the plot. I am not sure which exact characteristic of my data causes the problem, but I will observe if further when I have time for it. Until then, here is the notebook in which the error occurred:
http://nbviewer.ipython.org/gist/AKuederle/d7837facee0479beb6cf
The first plot is the pandas plot, which has the wrong x-limits.
The second plot is the same (or at least I thought) the same plot using matplotlib directly.
The third plot shows, that it padas issue is indeed the x-limits and not something else in plotting process
Edit:
Used package Versions:
```
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Windows
OS-release: 8
machine: AMD64
processor: AMD64 Family 16 Model 10 Stepping 0, AuthenticAMD
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.16.2
nose: 1.3.7
Cython: 0.23.3
numpy: 1.9.3
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.2.3
patsy: 0.4.0
dateutil: 2.4.1
pytz: 2015.6
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 2.0.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.5
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.8
pymysql: None
psycopg2: None
```
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| null | 9 | 2015-10-13T12:42:06Z | 2015-10-13T14:39:19Z | 2015-10-13T13:08:17Z | NONE | null | ```
dic = {'01/31/2015': {'A': 5, 'B': 10, 'C': 15},
'02/28/2015': {'A': 1, 'B': 20, 'C': 2}}
df = pd.DataFrame.from_dict(dic, orient='index')
df * 100 / df.sum(axis=1)
Out[33]:
01/31/2015 02/28/2015 A B C
01/31/2015 NaN NaN NaN NaN NaN
02/28/2015 NaN NaN NaN NaN NaN
```
What used to occur here was this, which I assume is expected behavior:
```
df.apply(lambda x: x * 100 / x.sum(), axis=1)
Out[34]:
A C B
01/31/2015 16.666667 50.000000 33.333333
02/28/2015 4.347826 8.695652 86.956522
```
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} | 3 | 2015-10-13T13:33:32Z | 2020-03-31T05:30:09Z | null | MEMBER | null | I didn't directly found an open issue about it, but is there a reason we do not implement interpolation of datetime _values_ (so not in the index)?
Example:
```
In [14]: s = pd.Series(pd.date_range('2012-01-01', periods=5))
In [16]: s[2] = np.nan
In [17]: s
Out[17]:
0 2012-01-01
1 2012-01-02
2 NaT
3 2012-01-04
4 2012-01-05
dtype: datetime64[ns]
In [18]: s.interpolate()
Out[18]:
0 2012-01-01
1 2012-01-02
2 NaT
3 2012-01-04
4 2012-01-05
dtype: datetime64[ns]
```
A crude manual work around:
```
In [20]: s2 = pd.Series(s.values.astype('int64'))
In [21]: s2[s2<0] = np.nan
In [22]: s2
Out[22]:
0 1.325376e+18
1 1.325462e+18
2 NaN
3 1.325635e+18
4 1.325722e+18
dtype: float64
In [23]: pd.to_datetime(s2.interpolate(), unit='ns')
Out[23]:
0 2012-01-01
1 2012-01-02
2 2012-01-03
3 2012-01-04
4 2012-01-05
dtype: datetime64[ns]
```
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} | 2 | 2015-10-13T15:26:45Z | 2015-10-13T15:58:47Z | 2015-10-13T15:50:55Z | NONE | null | Change `# duplicate columns & possible reduce dimensionaility` error message comment to `# duplicate columns & possible reduce dimensionality`.
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} | 1 | 2015-10-13T17:26:49Z | 2015-10-13T18:50:57Z | 2015-10-13T18:50:33Z | NONE | null | pandas 0.17.0 does not seem to set the freq attribute on the index anymore. I believe 0.16.2 did.
Example:
```
import pandas
import numpy
import datetime
df = pandas.DataFrame(numpy.random.randn(24, 1), columns=['a'], index=pandas.date_range(datetime.datetime(2015, 10, 1), datetime.datetime(2015,10,1,23), freq='H', tz='US/Eastern'))
new_index = pandas.date_range(datetime.datetime(2015, 10, 2), datetime.datetime(2015,10,2,23), freq='H', tz='US/Eastern')
new_df = df.set_index(new_index)
In [2]: df.index
Out[2]:
DatetimeIndex(['2015-10-01 00:00:00-04:00', '2015-10-01 01:00:00-04:00',
'2015-10-01 02:00:00-04:00', '2015-10-01 03:00:00-04:00',
'2015-10-01 04:00:00-04:00', '2015-10-01 05:00:00-04:00',
'2015-10-01 06:00:00-04:00', '2015-10-01 07:00:00-04:00',
'2015-10-01 08:00:00-04:00', '2015-10-01 09:00:00-04:00',
'2015-10-01 10:00:00-04:00', '2015-10-01 11:00:00-04:00',
'2015-10-01 12:00:00-04:00', '2015-10-01 13:00:00-04:00',
'2015-10-01 14:00:00-04:00', '2015-10-01 15:00:00-04:00',
'2015-10-01 16:00:00-04:00', '2015-10-01 17:00:00-04:00',
'2015-10-01 18:00:00-04:00', '2015-10-01 19:00:00-04:00',
'2015-10-01 20:00:00-04:00', '2015-10-01 21:00:00-04:00',
'2015-10-01 22:00:00-04:00', '2015-10-01 23:00:00-04:00'],
dtype='datetime64[ns, US/Eastern]', freq='H')
In [3]: new_df.index
Out[3]:
DatetimeIndex(['2015-10-02 00:00:00-04:00', '2015-10-02 01:00:00-04:00',
'2015-10-02 02:00:00-04:00', '2015-10-02 03:00:00-04:00',
'2015-10-02 04:00:00-04:00', '2015-10-02 05:00:00-04:00',
'2015-10-02 06:00:00-04:00', '2015-10-02 07:00:00-04:00',
'2015-10-02 08:00:00-04:00', '2015-10-02 09:00:00-04:00',
'2015-10-02 10:00:00-04:00', '2015-10-02 11:00:00-04:00',
'2015-10-02 12:00:00-04:00', '2015-10-02 13:00:00-04:00',
'2015-10-02 14:00:00-04:00', '2015-10-02 15:00:00-04:00',
'2015-10-02 16:00:00-04:00', '2015-10-02 17:00:00-04:00',
'2015-10-02 18:00:00-04:00', '2015-10-02 19:00:00-04:00',
'2015-10-02 20:00:00-04:00', '2015-10-02 21:00:00-04:00',
'2015-10-02 22:00:00-04:00', '2015-10-02 23:00:00-04:00'],
dtype='datetime64[ns, US/Eastern]', freq=None)
```
The datetime appears to have been set correctly, but the freq is = None on the new_df. Is this normal behavior with pandas 0.17.0?
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} | 6 | 2015-10-14T01:01:44Z | 2015-10-14T11:43:50Z | 2015-10-14T11:43:50Z | NONE | null | The following test case produces `IndexError: indices are out-of-bounds` with negative integers, while positive work fine:
```
testdf = pd.DataFrame({"rna": (1.5,2.2,3.2,4.5), -1000: [11,21,36,40],0: [10,22,43,34], 1000:[0, 10, 20, 30]})
testdf[[1000]] #this works
testdf[[-1000]] #this does not
```
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https://api.github.com/repos/pandas-dev/pandas/issues/11321 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11321/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11321/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11321/events | https://github.com/pandas-dev/pandas/issues/11321 | 111,360,328 | MDU6SXNzdWUxMTEzNjAzMjg= | 11,321 | Can't import pandas. Matplotlib and distutils conflict. | {
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| null | 1 | 2015-10-14T09:40:50Z | 2015-10-14T10:41:13Z | 2015-10-14T10:40:51Z | NONE | null | Trying to import pandas
``` python
In [1]: import pandas
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-1-d6ac987968b6> in <module>()
----> 1 import pandas
/usr/local/lib/python2.7/site-packages/pandas/__init__.pyc in <module>()
42 import pandas.core.config_init
43
---> 44 from pandas.core.api import *
45 from pandas.sparse.api import *
46 from pandas.stats.api import *
/usr/local/lib/python2.7/site-packages/pandas/core/api.py in <module>()
7 from pandas.core.common import isnull, notnull
8 from pandas.core.categorical import Categorical
----> 9 from pandas.core.groupby import Grouper
10 from pandas.core.format import set_eng_float_format
11 from pandas.core.index import Index, CategoricalIndex, Int64Index, Float64Index, MultiIndex
/usr/local/lib/python2.7/site-packages/pandas/core/groupby.py in <module>()
14 from pandas.core.base import PandasObject
15 from pandas.core.categorical import Categorical
---> 16 from pandas.core.frame import DataFrame
17 from pandas.core.generic import NDFrame
18 from pandas.core.index import Index, MultiIndex, CategoricalIndex, _ensure_index
/usr/local/lib/python2.7/site-packages/pandas/core/frame.py in <module>()
39 create_block_manager_from_arrays,
40 create_block_manager_from_blocks)
---> 41 from pandas.core.series import Series
42 from pandas.core.categorical import Categorical
43 import pandas.computation.expressions as expressions
/usr/local/lib/python2.7/site-packages/pandas/core/series.py in <module>()
2862 # Add plotting methods to Series
2863
-> 2864 import pandas.tools.plotting as _gfx
2865
2866 Series.plot = base.AccessorProperty(_gfx.SeriesPlotMethods, _gfx.SeriesPlotMethods)
/usr/local/lib/python2.7/site-packages/pandas/tools/plotting.py in <module>()
133 return False
134
--> 135 if _mpl_ge_1_5_0():
136 # Compat with mp 1.5, which uses cycler.
137 import cycler
/usr/local/lib/python2.7/site-packages/pandas/tools/plotting.py in _mpl_ge_1_5_0()
128 try:
129 import matplotlib
--> 130 return (matplotlib.__version__ >= LooseVersion('1.5')
131 or matplotlib.__version__[0] == '0')
132 except ImportError:
/usr/local/lib/python2.7/distutils/version.pyc in __cmp__(self, other)
294 other = LooseVersion(other)
295
--> 296 return cmp(self.version, other.version)
297
298
AttributeError: 'unicode' object has no attribute 'version'
```
matplotlib.**version** returns unicode object, but distutils.version has this code
``` python
def __cmp__ (self, other):
if isinstance(other, StringType):
other = LooseVersion(other)
return cmp(self.version, other.version)
```
and isinstance(u'123', StringType) is False
Pandas version - 0.17.0, matplotlib version - 1.4.0
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} | 0 | 2015-10-14T10:55:15Z | 2015-10-14T11:43:50Z | 2015-10-14T11:43:50Z | CONTRIBUTOR | null | closes #11320
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https://api.github.com/repos/pandas-dev/pandas/issues/11323 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11323/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11323/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11323/events | https://github.com/pandas-dev/pandas/issues/11323 | 111,377,818 | MDU6SXNzdWUxMTEzNzc4MTg= | 11,323 | BUG: merge on should accept Series | {
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} | 1 | 2015-10-14T11:19:01Z | 2018-06-28T11:39:08Z | 2018-06-28T11:39:08Z | CONTRIBUTOR | null | Prob need a better example, but xref #2996
these should accept Series (and align)!
```
In [47]: x = DataFrame({'A' : list('aabb'), 'B' : [1,2,3,4]})
In [48]: x.merge(x, left_on=['A','B'], right_on=['A',x['B']], how='left')
TypeError: 'Series' does not have the buffer interface
In [49]: x.merge(x, left_on=['A','B'], right_on=['A','B'], how='left')
Out[49]:
A B
0 a 1
1 a 2
2 b 3
3 b 4
```
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} | 7 | 2015-10-14T11:52:40Z | 2015-11-13T21:48:37Z | 2015-11-13T21:48:37Z | NONE | null | Exception is raised when
a) the original dataframe has a datetime column
b) the groupby.apply function returns a series object with a new datetime column
Code to reproduce:
```
import pandas as pd
import datetime
df = pd.DataFrame([['1', datetime.datetime.today()],
['2', datetime.datetime.today()],
['2', datetime.datetime(2010, 1, 1)]],
columns=['record', 'date'])
dd = df.groupby('record').apply(lambda x: pd.Series({'max_date': x['date'].max()}))
```
This is a new issue affecting 0.17
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} | 16 | 2015-10-14T13:00:55Z | 2015-10-28T11:58:03Z | 2015-10-28T11:13:27Z | NONE | null | closes #11269
This will make itertuples return namedtuples. I'm not sure about tests, here. Since `namedtuple` is a drop-in replacement for ordinary tuples (once they are created) I naively expect things to work.
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} | 2 | 2015-10-14T15:03:33Z | 2015-10-17T15:04:49Z | 2015-10-17T15:04:49Z | CONTRIBUTOR | null | Example:
```
>>> df = extended_df
Traceback (most recent call last):
File "/Users/josh/anaconda/envs/Openfolio/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 3066, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-14-3f216cf6821e>", line 1, in <module>
df = extended_df
NameError: name 'extended_df' is not defined
>>> df = expanded
>>> df
Out[16]:
pricing_date exchange_id close
pricing_date_ts
1445025600 2015-10-13 20:00:00+00:00 NaN NaN
1444939200 2015-10-13 20:00:00+00:00 NaN NaN
1444852800 2015-10-13 20:00:00+00:00 0 NaN
1444766400 2015-10-13 20:00:00+00:00 6 0.545
1444680000 2015-10-12 20:00:00+00:00 6 0.570
1444420800 2015-10-09 20:00:00+00:00 6 0.580
1444334400 2015-10-08 20:00:00+00:00 6 0.560
1444248000 2015-10-07 20:00:00+00:00 6 0.580
1444161600 2015-10-06 20:00:00+00:00 6 0.620
1444075200 2015-10-05 20:00:00+00:00 6 0.480
1443816000 2015-10-13 20:00:00+00:00 NaN NaN
>>> df.dtypes
Out[17]:
pricing_date datetime64[ns, UTC]
exchange_id float64
close float64
dtype: object
>>> df.replace(0, np.NaN)
Traceback (most recent call last):
File "/Users/josh/anaconda/envs/Openfolio/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 3066, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-18-cc26b9ec3ff7>", line 1, in <module>
df.replace(0, np.NaN)
File "/Users/josh/anaconda/envs/Openfolio/lib/python2.7/site-packages/pandas/core/generic.py", line 2996, in replace
inplace=inplace, regex=regex)
File "/Users/josh/anaconda/envs/Openfolio/lib/python2.7/site-packages/pandas/core/internals.py", line 2761, in replace
return self.apply('replace', **kwargs)
File "/Users/josh/anaconda/envs/Openfolio/lib/python2.7/site-packages/pandas/core/internals.py", line 2710, in apply
applied = getattr(b, f)(**kwargs)
File "/Users/josh/anaconda/envs/Openfolio/lib/python2.7/site-packages/pandas/core/internals.py", line 560, in replace
mask = com.mask_missing(self.values, to_replace)
File "/Users/josh/anaconda/envs/Openfolio/lib/python2.7/site-packages/pandas/core/common.py", line 449, in mask_missing
mask = arr == x
File "/Users/josh/anaconda/envs/Openfolio/lib/python2.7/site-packages/pandas/tseries/index.py", line 84, in wrapper
other = _ensure_datetime64(other)
File "/Users/josh/anaconda/envs/Openfolio/lib/python2.7/site-packages/pandas/tseries/index.py", line 111, in _ensure_datetime64
raise TypeError('%s type object %s' % (type(other), str(other)))
TypeError: <type 'int'> type object 0
```
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} | 8 | 2015-10-14T16:13:51Z | 2015-10-17T03:33:38Z | 2015-10-16T16:40:27Z | CONTRIBUTOR | null | closes #11247
This is my first time contributing through PR so forgive me if I make any silly mistake.
The issue can be referred to in GH11247
I followed @jreback 's suggestion to box things up in `to_dict()` but I am not sure if this is what he means because I do not know exactly when `_maybe_datetime()` should be used. Initially I tried to tweak `internals.py` but it fails.
Let me know how I can improve from here. Hope I can really learn from my first PR.
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closes #11408
This includes updates to 3 Excel files, plus a test in test_excel.py,
plus the fix in parsers.py
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} | 1 | 2015-10-14T19:26:29Z | 2015-10-17T15:04:49Z | 2015-10-17T15:04:49Z | CONTRIBUTOR | null | closes #11326
xref #11153
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| null | 7 | 2015-10-15T00:22:32Z | 2015-12-30T15:21:05Z | 2015-12-30T15:21:00Z | CONTRIBUTOR | null | From [SO](http://stackoverflow.com/questions/33137686/python-loading-zip-codes-into-a-dataframe-as-strings/33137797#33137797)
``` python
In [16]: df = pd.DataFrame({'a': ['001','002']})
In [17]: df.to_excel('temp.xlsx')
In [18]: pd.read_excel('temp.xlsx')
Out[18]:
a
0 1
1 2
```
I think it would probably make sense for `read_excel` to not try and convert strings to numeric, or at least have another keyword argument.
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| null | 4 | 2015-10-15T08:57:20Z | 2015-11-05T18:38:37Z | 2015-11-05T18:38:37Z | NONE | null | Testing pandas 0.17:
``` python
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-57-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_AU.UTF-8
pandas: 0.17.0
nose: 1.3.7
pip: 7.1.2
setuptools: 17.1.1
Cython: 0.22
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: None
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.6
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 2.2.3
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: 1.0b6
httplib2: 0.9.1
apiclient: 1.4.0
sqlalchemy: 1.0.5
pymysql: None
psycopg2: None
```
nosetests pandas fail at:
`test_int64_overflow (pandas.io.tests.test_parsers.TestCParserHighMemory) ... Segmentation fault (core dumped)`
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| null | 4 | 2015-10-15T12:18:39Z | 2021-04-20T05:36:43Z | null | NONE | null | Series.hist accepts arguments such as xlim, ylim because it uses Series.plot internally, but DataFrame.hist doesn't, as it forwards the arguments to pyplot.hist directly.
I don't know about other arguments, but I expected at least xlim/ylim to work.
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} | 6 | 2015-10-15T15:20:48Z | 2016-02-12T15:01:29Z | 2016-02-12T15:01:29Z | CONTRIBUTOR | null | I think this easy-to-make mistake should raise a more descriptive error... maybe something like
``` python
ValueError: Did you mean to supply a 'sep' keyword?
```
I might be missing the main usage of `str.cat()`, but all the uses I've found involve concatenating series together with a sep character.
``` python
In [1]: import pandas as pd
In [2]: s = pd.Series(['foo', 'bar', 'baz'])
In [3]: s.str.cat('|')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-8bc5f2a1347b> in <module>()
----> 1 s.str.cat('|')
/home/charles.hack/anaconda/envs/seahydra/lib/python2.7/site-packages/pandas/core/strings.pyc in cat(self, others, sep, na_rep)
1137 @copy(str_cat)
1138 def cat(self, others=None, sep=None, na_rep=None):
-> 1139 result = str_cat(self.series, others=others, sep=sep, na_rep=na_rep)
1140 return self._wrap_result(result)
1141
/home/charles.hack/anaconda/envs/seahydra/lib/python2.7/site-packages/pandas/core/strings.pyc in str_cat(arr, others, sep, na_rep)
71 arrays = _get_array_list(arr, others)
72
---> 73 n = _length_check(arrays)
74 masks = np.array([isnull(x) for x in arrays])
75 cats = None
/home/charles.hack/anaconda/envs/seahydra/lib/python2.7/site-packages/pandas/core/strings.pyc in _length_check(others)
111 if n is None:
112 n = len(x)
--> 113 elif len(x) != n:
114 raise ValueError('All arrays must be same length')
115
TypeError: len() of unsized object
In [4]: s.str.cat(sep='|')
Out[4]: 'foo|bar|baz'
```
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| null | 1 | 2015-10-15T18:45:05Z | 2015-11-18T20:15:48Z | 2015-11-18T20:15:48Z | NONE | null | It is recommended by Google that we use Service Account credentials make
API calls from server. See [this issue comment](https://github.com/pydata/pandas/issues/8489#issuecomment-148466759) for details.
Docs and tests are not yet updated. DO NOT MERGE.
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| null | 5 | 2015-10-15T18:55:32Z | 2015-10-16T15:15:09Z | 2015-10-15T19:48:03Z | CONTRIBUTOR | null | Older versions up to 0.16.2:
``` python
import pandas as pd
print(pd.__version__)
index = pd.date_range(start='2000-1-1', end='2000-1-3')
pd.DataFrame({1: [3, 3, 3], 2: [6, 6, 6]}, index=index) / pd.Series([2, 2, 2], index=index)
```
```
0.14.0rc1-33-g8c1f550
1 2
2000-01-01 1.5 3
2000-01-02 1.5 3
2000-01-03 1.5 3
```
0.17:
```
0.17.0
2000-01-01 00:00:00 2000-01-02 00:00:00 2000-01-03 00:00:00 1 2
2000-01-01 NaN NaN NaN NaN NaN
2000-01-02 NaN NaN NaN NaN NaN
2000-01-03 NaN NaN NaN NaN NaN
```
Work-around:
Use `df.divide(series, axis='index')`.
Is this change in API intentional? Couldn't find it in the release announcements.
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| null | 6 | 2015-10-15T19:37:04Z | 2015-10-15T20:15:07Z | 2015-10-15T19:57:41Z | NONE | null | When I am trying to do `rows.sort_index(inplace = True)` I get SettingWithCopyWarning:
`A value is trying to be set on a copy of a slice from a DataFrame`
`rows = rows.sort_index()` can be used as a workaround to avoid the warning.
Pandas version 0.17.0 installed by pip.
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| null | 4 | 2015-10-15T21:01:35Z | 2015-10-16T00:44:30Z | 2015-10-15T22:12:15Z | CONTRIBUTOR | null | Looking here: https://github.com/pydata/pandas/pull/11325/files#diff-1e79abbbdd150d4771b91ea60a4e1cc7R686, #11325
...currently `itertuples` is building a new full objects in memory and then creating an iterator over those objects. Because it's building a new tuple for each row, these aren't views (I think). Should these instead yield a tuple / row / item on each call, using a generator?
The upside is that you don't need to rebuild the object in memory at once. The downside is that it is dependent on the underlying data (as though it were a view).
(and possible there is something going on that I don't understand, either with pandas or generators...)
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} | 6 | 2015-10-15T23:38:35Z | 2016-07-16T22:40:39Z | 2015-10-16T21:51:18Z | NONE | null | Just updated to 0.17.0 (python 2.7.9 on OS X Yosemite) and I found the following:
```
>>> import pandas as pd
>>> pd.__version__
u'0.17.0'
>>> s = pd.Series([(1,1),(1,2)])
>>> s==(1,2)
0 False
1 False
dtype: bool
```
Meanwhile in 0.16.2:
```
>>> import pandas as pd
>>> pd.__version__
'0.16.2'
>>> s = pd.Series([(1,1),(1,2)])
>>> s==(1,2)
0 False
1 True
dtype: bool
```
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} | 1 | 2015-10-16T02:49:53Z | 2016-07-21T16:02:53Z | 2015-10-16T07:53:21Z | CONTRIBUTOR | null | Closes #4903
This seemed to be fixed already, so just adding tests
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| null | 1 | 2015-10-16T13:53:42Z | 2015-10-16T14:36:51Z | 2015-10-16T14:36:42Z | NONE | null | Unstacking with an integer level seems to raise a FutureWarning from numpy. I'm using pandas version 0.17.0 and numpy version 1.10.1.
```
In[14]: d.unstack('ticker')
Out[14]:
factor close open
ticker BMO TD BMO TD
date
2005-08-01 61.10 55.90 61.75 56.60
2005-08-02 62.15 56.77 61.18 55.80
2005-08-03 61.40 55.79 61.50 55.49
2005-08-04 59.09 54.81 60.96 55.15
... ... ... ...
2015-10-09 75.51 53.25 76.09 53.70
2015-10-12 75.51 53.25 76.09 53.70
2015-10-13 75.35 52.97 75.51 52.95
2015-10-14 74.41 52.37 75.31 53.06
[2663 rows x 4 columns]
In[15]: d.unstack(1)
C:\Anaconda3\envs\pyq\lib\site-packages\pandas\core\index.py:4281: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
return np.sum(name == np.asarray(self.names)) > 1
Out[15]:
factor close open
ticker BMO TD BMO TD
date
2005-08-01 61.10 55.90 61.75 56.60
2005-08-02 62.15 56.77 61.18 55.80
2005-08-03 61.40 55.79 61.50 55.49
2005-08-04 59.09 54.81 60.96 55.15
... ... ... ...
2015-10-09 75.51 53.25 76.09 53.70
2015-10-12 75.51 53.25 76.09 53.70
2015-10-13 75.35 52.97 75.51 52.95
2015-10-14 74.41 52.37 75.31 53.06
[2663 rows x 4 columns]
```
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| null | 3 | 2015-10-16T14:18:04Z | 2015-10-21T10:29:46Z | 2015-10-21T10:29:46Z | NONE | null | I'm having problems with building a package depending on Pandas behind proxy, because setup_requires is specified in setup.py.
"pip install" calls setup.py egg_info, which in turn wants numpy, which it cannot find. Numpy is installed along with Pandas by pip, but setup.py can't know about this.
The same issue would also appear if I'd want install an offline package for the app I'm using.
This is why [pip documentation discourages setup_requires](https://pip.readthedocs.org/en/stable/user_guide/#ensuring-repeatability)
I see that there was a request opposite to mine (#3861) to make it usable with buildout... But maybe there's another way to use it with buildout? I think that pip is more of a Python standard than buildout...
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} | 12 | 2015-10-16T14:22:08Z | 2015-11-13T21:17:44Z | 2015-11-13T16:05:41Z | MEMBER | null | Closes #10089.
- `value` can only accept scalar.
- `MultiIndex.fillna` raises `NotImplementedError` because `isnull` is not defined for MI.
- Moved `hasnans` and related properties to base `Index`.
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} | 22 | 2015-10-16T14:22:17Z | 2018-09-11T19:41:33Z | 2015-10-23T16:57:20Z | NONE | null | I have a 1.1GB json file that I try to load with `pandas.json.load`:
```
import pandas as pd
with open('/tmp/file.json') as fileh:
text = pd.json.load(fileh)
```
It breaks with the following output
```
Error in `python3': double free or corruption (out): 0x00007ffe082171f0 ***
```
I can load the file with Pythons built-in json module. What is going wrong here?
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} | 0 | 2015-10-16T16:01:39Z | 2015-10-16T21:51:18Z | 2015-10-16T21:51:18Z | CONTRIBUTOR | null | closes #11339
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| null | 12 | 2015-10-16T16:41:59Z | 2016-03-01T17:33:08Z | 2015-10-16T16:53:50Z | NONE | null | Strange behaviour that appears only with a large number of rows.
``` python
import pandas as pd
import numpy as np
np.random.seed(0)
data = pd.DataFrame(np.random.rand(500000, 4))
def sum_products(data):
data_np = sum(data.values[:,0] * data.values[:,1] * data.values[:,2] * data.values[:,3])
data_pd = sum(data.ix[:,0] * data.ix[:,1] * data.ix[:,2] * data.ix[:,3])
return [data_np, data_pd]
calls = []
for _ in range(25):
test = sum_products(data)
calls.append(test)
print('[Numpy, Pandas]', calls )
Out[6]:
[Numpy, Pandas]
[[31234.084575876517, 31234.084575876517],
[31234.084575876517, 31234.084575876517],
[31234.084575876517, 31234.084575876517],
[31234.084575876517, 0.0],
[31234.084575876517, 31234.084575876517],
[31234.084575876517, 31234.084575876517],
[31234.084575876517, 31234.084575876517],
[31234.084575876517, 31234.084575876517],
[31234.084575876517, 0.0],
[31234.084575876517, 31234.084575876517],
[31234.084575876517, 31234.084575876517],
[31234.084575876517, 31234.084575876517],
[31234.084575876517, 31234.084575876517],
[31234.084575876517, 31234.084575876517],
[31234.084575876517, 31234.084575876517],
[31234.084575876517, 0.0],
[31234.084575876517, 31234.084575876517],
[31234.084575876517, 31234.084575876517],
[31234.084575876517, 31234.084575876517],
[31234.084575876517, 31234.084575876517],
[31234.084575876517, 31234.084575876517],
[31234.084575876517, 31234.084575876517],
[31234.084575876517, 31234.084575876517],
[31234.084575876517, 31234.084575876517],
[31234.084575876517, 31234.084575876517]]
```
This bug appears randomly, but using this combination (4 columns, 500k rows and 25 repetitions) I can see it at least once almost all the executions.
I have tried several `Pandas` versions (0.13-0.17) with `Python 3.4` and the bug is still there.
HOWEVER, using `Python 2.7` it works prefectly (at least after several executions).
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Windows
OS-release: 8
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: es_ES
pandas: 0.17.0
nose: 1.3.7
pip: 7.1.2
setuptools: 18.4
Cython: 0.23.3
numpy: 1.10.1
scipy: 0.16.0
statsmodels: None
IPython: 4.0.0
sphinx: 1.2.3
patsy: 0.4.0
dateutil: 2.4.1
pytz: 2015.6
blosc: None
bottleneck: None
tables: None
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 2.0.2
xlrd: 0.9.4
xlwt: None
xlsxwriter: 0.7.5
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.8
pymysql: None
psycopg2: None
```
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| null | 4 | 2015-10-16T23:22:46Z | 2015-10-18T02:32:14Z | 2015-10-17T15:11:24Z | CONTRIBUTOR | null | I don't know if this is possible with the current pytables implementation/interface, but I would find it useful to be able to do filtering like this on disk:
``` python
df = pd.read_hdf(dbname, 'df', where='colname in ["a","b"]')
```
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} | 2 | 2015-10-16T23:35:39Z | 2015-12-31T13:46:31Z | 2015-12-31T13:46:31Z | CONTRIBUTOR | null | Consider the following Series:
```
object_id
0CKVYKjyFn 76 days
0CrPL2QKH3 -15 days
0CrVStlVrg 23 days
0Cc5ZvS67u 76 days
0CTOk5OdtI 76 days
0CTSWtTzBa 76 days
0CwBqVeNCX 76 days
0CIRJFIOcD 58 days
0CRQPCxzQe 350 days
0CAq4m9Nru 15 days
0C617yvXBj 76 days
0CzUUJNKX9 -16 days
Name: days_left, dtype: timedelta64[ns]
```
I am hoping to convert the above to hours.
If I do:
```
my_series.dt.hours
```
I get:
```
AttributeError: 'Series' object has no attribute 'hours
```
What's even more strange is that if I do:
``` python
> my_series[0].total_seconds()/3600
1824.0
```
it works for one element, but if I do:
```
> my_series.apply(lambda x: x.total_seconds())
```
I get:
```
AttributeError: 'numpy.timedelta64' object has no attribute 'total_seconds'
```
I thought `apply` would run the function I pass it item by item in the series. Why does `total_seconds()` work for a single item, but not with `apply`?
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| null | 0 | 2015-10-17T00:16:25Z | 2021-04-20T05:37:17Z | null | MEMBER | null | Create `IndexFormatter` like `Series` and `DataFrame`.
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| null | 5 | 2015-10-17T00:29:24Z | 2018-06-29T05:24:34Z | 2018-06-29T05:24:34Z | MEMBER | null | Currently there are some inconsistent results. We should clarify the policy first.
```
import pandas as pd
s = pd.Series([pd.Timestamp('2011-01-01'), pd.NaT])
s
#0 2011-01-01
#1 NaT
# dtype: datetime64[ns]
```
#### 1. coerced to object dtype
I prefer this behavior.
```
s.fillna(pd.Timestamp('2011-01-02', tz='Asia/Tokyo'))
#0 2011-01-01 00:00:00
#1 2011-01-02 00:00:00+09:00
# dtype: object
```
#### 2. converted to GMT
```
s[1] = pd.Timestamp('2011-01-02', tz='Asia/Tokyo')
s
#0 2011-01-01 00:00:00
#1 2011-01-01 15:00:00
# dtype: datetime64[ns]
```
#### 3. raise explicit error
This looks second best.
```
stz = pd.Series(pd.DatetimeIndex(['2012-01-01', '2012-01-02'], tz='Asia/Tokyo'))
stz
#0 2012-01-01 00:00:00+09:00
#1 2012-01-02 00:00:00+09:00
# dtype: datetime64[ns, Asia/Tokyo]
pd.Index(s).union(pd.Index(stz))
# TypeError: Cannot join tz-naive with tz-aware DatetimeIndex
```
#### 4. not handled properly (bug)
```
s.append(stz)
# AttributeError: 'numpy.ndarray' object has no attribute 'tz_localize'
```
-> Split to #11455, closed by #12195.
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} | 5 | 2015-10-17T01:20:27Z | 2015-10-21T22:41:46Z | 2015-10-17T15:08:44Z | CONTRIBUTOR | null | Adds new functions to check for infinity rather than calling `np.isposinf` and `np.isneginf`, which were (surprising to me) a significant drag on `to_excel`.
I also added `xlsxwriter` to the asv build configuration. `openpyxl` is still failing, I'm assuming something do with the specific version on conda?
```
before after ratio
[472e6e0e] [8002555d]
156.36ms 154.16ms 0.99 packers.packers_read_excel.time_packers_read_excel
failed failed n/a packers.packers_write_excel_openpyxl.time_packers_write_excel_openpyxl
469.69ms 357.49ms 0.76 packers.packers_write_excel_xlsxwriter.time_packers_write_excel_xlsxwriter
368.96ms 270.60ms 0.73 packers.packers_write_excel_xlwt.time_packers_write_excel_xlwt
```
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| null | 1 | 2015-10-17T05:06:03Z | 2015-10-17T14:50:42Z | 2015-10-17T14:50:34Z | NONE | null | After creating a dataframe from excel and then deleting some rows by column value like
```
df = df[df.Name != "TBC"]
```
when need to iterate through the value of a column it throws key error as shown below.
It works fine if I do not delete the rows.
"
---> 38 print df["Country"][19]
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/series.pyc in **getitem**(self, key)
549 def **getitem**(self, key):
550 try:
--> 551 result = self.index.get_value(self, key)
552
553 if not np.isscalar(result):
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/index.pyc in get_value(self, series, key)
1721
1722 try:
-> 1723 return self._engine.get_value(s, k)
1724 except KeyError as e1:
1725 if len(self) > 0 and self.inferred_type in ['integer','boolean']:
pandas/index.pyx in pandas.index.IndexEngine.get_value (pandas/index.c:3204)()
pandas/index.pyx in pandas.index.IndexEngine.get_value (pandas/index.c:2903)()
pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3843)()
pandas/hashtable.pyx in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:6525)()
pandas/hashtable.pyx in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:6463)()
KeyError: 19
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} | 1 | 2015-10-17T10:24:11Z | 2019-11-16T22:04:45Z | 2019-11-16T22:04:45Z | MEMBER | null | From the mailing list, considering this example:
```
In [56]: s = pd.Series(range(6), pd.MultiIndex.from_product([['A', 'B'], [1,2,3]], names=['lev1', 'lev2']), name='Amount')
In [57]: s
Out[57]:
lev1 lev2
A 1 0
2 1
3 2
B 1 3
2 4
3 5
Name: Amount, dtype: int64
In [58]: df = pd.DataFrame({'lev1':list('AAABBB'), 'lev2':[1,2,3,1,2,3], 'col':0})
In [59]: df
Out[59]:
col lev1 lev2
0 0 A 1
1 0 A 2
2 0 A 3
3 0 B 1
4 0 B 2
5 0 B 3
```
So there are overlapping columns, but as the index in the series and as columns in the dataframe.
To merge both, these work:
```
In [63]: pd.concat([df.set_index(['lev1', 'lev2']), s], axis=1)
Out[63]:
col Amount
lev1 lev2
A 1 0 0
2 0 1
3 0 2
B 1 0 3
2 0 4
3 0 5
In [64]: pd.merge(df, s.reset_index(), on=['lev1', 'lev2'])
Out[64]:
col lev1 lev2 Amount
0 0 A 1 0
1 0 A 2 1
2 0 A 3 2
3 0 B 1 3
4 0 B 2 4
5 0 B 3 5
```
But I would expect this also to work:
```
In [69]: df.merge(s, left_on=['lev1', 'lev2'], right_index=True)
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-69-1b78b4267e47> in <module>()
----> 1 df.merge(s, left_on=['lev1', 'lev2'], right_index=True)
C:\Anaconda\lib\site-packages\pandas\core\frame.pyc in merge(self, right, how, o
n, left_on, right_on, left_index, right_index, sort, suffixes, copy)
4069 left_on=left_on, right_on=right_on,
4070 left_index=left_index, right_index=right_index, sor
t=sort,
-> 4071 suffixes=suffixes, copy=copy)
4072
4073 #-------------------------------------------------------------------
---
C:\Anaconda\lib\site-packages\pandas\tools\merge.pyc in merge(left, right, how,
on, left_on, right_on, left_index, right_index, sort, suffixes, copy)
36 right_index=right_index, sort=sort, suffixes=su
ffixes,
37 copy=copy)
---> 38 return op.get_result()
39 if __debug__:
40 merge.__doc__ = _merge_doc % '\nleft : DataFrame'
C:\Anaconda\lib\site-packages\pandas\tools\merge.pyc in get_result(self)
184
185 def get_result(self):
--> 186 join_index, left_indexer, right_indexer = self._get_join_info()
187
188 ldata, rdata = self.left._data, self.right._data
C:\Anaconda\lib\site-packages\pandas\tools\merge.pyc in _get_join_info(self)
254 def _get_join_info(self):
255 left_ax = self.left._data.axes[self.axis]
--> 256 right_ax = self.right._data.axes[self.axis]
257 if self.left_index and self.right_index:
258 join_index, left_indexer, right_indexer = \
IndexError: list index out of range
```
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} | 8 | 2015-10-17T15:02:57Z | 2018-02-14T00:54:07Z | null | CONTRIBUTOR | null | xref #11352
- [ ] use `to_native_types` by block to yield already formatted cells
- [ ] yield cells in row-oriented way https://github.com/pydata/pandas/pull/11144#issuecomment-141966626
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} | 0 | 2015-10-17T16:30:40Z | 2015-10-18T23:52:37Z | 2015-10-18T23:52:37Z | CONTRIBUTOR | null | closes #8537
- avoid numpy comparison to `None` warnings
- change null indexing from `ValueError` to `TypeError` (more consistent with other errors)
- elementwise comparisons with index names xref #11162
- elementwise comparisons vs string-likes
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| null | 1 | 2015-10-17T17:13:24Z | 2020-05-02T03:53:38Z | null | CONTRIBUTOR | null | xref #11328
``` python
In [100]: df = pd.DataFrame({0: range(5), 1:range(5)})
In [101]: df.to_excel('tmp.xlsx')
In [102]: df.to_csv('tmp.csv')
In [103]: pd.read_excel('tmp.xlsx', index_col=0).columns
Out[103]: Int64Index([0, 1], dtype='int64')
In [106]: pd.read_csv('tmp.csv', index_col=0).columns
Out[106]: Index([u'0', u'1'], dtype='object')
```
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} | 4 | 2015-10-17T20:32:06Z | 2015-11-13T16:20:55Z | 2015-11-13T16:20:55Z | NONE | null | Based on my discussion with Jeff
http://stackoverflow.com/questions/25714549/indexing-and-data-columns-in-pandas-pytables/25715005?noredirect=1#comment53932720_25715005
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} | 11 | 2015-10-18T03:31:36Z | 2016-09-13T10:24:03Z | 2016-09-13T10:24:02Z | NONE | null | [This line](https://github.com/pydata/pandas/blob/b2a547e8a9dd6ab3332f18a6f87ecb31181d7d30/pandas/io/gbq.py#L330) seems for me to be too strict for repeated insertion, because apparently GBQ is not consistent in the order of fields in the schema (or my application screws the order of fields up, anyway, I would say the verification is too strict).
So for example:
``` python
dict1 = {'fields': [{'name': 'coordinates_0', 'type': 'FLOAT'}, {'name': 'created_at', 'type': 'STRING'}]}
dict2 = {'fields': [{'name': 'created_at', 'type': 'STRING'}, {'name': 'coordinates_0', 'type': 'FLOAT'}]}
dict1 == dict2 # gives False
```
would make verification fail, though insertion would work, as the insert as JSON makes the order of fields irrelevant.
Solved that for myself for the moment with:
``` python
def verify_schema(self, dataset_id, table_id, schema):
from apiclient.errors import HttpError
try:
bq_schema = (self.service.tables().get(
projectId=self.project_id,
datasetId=dataset_id,
tableId=table_id
).execute()['schema'])
return set(
[json.dumps(x) for x in bq_schema['fields']] # dump necessary to make dicts hashable
) == set(
[json.dumps(x) for x in schema['fields']]
) # this still fails if key order is different. But GBQ seems to keep key order.
except HttpError as ex:
self.process_http_error(ex)
```
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| null | 6 | 2015-10-18T03:55:45Z | 2015-10-28T21:06:12Z | 2015-10-18T13:34:42Z | NONE | null | Hi,
Whenever I do inplace operations in pandas, especially when performing operations like replace or astype. The memory blows up close or greater than 8GB(esp on values).
Same happens when using values or concatenation of larger dataframes.
The workaroud is to do a per column operation, even on concatenation(using column addition). But since this is becoming a common issue (or at least that what I see), can algorithms for efficient memory management be added whole dataframe operations, e.g., checking the memory of the machine and then splitting the operation into chunks so that the user won't be less concerned in having memory bottlenecks when executing such pandas commands.
Thanks in advance.
Example,
``` python
allcols=allcols[0:400]
train=[eval('dftrain.'+allcols[0]).values]
del allcols[0]
for x in allcols:
train=np.append(train,[eval('dftrain.'+x).values], axis=0)
del dftrain
```
if I used dftrain.values[x,y] command, the memory usage will blow up above 8GB, and the kernel will die.
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} | 14 | 2015-10-18T04:23:02Z | 2015-11-01T10:28:53Z | 2015-11-01T10:27:53Z | CONTRIBUTOR | null | I integrated the comments in #8861 from @seth-p into the exponential weighted moment functions docs.
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} | 6 | 2015-10-18T08:36:40Z | 2015-10-18T16:03:53Z | 2015-10-18T16:02:57Z | CONTRIBUTOR | null | as requested here: https://github.com/pydata/pandas/issues/11033#issuecomment-148924778
i can’t test on windows unfortunately.
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} | 1 | 2015-10-18T21:13:54Z | 2019-12-18T22:20:12Z | 2019-12-18T22:20:12Z | CONTRIBUTOR | null | This would be in symmetry with the existing support of DataFrame(np.array) and DataFrame(Series).
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| null | 4 | 2015-10-18T21:20:58Z | 2015-10-19T05:50:27Z | 2015-10-18T23:20:02Z | NONE | null | The following example works with MultiIndex, but fails with simple index, which is bad in terms of generalization / intuitive behaviour:
```
#Create example:
col_nums = [0]
df = pd.DataFrame({"V1":["a","b","c","d","e", "aaaah!!!"],
"W":["c","d","c","d","c","c"],
"data":np.arange(6)})
df.set_index(["V1","W"], inplace = True)
df.to_csv("testtable.tab",sep = "\t")
```
```
# MulitIndex -> works:
sep = "\t"
indexcols =[0,1]
chunksize=5
xbed = "testtable.tab"
%rm 'tempstore.h5'
# create a store
with pd.HDFStore('tempstore.h5') as store:
for nn, chunk in enumerate(pd.read_table(xbed, chunksize=chunksize, sep = sep, index_col= indexcols)):
group = "x"
print(chunk.index.names)
store.append(group, chunk, format = "table",
min_itemsize=dict(zip(chunk.index.names, [32]*len(chunk.index.names))))
print("chunk #" , nn, file = sys.stderr)
```
```
# simple Index -> fails:
sep = "\t"
indexcols =[0] # <==== this is the only difference
chunksize=5
xbed = "testtable.tab"
%rm 'tempstore.h5'
# create a store
with pd.HDFStore('tempstore.h5') as store:
for nn, chunk in enumerate(pd.read_table(xbed, chunksize=chunksize, sep = sep, index_col= indexcols)):
group = "x"
print(chunk.index.names)
store.append(group, chunk, format = "table",
min_itemsize=dict(zip(chunk.index.names, [32]*len(chunk.index.names))))
print("chunk #" , nn, file = sys.stderr)
```
`-> ... ValueError: min_itemsize has the key [V1] which is not an axis or data_column`
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} | 2 | 2015-10-18T22:44:37Z | 2018-06-28T10:23:02Z | 2018-06-28T10:23:02Z | CONTRIBUTOR | null | xref #12502 (example there as well)
xref #14148
```
In [3]: idx = pd.date_range('20130101',periods=3,tz='US/Eastern')
In [4]: df = DataFrame({'A' : [1,2,3]})
In [5]: df['B'] = idx
In [6]: df.dtypes
Out[6]:
A int64
B datetime64[ns, US/Eastern]
dtype: object
In [7]: df.loc[[True,False,True],'C'] = idx
In [8]: df
Out[8]:
A B C
0 1 2013-01-01 00:00:00-05:00 2013-01-01 05:00:00
1 2 2013-01-02 00:00:00-05:00 NaT
2 3 2013-01-03 00:00:00-05:00 2013-01-02 05:00:00
# C should be the same time zone
In [9]: df.dtypes
Out[9]:
A int64
B datetime64[ns, US/Eastern]
C datetime64[ns]
dtype: object
```
this is also realized to #11351 [4]
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| null | 1 | 2015-10-19T05:50:28Z | 2020-04-26T20:54:46Z | null | CONTRIBUTOR | null | ```
>>> import pandas as pd
>>> pd.eval('not True')
-2
```
Not sure if this was intentional, since `'not'` maps to `op.invert` in https://github.com/pydata/pandas/blob/master/pandas/computation/ops.py#L458. After fixing that `numexpr` still `invert`s rather than `not`s the operand.
```
INSTALLED VERSIONS
------------------
commit: 53a0db18104abde098d43c66d77555fad12ed8c1
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-63-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.0+62.g53a0db1
nose: 1.3.6
pip: 7.1.2
setuptools: 18.4
Cython: 0.22
numpy: 1.9.2
scipy: 0.15.1
statsmodels: None
IPython: 4.0.0
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.4
blosc: None
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: 2.0.3
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.6
lxml: 3.4.4
bs4: 4.3.2
html5lib: 1.0b2
httplib2: None
apiclient: None
sqlalchemy: 1.0.4
pymysql: 0.6.6.None
psycopg2: None
```
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| null | 8 | 2015-10-19T06:23:48Z | 2015-11-18T23:56:16Z | 2015-11-18T20:16:52Z | NONE | null | closes #10381
The fix replaces named index columns in [`Table.index_axes` with their names](https://github.com/pydata/pandas/compare/master...DSLituiev:master#diff-1b15d1477da3a0548d2dd72a5d023d00L3323) and [corrects `min_itemsize` if it contains 'index' item which is not in `Table.index_axes`](https://github.com/pydata/pandas/compare/master...DSLituiev:master#diff-1b15d1477da3a0548d2dd72a5d023d00R3093)
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} | 3 | 2015-10-19T11:14:03Z | 2018-07-06T20:21:08Z | 2017-07-10T21:07:50Z | NONE | null | I can index a Panel to get a data frame, e.g. by major_axis:
```
>>> p = pd.Panel(np.arange(8).reshape(2,2,2),items=('A1','B1'),major_axis=('A2','B2'),minor_axis=('A3','B3'))
>>> p.loc[:,'A2',:]
A1 B1
A3 0 4
B3 1 5
```
But if I do the same index when the minor_axis is a MultiIndex the resulting data frame is unexpectedly transposed:
```
mi = pd.MultiIndex((['A3','B3'],['a','b']), [(0,0),(1,1)])
p = pd.Panel(np.arange(8).reshape(2,2,2),items=('A1','B1'),major_axis=('A2','B2'),minor_axis=mi)
p.loc[:,'A2',:]
A3
b b
A1 0 1
B1 4 5
```
Actually I think the second behaviour is closer to what I expect - the items axis becomes dimension 0 of the DataFrame. But whichever is used, we should have stability, especially if 1-item MultiIndex objects automatically become Index objects.
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} | 4 | 2015-10-19T11:16:27Z | 2015-12-13T20:42:04Z | 2015-12-13T20:42:04Z | CONTRIBUTOR | null | This code returns different values in `0.17.0` and `0.15.2`
``` python
import pandas as pd
from pandas.util.testing import assert_index_equal
pd.show_versions()
offsets = [
pd.offsets.Day, pd.offsets.MonthBegin,
pd.offsets.QuarterBegin, pd.offsets.YearBegin,
]
dates = pd.date_range('2011-01-01', '2011-01-05', freq='D')
for offset in offsets:
# adding each item individually or vectorised should give same answer
expected_vec = dates + offset(n=0)
expected = pd.DatetimeIndex([d + offset(n=0) for d in dates])
msg = "offset: {}, vectorised: {}, individual: {}".format(
offset, expected_vec, expected
)
try:
if pd.__version__ == '0.17.0':
assert_index_equal(expected_vec, expected, check_names=False)
else:
assert_index_equal(expected_vec, expected)
except AssertionError as er:
raise Exception(msg + str(er))
```
### 0.17.0
``` python
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 26 Stepping 5, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.0
nose: 1.3.7
pip: 7.1.0
setuptools: 18.0.1
Cython: 0.22
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 3.2.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.1
pytz: 2015.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: 0.7.3
lxml: None
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.7
pymysql: None
psycopg2: None
Traceback (most recent call last):
File "c:\dev\code\sandbox\pandas_17_vs_15_dateoffsets.py", line 24, in <module>
raise Exception(msg + str(er))
Exception: offset: <class 'pandas.tseries.offsets.MonthBegin'>, vectorised: DatetimeIndex(['2010-12-01', '2011-01-01', '2011-01-01', '2011-01-01',
'2011-01-01'],
dtype='datetime64[ns]', freq=None), individual: DatetimeIndex(['2011-01-01', '2011-02-01', '2011-02-01', '2011-02-01',
'2011-02-01'],
dtype='datetime64[ns]', freq=None)Index are different
Index values are different (100.0 %)
[left]: DatetimeIndex(['2010-12-01', '2011-01-01', '2011-01-01', '2011-01-01',
'2011-01-01'],
dtype='datetime64[ns]', freq=None)
[right]: DatetimeIndex(['2011-01-01', '2011-02-01', '2011-02-01', '2011-02-01',
'2011-02-01'],
dtype='datetime64[ns]', freq=None)
```
### 0.15.2
``` python
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 26 Stepping 5, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_GB
pandas: 0.15.2
nose: 1.3.7
Cython: 0.22
numpy: 1.9.2
scipy: 0.15.1
statsmodels: None
IPython: 3.2.1
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.1
pytz: 2015.4
bottleneck: 1.0.0
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.4
xlwt: 0.7.5
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 1.0.7
pymysql: None
psycopg2: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/11371 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11371/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11371/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11371/events | https://github.com/pandas-dev/pandas/pull/11371 | 112,127,114 | MDExOlB1bGxSZXF1ZXN0NDgwNjA0NDE= | 11,371 | BUG: pivot table bug with Categorical indexes, #10993 | {
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} | 10 | 2015-10-19T11:39:15Z | 2015-10-20T20:47:02Z | 2015-10-20T17:29:50Z | CONTRIBUTOR | null | closes #10993
replaces #10989
So issue #10993 involves the insertion of a key into a multi-index that has as one of its levels a `CategoricalIndex`. This causes the semantics to break down because we are inserting an new key.
Existing
```
In [16]: df = DataFrame({'A' : [1,2], 'B' : [3,4] })
In [17]: df.columns = MultiIndex([pd.CategoricalIndex(list('ab')),[1,2]],[[0,1],[0,1]])
In [3]: df.columns.levels[0]
Out[3]: CategoricalIndex([u'a', u'b'], categories=[u'a', u'b'], ordered=False, dtype='category')
In [18]: df
Out[18]:
a b
1 2
0 1 3
1 2 4
In [19]: df.columns
Out[19]:
MultiIndex(levels=[[u'a', u'b'], [1, 2]],
labels=[[0, 1], [0, 1]])
In [20]: df[('c',3)] = 5
TypeError: cannot insert an item into a CategoricalIndex that is not already an existing category
```
New
```
In [4]: df[('c',3)] = 5
In [5]: df
Out[5]:
a b c
1 2 3
0 1 3 5
1 2 4 5
In [8]: df.columns.levels[0]
Out[8]: CategoricalIndex([u'a', u'b', u'c'], categories=[u'a', u'b', u'c'], ordered=False, dtype='category')
```
The only issue that was slightly controversial is that `.insert` will retain the `ordered` attribute (and new categories go to the end). while `.append` will always have `ordered=False`. In theory we could do the same, but `.append` is used to generally append _another_ `CategoricalIndex`, so you would have some possiblity of interleaving of the 'ordered' categories (IOW, if self has `[1,2,3]` and other has `[3,2,1,4]`, then the result will be `[1,2,3,4]`.
We _could_ raise if we have mixed ordering (e.g. self is `ordered=False`, other is `ordered=True`).
Of course the user is free to reorder and such, but the default should be intuitive.
Futher note that we can now concat pandas objects with `CategoricalIndexes` (I don't think was specified before, certainly not tested), e.g.
```
In [1]: df = DataFrame({'A' : np.arange(5)},index=pd.CategoricalIndex(list('aabbc')))
In [2]: df2 = DataFrame({'A' : np.arange(5)},index=pd.CategoricalIndex(list('bbcde')))
In [3]: pd.concat([df,df2])
Out[3]:
A
a 0
a 1
b 2
b 3
c 4
b 0
b 1
c 2
d 3
e 4
In [4]: pd.concat([df,df2]).index
Out[4]: CategoricalIndex([u'a', u'a', u'b', u'b', u'c', u'b', u'b', u'c', u'd', u'e'], categories=[u'a', u'b', u'c', u'd', u'e'], ordered=False, dtype='category')
```
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https://api.github.com/repos/pandas-dev/pandas/issues/11372 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11372/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11372/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11372/events | https://github.com/pandas-dev/pandas/issues/11372 | 112,162,699 | MDU6SXNzdWUxMTIxNjI2OTk= | 11,372 | DataFrame.ix[idx, :] = value sets wrong values when idx is a MultiIndex and DataFrame.columns is also a MultiIndex | {
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} | 6 | 2015-10-19T14:57:26Z | 2015-10-27T11:17:08Z | 2015-10-27T11:17:08Z | CONTRIBUTOR | null | This code is broken in `0.17.0` but not in `0.15.2`:
``` python
import pandas as pd
import numpy as np
np.random.seed(1)
from itertools import product
from pandas.util.testing import assert_frame_equal
pd.show_versions()
idx = pd.MultiIndex.from_tuples(
list(
product(['A', 'B', 'C'],
pd.date_range('2015-01-01', '2015-04-01', freq='MS'))
)
)
sub = pd.MultiIndex.from_tuples(
[('A', pd.Timestamp('2015-01-01')), ('A', pd.Timestamp('2015-02-01'))]
)
# if cols = ['foo', 'bar', 'baz', 'quux'], there is no error.
cols = pd.MultiIndex.from_tuples(
list(
product(['foo', 'bar'],
pd.date_range('2015-01-01', '2015-02-01', freq='MS'))
)
)
test = pd.DataFrame(np.random.random((12, 4)), index=idx, columns=cols)
vals = pd.DataFrame(np.random.random((2, 4)), index=sub, columns=cols)
test.ix[sub, :] = vals
print test.ix[sub, :]
print vals
assert_frame_equal(test.ix[sub, :], vals)
```
### 0.17.0
``` python
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 26 Stepping 5, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.0
nose: 1.3.7
pip: 7.1.0
setuptools: 18.0.1
Cython: 0.22
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 3.2.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.1
pytz: 2015.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: 0.7.3
lxml: None
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.7
pymysql: None
psycopg2: None
foo bar
2015-01-01 2015-02-01 2015-01-01 2015-02-01
A 2015-01-01 0.287775 0.130029 0.019367 0.678836
2015-02-01 0.287775 0.130029 0.019367 0.678836
foo bar
2015-01-01 2015-02-01 2015-01-01 2015-02-01
A 2015-01-01 0.287775 0.130029 0.019367 0.678836
2015-02-01 0.211628 0.265547 0.491573 0.053363
Traceback (most recent call last):
File "c:\dev\code\sandbox\multiindex.py", line 41, in <module>
assert_frame_equal(test.ix[sub, :], vals)
File "c:\python\envs\pd017\lib\site-packages\pandas\util\testing.py", line 1028, in assert_frame_equal
obj='DataFrame.iloc[:, {0}]'.format(i))
File "c:\python\envs\pd017\lib\site-packages\pandas\util\testing.py", line 925, in assert_series_equal
check_less_precise, obj='{0}'.format(obj))
File "pandas\src\testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas\src\testing.c:3809)
File "pandas\src\testing.pyx", line 147, in pandas._testing.assert_almost_equal (pandas\src\testing.c:2685)
File "c:\python\envs\pd017\lib\site-packages\pandas\util\testing.py", line 798, in raise_assert_detail
raise AssertionError(msg)
AssertionError: DataFrame.iloc[:, 0] are different
DataFrame.iloc[:, 0] values are different (50.0 %)
[left]: [0.287775338586, 0.287775338586]
[right]: [0.287775338586, 0.211628116]
```
### 0.15.2
``` python
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 26 Stepping 5, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_GB
pandas: 0.15.2
nose: 1.3.7
Cython: 0.22
numpy: 1.9.2
scipy: 0.15.1
statsmodels: None
IPython: 3.2.1
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.1
pytz: 2015.4
bottleneck: 1.0.0
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.4
xlwt: 0.7.5
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 1.0.7
pymysql: None
psycopg2: None
foo bar
2015-01-01 2015-02-01 2015-01-01 2015-02-01
A 2015-01-01 0.287775 0.130029 0.019367 0.678836
2015-02-01 0.211628 0.265547 0.491573 0.053363
foo bar
2015-01-01 2015-02-01 2015-01-01 2015-02-01
A 2015-01-01 0.287775 0.130029 0.019367 0.678836
2015-02-01 0.211628 0.265547 0.491573 0.053363
```
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| null | 12 | 2015-10-19T17:59:46Z | 2021-04-20T05:39:36Z | null | NONE | null | Please quickly recall how awesome named arguments for Python are and then read through the following idea:
In case of identical or almost identical values of `index` and `columns`, it can be annoying to remember the assignment of the two data-axes to index and columns respectively. This could be solved by assigning names to `index` and `columns`, e.g. `index_name=y`and `columns_name=x`, and hence being able to retrieve values via `pd.DataFrame.name_loc(x=10, y=20)` (syntax not final).
I have no idea, if such a feature can be implemented with reasonable effort. I do however believe, that it would highly increase the usability of DataFrames for certain usecases.
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| null | 11 | 2015-10-19T18:00:20Z | 2015-10-29T10:18:03Z | 2015-10-19T20:58:59Z | CONTRIBUTOR | null | Test case:
``` python
user@host:~$ python3
Python 3.4.3+ (default, Oct 14 2015, 16:03:50)
[GCC 5.2.1 20151010] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from io import StringIO
>>> import pandas as pd
>>> src = """first, second
... 0,0.1
... 1,1.1
... """
>>> df = pd.read_csv(StringIO(src), na_values='XX')
>>> print(df)
first second
0 0 0.1
1 1 1.1
>>> df = pd.read_csv(StringIO(src), na_values='-999.99')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/goyo/.local/lib/python3.4/site-packages/pandas/io/parsers.py", line 491, in parser_f
return _read(filepath_or_buffer, kwds)
File "/home/goyo/.local/lib/python3.4/site-packages/pandas/io/parsers.py", line 278, in _read
return parser.read()
File "/home/goyo/.local/lib/python3.4/site-packages/pandas/io/parsers.py", line 740, in read
ret = self._engine.read(nrows)
File "/home/goyo/.local/lib/python3.4/site-packages/pandas/io/parsers.py", line 1187, in read
data = self._reader.read(nrows)
File "pandas/parser.pyx", line 766, in pandas.parser.TextReader.read (pandas/parser.c:8082)
File "pandas/parser.pyx", line 788, in pandas.parser.TextReader._read_low_memory (pandas/parser.c:8338)
File "pandas/parser.pyx", line 868, in pandas.parser.TextReader._read_rows (pandas/parser.c:9465)
File "pandas/parser.pyx", line 975, in pandas.parser.TextReader._convert_column_data (pandas/parser.c:10858)
File "pandas/parser.pyx", line 1035, in pandas.parser.TextReader._convert_tokens (pandas/parser.c:11744)
File "pandas/parser.pyx", line 1085, in pandas.parser.TextReader._convert_with_dtype (pandas/parser.c:12634)
File "pandas/parser.pyx", line 1499, in pandas.parser._try_double (pandas/parser.c:19996)
File "pandas/parser.pyx", line 1818, in pandas.parser.kset_float64_from_list (pandas/parser.c:22852)
TypeError: 'set' object does not support indexing
>>> pd.util.print_versions.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-16-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: es_ES.UTF-8
pandas: 0.17.0
nose: 1.3.6
pip: 1.5.6
setuptools: 18.4
Cython: None
numpy: 1.8.2
scipy: 0.14.1
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.6
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.9
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 15 | 2015-10-19T19:43:54Z | 2018-03-23T22:21:16Z | 2018-03-23T22:21:16Z | NONE | null | Hello,
ISO 8601 defines duration.
https://en.wikipedia.org/wiki/ISO_8601#Durations
see also usage in XML Schema http://www.w3.org/TR/xmlschema-2/#d0e11648
Duration can't be defined as `timedelta` (or `timedelta64`) because
- months don't always have 30 days
- years don't always have 365 days
It will be nice if Pandas could accept ISO 8601 duration.
We should probably use `pd.tseries.offsets.DateOffset` like
```
pd.tseries.offsets.DateOffset(years=2, months=6, days=1)
```
Kind regards
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} | 12 | 2015-10-19T22:31:00Z | 2015-10-25T05:32:36Z | 2015-10-25T05:32:36Z | NONE | null | The `drop_duplicates()` function in Python 3 is broken. Take the following example snippet:
```
import pandas as pd
raw_data = {'x': [7,6,3,3,4,8,0],'y': [0,6,5,5,9,1,2]}
df = pd.DataFrame(raw_data, columns = ['x', 'y'])
print("Before:", df)
df = df.drop_duplicates()
print("After:", df)
```
When run under python 2, the results are correct, but when running under python 3, pandas removes `6,6` from the frame, which is a completely unique row. When using this function with large CSV files, it causes thousands of lines of unique data loss.
See:
http://stackoverflow.com/questions/33224356/why-is-pandas-dropping-unique-rows
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| null | 2 | 2015-10-19T23:13:25Z | 2015-11-15T18:41:09Z | 2015-10-19T23:20:00Z | CONTRIBUTOR | null | closes #11365
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} | 1 | 2015-10-20T01:05:09Z | 2015-10-20T12:19:36Z | 2015-10-20T12:19:18Z | NONE | null | This bug shows up when using the plot method of a Series or DataFrame (Series example below) that has a TimedeltaIndex. x-axis time points become regularly spaced rather than spaced according to real time spans.
``` python
import pandas as pd
clock_times = pd.to_datetime(['12:00:00','12:23:00','12:30:00','12:45:00','13:54:00','14:15:00'])
elapsed_times = clock_times - clock_times[0]
test_data = pd.Series(data=[1,2,3,4,5,6], index=elapsed_times)
test_data.plot(style='o')
```
(Going straight to matplotlib is a workaround (plt.plot(test_data.index, test_data).)
## INSTALLED VERSIONS
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Darwin
OS-release: 15.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.2
nose: None
Cython: None
numpy: 1.9.3
scipy: 0.16.0
statsmodels: None
IPython: 4.0.0
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.6
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
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} | 6 | 2015-10-20T03:09:47Z | 2016-09-14T12:25:14Z | 2016-09-14T12:25:10Z | NONE | null | GBQ offers a handy option to ignore unknown values in a data insert.
This is especially useful if working with data that changes in structure without notice.
To make pandas more comprehensively reflecting the functionality of GBQ I would suggest to add an optional parameter in to_gbq that prepends the body (e.g. [here](https://github.com/pydata/pandas/blob/b2a547e8a9dd6ab3332f18a6f87ecb31181d7d30/pandas/io/gbq.py#L295)) with
``` python
body = {'ignoreUnknownValues': True, 'rows': rows}
```
or to patch the table schema if necessary (which would be more complex to implement, I guess).
Thanks for your consideration (and working on this io option! Love it. Saved me hours, if not days.)
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| null | 1 | 2015-10-20T04:10:36Z | 2015-10-20T10:05:29Z | 2015-10-20T10:03:41Z | NONE | null | Previously when using Pandas 0.16.2 and Numpy 1.9.2 one could run various numpy funcs on a pandas Series and have the result returned back typed as a Series (instead of an NDarray). Example:
``` python
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(10, 1))
dfn = np.nan_to_num(df[0])
print "dfn type: %s" % type(dfn)
```
renders the following output `dfn type: <class 'pandas.core.series.Series'>`
However, when using Numpy 1.10.1, the result is now typed as an NDarray. While this obviously is a result of numpy internal changes, does Pandas 0.17.x resolve/work around this issue? So far I've only tested `np.nan_to_num` but perhaps there are other funcs that exhibit the same behaviour.
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| null | 2 | 2015-10-20T05:46:17Z | 2015-10-20T20:17:40Z | 2015-10-20T09:58:51Z | NONE | null | The public `pandas.version` submodule seems to have been removed without any deprecation warning beforehand in gh-10370. This causes a number of test failures for statsmodels.
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https://api.github.com/repos/pandas-dev/pandas/issues/11382 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11382/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11382/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11382/events | https://github.com/pandas-dev/pandas/issues/11382 | 112,326,787 | MDU6SXNzdWUxMTIzMjY3ODc= | 11,382 | truncate() raises IndexError on large timeseries | {
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} | 16 | 2015-10-20T09:42:58Z | 2016-05-07T16:53:42Z | 2016-05-07T16:53:42Z | NONE | null | Hi,
I found a bug, tested on pandas 0.16 and 0.17. On large time series, the `truncate()` method throws an IndexError. Here's code that reproduces the bug:
``` python
>>> import pandas as pd
>>> # Works fine on small timeseries:
>>> small = pd.Series(data=range(int(2e3)), index=range(int(2e3)))
>>> small.truncate()
>>> small.truncate(before=0, after=3e3)
>>> # IndexError thrown on large timeseries:
>>> big = pd.Series(data=range(int(2e6)), index=range(int(2e6)))
>>> big.truncate()
>>> big.truncate(before=0, after=3e6)
```
Info version:
INSTALLED VERSIONS
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-41-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.17.0
nose: 1.3.4
pip: 7.1.2
setuptools: 5.5.1
Cython: 0.20.2
numpy: 1.8.2
scipy: 0.14.0
statsmodels: None
IPython: 3.0.0
sphinx: 1.2.2
patsy: None
dateutil: 2.4.2
pytz: 2015.6
blosc: None
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: 0.9
apiclient: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
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https://api.github.com/repos/pandas-dev/pandas/issues/11383 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11383/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11383/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11383/events | https://github.com/pandas-dev/pandas/issues/11383 | 112,359,321 | MDU6SXNzdWUxMTIzNTkzMjE= | 11,383 | ValueError: numpy.dtype has the wrong size, try recompiling | {
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} | 21 | 2015-10-20T12:41:22Z | 2017-06-06T11:29:35Z | 2015-10-20T12:56:21Z | NONE | null | I have been setting up a new CI server and I'm hitting a problem when running our tests on the new server - tests which use Pandas are giving this error:
```
File "/root/.virtualenvs/myproject/local/lib/python2.7/site-packages/pandas/__init__.py", line 7, in <module>
from . import hashtable, tslib, lib
File "pandas/src/numpy.pxd", line 157, in init pandas.lib (pandas/lib.c:78660)
ValueError: numpy.dtype has the wrong size, try recompiling
```
I have read other threads about the cause of this problem but they don't seem to apply in my case.
We are running in a virtualenv, inside a Docker container (Debian Stretch).
There is no 'system' numpy, pandas or cython installed:
```
root@e00c968153d1:/# pip freeze | grep -i numpy
root@e00c968153d1:/# pip freeze | grep -i pandas
root@e00c968153d1:/# pip freeze | grep -i cython
```
In our requirements.txt we have:
```
numpy==1.9.1
pandas==0.15.2
```
but...
```
$ workon myproject
$ ipython
In [1]: import numpy
In [2]: numpy.version.full_version
Out[2]: '1.9.1'
In [3]: import pandas
numpy.dtype has the wrong size, try recompiling
```
Now, I have found a manual fix, which is to re-install pandas:
```
$ pip uninstall pandas
$ pip install --no-cache-dir pandas==0.15.2
```
The need for `--no-cache-dir` suggests something about the problem, but I don't understand what exactly. I am installing in a 'virgin' docker container, there is no pip cache before I first install requirements.txt:
```
$ ls -l /root/.cache/pip/wheels
$ ls: cannot access /root/.cache/pip/wheels: No such file or directory
```
After `pip install -r requirements.txt`:
```
$ locate pandas
/root/.cache/pip/wheels/f7/7b/7c/6de5614135da1e25d864beec01ed40bb43fcafdeda870a05f6/pandas-0.15.2-cp27-none-linux_x86_64.whl
/root/.virtualenvs/myproject/lib/python2.7/site-packages/pandas
...
(only one installation of pandas found)
```
The manual fix is not acceptable as I need to automate the build.
Appreciate if you can suggest other avenues to explore towards a solution.
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| null | 1 | 2015-10-20T14:03:10Z | 2015-10-20T14:06:09Z | 2015-10-20T14:04:58Z | NONE | null | An update to pandas 0.17.0 leads to an error in the read_excel function. The line <code>index_col=['Site', 'Commodity', 'Type']</code> raises an error as pandas expects integers instead of strings.
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https://api.github.com/repos/pandas-dev/pandas/issues/11385 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11385/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11385/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11385/events | https://github.com/pandas-dev/pandas/issues/11385 | 112,405,320 | MDU6SXNzdWUxMTI0MDUzMjA= | 11,385 | str.extract raises ValueError with group named "name" | {
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} | 3 | 2015-10-20T16:15:02Z | 2016-04-10T01:46:47Z | 2016-04-10T01:46:47Z | CONTRIBUTOR | null | For a Series `S`, I find the `S.str.extract` method very useful. It is great how you implemented naming the resulting DataFrame columns according to the names specified in the capturing groups of the regular expression.
However there seems to be a bug when there is a capture group named "name" for example
``` python
>>> import re
>>> import pandas as pd
>>> import numpy as np
>>> data = {
... 'Dave': '[email protected]',
... 'multiple': '[email protected] some text [email protected]',
... 'none': np.nan,
... }
>>> pattern = r'''
... (?P<name>[a-z]+)
... @
... (?P<domain>[a-z]+)
... \.
... (?P<tld>[a-z]{2,4})
... '''
>>> S = pd.Series(data)
>>> result = S.str.extract(pattern, re.VERBOSE)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/core/strings.py", line 1370, in extract
return self._wrap_result(result, name=name)
File "pandas/core/strings.py", line 1088, in _wrap_result
name = kwargs.get('name') or getattr(result, 'name', None) or self.series.name
File "pandas/core/generic.py", line 730, 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().
>>> from pandas.util.print_versions import show_versions
>>> show_versions()
INSTALLED VERSIONS
------------------
commit: 5d953e3fba420b6721c7f1c5d53e5812fe113bbc
python: 2.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.8.0-44-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
pandas: 0.17.0+73.g5d953e3
nose: 1.1.2
pip: None
setuptools: 0.6
Cython: 0.20.1
numpy: 1.9.1
scipy: 0.14.0
statsmodels: None
IPython: 0.12.1
sphinx: None
patsy: None
dateutil: 1.5
pytz: 2015.6
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.7.2
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
>>>
```
The result I expected was
``` python
>>> exp_list = [
... ("dave", "google", "com"),
... ("rob", "gmail", "com"),
... (np.nan, np.nan, np.nan),
... ]
>>> exp = pd.DataFrame(
... exp_list,
... ["Dave", "multiple", "none"],
... ["name", "domain", "tld"])
>>> print exp
name domain tld
Dave dave google com
multiple rob gmail com
none NaN NaN NaN
>>>
```
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} | 57 | 2015-10-20T16:42:01Z | 2016-02-09T22:22:32Z | 2016-02-09T22:20:53Z | CONTRIBUTOR | null | For a series `S`, the excellent `S.str.extract` method returns the first match in each subject of the series:
``` python
>>> import re
>>> import pandas as pd
>>> import numpy as np
>>> data = {
... 'Dave': '[email protected]',
... 'multiple': '[email protected] some text [email protected]',
... 'none': np.nan,
... }
>>> pattern = r'''
... (?P<user>[a-z]+)
... @
... (?P<domain>[a-z]+)
... \.
... (?P<tld>[a-z]{2,4})
... '''
>>> S = pd.Series(data)
>>> S.str.extract(pattern, re.VERBOSE)
user domain tld
Dave dave google com
multiple rob gmail com
none NaN NaN NaN
>>>
```
That's great, but sometimes we want to extract all matches in each element of the series. You can do that with `S.str.findall` but its result does not include the names specified in the capturing groups of the regular expression:
``` python
>>> S.str.findall(pattern, re.VERBOSE)
Dave [(dave, google, com)]
multiple [(rob, gmail, com), (steve, gmail, com)]
none NaN
dtype: object
>>>
```
I propose the `S.str.extractall` method which returns a `Series` the same length as the subject `S`. Each element of the series is a `DataFrame` with a row for each match and a column for each group:
``` python
>>> result = S.str.extractall(pattern, re.VERBOSE)
>>> result[0]
user domain tld
0 dave google com
>>> result[1]
user domain tld
0 rob gmail com
1 steve gmail com
>>> result[2]
Empty DataFrame
Columns: [user, domain, tld]
Index: []
>>>
```
Before I write any more testing code, can we start a discussion about whether or not this is an acceptable design choice, in relation to the other functionality of pandas? @sinhrks @jorisvandenbossche @jreback @mortada since you seem to be discussing extract in #10103
Also do you have any ideas about how to get the result (a Series of DataFrames) to print more nicely? With my current fork we have
``` python
>>> result
Dave user domain tld
0 dave google com
multiple user domain tld
0 rob gmail com
1 s...
none Empty DataFrame
Columns: [user, domain, tld]
I...
dtype: object
>>>
```
In R the equivalent functionality is provided by the https://github.com/tdhock/namedCapture package (str_match_all_named returns a list of data.frames), and the resulting printout is readable because of the way that R prints lists:
``` r
> library(namedCapture)
> S <- c(
+ Dave='[email protected]',
+ multiple='[email protected] some text [email protected]',
+ none=NA)
> pattern <- paste0(
+ "(?P<user>[a-z]+)",
+ "@",
+ "(?P<domain>[a-z]+)",
+ "[.]",
+ "(?P<tld>[a-z]{2,4})")
> str_match_all_named(S, pattern)
$Dave
user domain tld
[1,] "dave" "google" "com"
$multiple
user domain tld
[1,] "rob" "gmail" "com"
[2,] "steve" "gmail" "com"
$none
<0 x 0 matrix>
>
```
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| null | 2 | 2015-10-20T18:35:39Z | 2018-11-21T17:00:19Z | 2018-11-21T17:00:19Z | NONE | null | I have a dataframe, and I want to calculate ewmcov. Sometimes the dataframe has missing values.
I've noticed that using the same dataframe, I get two different results with two separate calls of ewmcov.
Anyone have an idea on why this could be?
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} | 1 | 2015-10-20T18:53:08Z | 2015-11-13T22:25:13Z | 2015-11-13T22:25:13Z | CONTRIBUTOR | null | ``` python
======================================================================
ERROR: test_constructor_unsortable (pandas.tests.test_categorical.TestCategorical)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/var/tmp/portage/dev-python/pandas-0.17.0/work/pandas-0.17.0-python3_4/lib/pandas/core/algorithms.py", line 198, in factorize
sorter = uniques.argsort()
TypeError: unorderable types: datetime.datetime() > int()
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/var/tmp/portage/dev-python/pandas-0.17.0/work/pandas-0.17.0-python3_4/lib/pandas/core/categorical.py", line 265, in __init__
codes, categories = factorize(values, sort=True)
File "/var/tmp/portage/dev-python/pandas-0.17.0/work/pandas-0.17.0-python3_4/lib/pandas/core/algorithms.py", line 207, in factorize
lambda x: isinstance(x,string_types) ]
File "/var/tmp/portage/dev-python/pandas-0.17.0/work/pandas-0.17.0-python3_4/lib/pandas/core/algorithms.py", line 206, in <listcomp>
np.sort(np.array([ e for i, e in enumerate(uniques) if f(e) ],dtype=object)) for f in [ lambda x: not isinstance(x,string_types),
File "/usr/lib64/python3.4/site-packages/numpy/core/fromnumeric.py", line 819, in sort
a.sort(axis, kind, order)
TypeError: unorderable types: datetime.datetime() > int()
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/var/tmp/portage/dev-python/pandas-0.17.0/work/pandas-0.17.0-python3_4/lib/pandas/tests/test_categorical.py", line 94, in test_constructor_unsortable
Categorical.from_array(arr, ordered=True)
File "/var/tmp/portage/dev-python/pandas-0.17.0/work/pandas-0.17.0-python3_4/lib/pandas/core/categorical.py", line 351, in from_array
return Categorical(data, **kwargs)
File "/var/tmp/portage/dev-python/pandas-0.17.0/work/pandas-0.17.0-python3_4/lib/pandas/core/categorical.py", line 271, in __init__
raise TypeError("'values' is not ordered, please explicitly specify the "
TypeError: 'values' is not ordered, please explicitly specify the categories order by passing in a categories argument.
----------------------------------------------------------------------
Ran 9056 tests in 1000.617s
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.1.9-lh
machine: x86_64
processor: Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz
byteorder: little
LC_ALL: None
LANG: en_GB.utf8
pandas: 0.17.0
nose: 1.3.7
pip: 7.1.2
setuptools: 18.4
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: None
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.6
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: 0.9999999
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.8
pymysql: 0.6.7.None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
```
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| null | 4 | 2015-10-20T22:35:14Z | 2021-04-20T05:41:32Z | 2021-04-20T05:41:32Z | CONTRIBUTOR | null | This seems to be specific to Pandas as opposed to matplotlib. Worked as desired as of a few months ago when I last updated from master.
This is the desired behavior in matplotlib:
``` python
plt.barh([0,1], [.3, .5], xerr=[[.1,.2], [.3,.4]],
color = ['red','green'],
ecolor='black')
```
In the Pandas call, it seems to ignore the second error bar argument, both the upper and lower bounds of the error bar are the same:
``` python
s = pd.Series([.3,.5])
s.plot(kind='barh', xerr=[[.1,.2], [.3,.4]],
color = ['red','green'],
ecolor='black')
```
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| null | 2 | 2015-10-20T23:11:05Z | 2015-10-20T23:18:23Z | 2015-10-20T23:16:24Z | NONE | null | With MultiIndex, stats.api.ols returns prediction of a different shape than outcome variable:
```
"set up the example"
x1 = np.arange(0,4)
x2 = 2*np.random.rand(4)
y = x1+x2 + .2*np.random.randn(4)
df = pd.DataFrame( np.c_[y,x1, x2] ,
columns = ["y", "x1","x2"],
index = pd.MultiIndex.from_tuples([("chr1", 1), ("chr1",2), ("chr2",2), ("chr2", 4)]))
df.index.names = ["chr", "pos"]
df["y"]
"set up the model"
from pandas.stats.api import ols
res = ols(y = df["y"] , x = df[["x1","x2"]])
unexpected_output = res.predict()
unexpected_output
```
following post-formating recovers expected shape:
```
cols = unexpected_output.columns
unexpected_output.reset_index()
expected_output = pd.melt(unexpected_output.reset_index(),
id_vars= res.y.index.names[0] \
).dropna().set_index(res.y.index.names).loc[res.y.index.get_values()]
expected_output
```
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} | 7 | 2015-10-21T01:08:58Z | 2015-10-23T16:57:37Z | 2015-10-23T16:57:20Z | CONTRIBUTOR | null | closes #11344
taken from esnme/ultrajson#145
test code
```
import json
import pandas
from pandas.compat import zip, range
SIZE = 5*10**7
FILENAME = 'generated.json'
with open(FILENAME, 'w') as fileh:
json.dump(dict(zip(range(SIZE), range(SIZE))), fileh)
with open(FILENAME) as fileh:
pandas.json.load(fileh)
```
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} | 0 | 2015-10-21T01:31:22Z | 2015-10-21T10:15:06Z | 2015-10-21T10:15:06Z | CONTRIBUTOR | null | https://github.com/pydata/pandas/commit/0c8a8e1372aef137ec71f0ba9c7af58cb4a2a8ac
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| null | 3 | 2015-10-21T01:35:06Z | 2015-10-21T02:15:56Z | 2015-10-21T01:40:54Z | NONE | null | While invoking .get_all_data() and collecting options data for all strikes and expirations the entire dataframe errors out as soon as the parser comes across an expiration chain where data is only available for one type (calls or puts) and not both.
For example:
new_data=pd.DataFrame()
option = Options('AA', 'yahoo')
data = option.get_all_data()
Crashes with:
File "/usr/lib/python2.7/dist-packages/pandas/io/data.py", line 1139, in get_all_data
return self._get_data_in_date_range(dates=expiry_dates, call=call, put=put)
File "/usr/lib/python2.7/dist-packages/pandas/io/data.py", line 1153, in _get_data_in_date_range
frame = self._get_option_data(expiry=expiry_date, name=name)
File "/usr/lib/python2.7/dist-packages/pandas/io/data.py", line 771, in _get_option_data
frames = self._get_option_frames_from_yahoo(expiry)
File "/usr/lib/python2.7/dist-packages/pandas/io/data.py", line 688, in _get_option_frames_from_yahoo
option_frames = self._option_frames_from_url(url)
File "/usr/lib/python2.7/dist-packages/pandas/io/data.py", line 725, in _option_frames_from_url
raise RemoteDataError('Received no data from Yahoo at url: %s' % url)
pandas.io.data.RemoteDataError: Received no data from Yahoo at url: http://finance.yahoo.com/q/op?s=AA&date=1450396800
Since about half tickers have such chains, pulling data reliably is a challenge.
Python 2.7.9
Pandas was installed through pip
Ubuntu 15.04
## pip shows pandas:
Name: pandas
Version: 0.15.0
Location: /usr/lib/python2.7/dist-packages
Requires: python-dateutil, pytz, numpy
## ~$ pip show pandas-datareader
Name: pandas-datareader
Version: 0.2.0
Location: /usr/local/lib/python2.7/dist-packages
Requires: pandas
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} | 0 | 2015-10-21T02:33:09Z | 2015-10-21T10:15:06Z | 2015-10-21T10:15:06Z | CONTRIBUTOR | null | This reverts commit 0c8a8e1372aef137ec71f0ba9c7af58cb4a2a8ac.
closes #11394
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} | 6 | 2015-10-21T05:08:15Z | 2015-11-10T12:13:06Z | 2015-11-10T12:12:36Z | CONTRIBUTOR | null | closes #10510
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} | 2 | 2015-10-21T07:16:58Z | 2015-11-14T19:34:36Z | 2015-11-13T15:15:33Z | NONE | null | New pull request with tests and comments. See https://github.com/pydata/pandas/pull/9821 for original PR.
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https://api.github.com/repos/pandas-dev/pandas/issues/11400 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11400/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11400/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11400/events | https://github.com/pandas-dev/pandas/pull/11400 | 112,537,936 | MDExOlB1bGxSZXF1ZXN0NDgzMDIxNzc= | 11,400 | BUG: using .ix with a multi-index indexer | {
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} | 6 | 2015-10-21T08:40:16Z | 2015-10-27T13:25:20Z | 2015-10-27T11:17:08Z | CONTRIBUTOR | null | closes #11372
Add an optional argument to _NDFrameIndexer to indicate
if the indexer is from a MultiIndex
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} | 2 | 2015-10-21T11:19:10Z | 2015-10-21T15:30:48Z | 2015-10-21T15:28:55Z | CONTRIBUTOR | null | See the discussion in #11359
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} | 4 | 2015-10-21T19:10:40Z | 2015-10-27T11:12:01Z | 2015-10-27T11:12:01Z | CONTRIBUTOR | null | Series.index_sort() _always_ returns a new Series, ignoring the `inplace` keyword argument. This is in contrast to DataFrame's sort_index() which works as expected.
``` python
vals = range(10, 0, -1)
x = pd.Series(vals)
# Returns a new Series with the sorted index instead of
# changing x in place
y = x.sort_index(ascending=False, inplace=True)
# Not the same
(x != y).all()
# sort_index() works as expected for DataFrames
xx = pd.DataFrame({'col': x})
assert xx.sort_index(ascending=False, inplace=True) is None
```
show_versions() output:
## INSTALLED VERSIONS
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.0
nose: 1.3.7
pip: 7.1.2
setuptools: 18.4
Cython: None
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: None
patsy: 0.4.0
dateutil: 2.4.1
pytz: 2015.6
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 2.2.0-b1
xlrd: 0.9.3
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.3
pymysql: 0.6.6.None
psycopg2: None
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