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https://api.github.com/repos/pandas-dev/pandas/issues/6801 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6801/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6801/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6801/events | https://github.com/pandas-dev/pandas/issues/6801 | 30,884,056 | MDU6SXNzdWUzMDg4NDA1Ng== | 6,801 | Crosstab margins not calculating correctly | {
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"de... | closed | false | null | [] | null | 3 | 2014-04-04T18:39:50Z | 2014-04-04T19:31:12Z | 2014-04-04T19:31:12Z | NONE | null | The margins of the crosstab are not calculated correctly when the row data is based on a filtered dataframe command. Below is an example. You will notice that the table_filtered does not calculate column totals correctly. I am using pandas 0.13.1, python 3.3.2, Windows 7.
``` python
# Create dataframe
d = {'Fruit' ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6804 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6804/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6804/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6804/events | https://github.com/pandas-dev/pandas/issues/6804 | 30,891,454 | MDU6SXNzdWUzMDg5MTQ1NA== | 6,804 | unable to collapse Joint Filters | {
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I'm using Pandas' pyTables functionality to select rows from a moderate-sized table on disk (< 300,000,000 rows). However, it raises the exception:
`NotImplementedError: unable to collapse Joint Filters`
when I try to do something like:
`store.select('Trans','(ID == 998) & (TillID==13)')`
(where store is an ope... | {
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... | 5 | 2014-04-04T20:51:40Z | 2021-04-11T04:25:51Z | null | MEMBER | null | I have some frames that have 44 ish columns with names I can't really remember. I think it might be useful to be able to do something like
``` python
store.get_index('hdf5/table', axis=1)
```
which would return the columns of `hdf5/table`. @jreback thoughts?
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"labels_url": "htt... | 3 | 2014-04-04T23:17:45Z | 2014-04-06T18:13:51Z | 2014-04-06T18:13:51Z | CONTRIBUTOR | null | ``` python
import pandas as pd
import numpy as np
df = pd.DataFrame({'num':[1,2,3], 'num2': np.random.randn(3), 'strings':list('abc')})
df.sum().values # array(['6', '-1.47818918058', 'abc'], dtype=object)
```
The issue is that axis=0 tries to coerce the dtypes and `np.array` will promote the numerics to strings.
N... | {
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Set 'utf-8' as default in case sys.getfilesystemencoding() return None.
sys.getfilesystemencoding() will return 'mbcs' in Windows, and will 'utf-8' or user setting in other systems.
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I wanted to add a specified offset (10 seconds) to and existing datetime (an index previously created by pd.to_datetime), so I thought creating a timedelta and adding it would do the trick. As you can see it doesn't yield an error but produces a rather strange results.
```
@buddha[J:T26]|5> base = pd.to_dateti... | {
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- Replaces `compat.scipy.scoreatpercentile` with `numpy.percentile`.
- Sets `axis=0` in a few tests, because `axis=None` by default on `numpy.percentile`, which returns a scalar (i.e., operates on a flattened version of the array).
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This still round trips successfully. According to Stack Overflow post I
found, there isn't a specific representation of `INF` values -
http://stackoverflow.com/questions/15704538/how-can-i-express-nan-values-in-excel
so we're going to make the decisio... | {
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Line numbers as of commit 70de129:
#### Deprecated since 0.11 or before
The following are fixed in this PR:
- [x] Deprecate precision in favor of accuracy (#395) -- v0.7.0
.\pandas\core\format.py:{2143}
- [x] Deprecate force_unicode (#2224, #2225) -- v0.10.0
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1. Add getter/setter/deleter property that warns on usage.
2. Migrate all code that refers to `Timestamp.offset` to use `Timestamp.freq` instead.
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Related: #6641, #6813
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the rolling variance. Instead of keeping track of the sum and sum of
the squares of the items in the window, it tracks the mean and the sum
of squared differences from the mean. This turns out to be (much) more
numerically stable.
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>>> idx = pd.date_range(start='2010-11-02 01:00:00', periods=3, tz='US/Pacific', freq='1H')
>>> idx + offsets.Hour(3)
[2010-11-02 04:00:00-07:00, ..., 2010-11-02 06:00:00-07:00]
Length: 3, Freq: H, Timezone: US/Pacific
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```
panel_shift | 263.2827 | 0.0746 | 3528.0756 |
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https://api.github.com/repos/pandas-dev/pandas/issues/6827 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6827/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6827/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6827/events | https://github.com/pandas-dev/pandas/pull/6827 | 30,945,057 | MDExOlB1bGxSZXF1ZXN0MTQ0Mjc0Mjc= | 6,827 | CLN apply pep257 via docformatter | {
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cc #6248
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https://api.github.com/repos/pandas-dev/pandas/issues/6828 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6828/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6828/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6828/events | https://github.com/pandas-dev/pandas/issues/6828 | 30,951,973 | MDU6SXNzdWUzMDk1MTk3Mw== | 6,828 | get_near_stock_price with year only does not work | {
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"gists_url": "htt... | [] | closed | false | null | [] | null | 1 | 2014-04-07T00:04:14Z | 2015-03-08T16:45:09Z | 2015-03-08T16:45:09Z | CONTRIBUTOR | null | ```
In [15]:
web.Options('AAPL', 'yahoo').get_near_stock_price(year=2014)
---------------------------------------------------------------------------
UnboundLocalError Traceback (most recent call last)
<ipython-input-15-617ce269ddce> in <module>()
----> 1 web.Options('AAPL', 'yahoo').get_near_st... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6829 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6829/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6829/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6829/events | https://github.com/pandas-dev/pandas/issues/6829 | 30,952,005 | MDU6SXNzdWUzMDk1MjAwNQ== | 6,829 | get_forward_data is deprecated with no suggestion of replacement | {
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"gists_url": "htt... | [] | closed | false | null | [] | null | 1 | 2014-04-07T00:06:27Z | 2015-03-08T16:45:35Z | 2015-03-08T16:45:35Z | CONTRIBUTOR | null | ```
warnings.warn("get_forward_data() is deprecated", FutureWarning)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/6830 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6830/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6830/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6830/events | https://github.com/pandas-dev/pandas/issues/6830 | 30,953,541 | MDU6SXNzdWUzMDk1MzU0MQ== | 6,830 | Insertion Point | {
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"gists_url": "htt... | [] | closed | false | null | [] | null | 5 | 2014-04-07T01:30:56Z | 2014-04-07T03:40:48Z | 2014-04-07T03:40:48Z | CONTRIBUTOR | null | Is there something like this: http://docs.oracle.com/javase/7/docs/api/java/util/Arrays.html#binarySearch(float[], float) in Pandas that returns the insertion point (index) for a given value in a given Series?
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https://api.github.com/repos/pandas-dev/pandas/issues/6831 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6831/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6831/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6831/events | https://github.com/pandas-dev/pandas/issues/6831 | 30,966,475 | MDU6SXNzdWUzMDk2NjQ3NQ== | 6,831 | Excelwriter : engine_kwargs error with xlsxwriter engine | {
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```
writer=pd.ExcelWriter('raw_data_dump.xlsx', engine='xlsxwriter', engine_kwargs={'constant_memory':True})
raw_data.to_excel(writer, sheet_name='Sheet1')
writer.save()
```
I get the following error :
```
Exception AttributeError: "'... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6832 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6832/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6832/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6832/events | https://github.com/pandas-dev/pandas/pull/6832 | 30,968,067 | MDExOlB1bGxSZXF1ZXN0MTQ0MzgyOTE= | 6,832 | DOC: Fixed repetition | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6833 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6833/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6833/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6833/events | https://github.com/pandas-dev/pandas/issues/6833 | 30,975,857 | MDU6SXNzdWUzMDk3NTg1Nw== | 6,833 | ENH: Read entire group of an HDF5 file in a single call | {
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... | 3 | 2014-04-07T11:20:00Z | 2021-04-11T04:26:06Z | null | NONE | null | See this [question in SO](http://stackoverflow.com/questions/22897597/read-entire-group-in-an-hdf5-file-using-a-pandas-hdfstore).
I guess that something like `store.get_group('df')` might do the job:
```
>>> import pandas as pd
>>> store = pd.HDFStore('test.h5',mode='w')
>>> store.append('df/foo1',DataFrame(np.random... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6834 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6834/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6834/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6834/events | https://github.com/pandas-dev/pandas/pull/6834 | 30,992,416 | MDExOlB1bGxSZXF1ZXN0MTQ0NTI2OTQ= | 6,834 | ENH: Scatter plot now supports errorbar | {
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... | closed | false | null | [] | {
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"creator": null,
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"due_on": "2014-05-31T07:00:00Z",
"html_url": "https://github.com/pandas-dev/pandas/milestone/24",
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"labels_url": "htt... | 15 | 2014-04-07T15:07:27Z | 2014-06-16T12:08:19Z | 2014-05-02T20:40:47Z | MEMBER | null | This is enhancement for #5638. I sometimes want to plot scatter with errorbars, thus I've refactored to support it.
Also, this includes following fixes. I've also modified tests to check the number of erro... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6835 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6835/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6835/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6835/events | https://github.com/pandas-dev/pandas/issues/6835 | 31,006,131 | MDU6SXNzdWUzMTAwNjEzMQ== | 6,835 | Offset bug for n != 1? | {
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},
{
"color": "0052cc",
"default": false,
"description": "DateOffsets",
"id": ... | open | false | null | [] | {
"closed_at": null,
"closed_issues": 786,
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... | 2 | 2014-04-07T17:48:44Z | 2017-12-12T02:30:12Z | null | CONTRIBUTOR | null | From stackoverflow
https://stackoverflow.com/questions/22835671/pandas-resample-with-seasonal-frequency#
Should this be True?
```
[~/]
[18]: from pandas.tseries.offsets import QuarterBegin
[~/]
[19]: ts = pd.Timestamp('2014-1-1')
[~/]
[20]: offset = QuarterBegin(2, startingMonth=4)
[~/]
[21]: offset.onOff... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6836 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6836/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6836/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6836/events | https://github.com/pandas-dev/pandas/issues/6836 | 31,041,209 | MDU6SXNzdWUzMTA0MTIwOQ== | 6,836 | How to specify the number of columns in read_csv? | {
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] | closed | false | null | [] | null | 2 | 2014-04-08T03:46:38Z | 2014-05-16T17:49:40Z | 2014-05-16T17:49:40Z | NONE | null | I have a file like this:
585.8 586.1 586.2 586.3 586.4 586.1 586.6 586.2 586.3 586.1
586.1 585.9 585.8 585.4 585.5 585.4 585.7 586.4 586.8 586.9
587.1 587.0 586.8 586.9 586.9 586.7 586.5 586.2 586.2 586.5
586.5 586.5 586.8 586.8 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6837 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6837/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6837/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6837/events | https://github.com/pandas-dev/pandas/issues/6837 | 31,067,537 | MDU6SXNzdWUzMTA2NzUzNw== | 6,837 | ENH: HDFStore.concat | {
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"closed_at": null,
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"creator": {
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"followers_url": "https://api.github.com/users/jorisva... | 4 | 2014-04-08T12:53:59Z | 2017-09-18T13:09:21Z | 2017-09-18T10:35:55Z | CONTRIBUTOR | null | From this (and other questions), related #4808
http://stackoverflow.com/questions/22934996/pandas-pytables-append-performance-and-increase-in-file-size
A convenience function to perform an on-disk concat from a list-like to a single table.
```
def concat(self, key, generator, index=True, append=False):
"""
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https://api.github.com/repos/pandas-dev/pandas/issues/6838 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6838/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6838/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6838/events | https://github.com/pandas-dev/pandas/issues/6838 | 31,068,811 | MDU6SXNzdWUzMTA2ODgxMQ== | 6,838 | DOC: set_option docstring | {
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Parking this here as I see it's not quite just a simple edit of a docstring. Might have time for a PR today. This is the current partial docstring of `set_option`. Maybe it's just me, but my eyes have a very hard time finding the actual parameters in here as it doesn't quite conform to the num... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6839 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6839/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6839/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6839/events | https://github.com/pandas-dev/pandas/issues/6839 | 31,069,092 | MDU6SXNzdWUzMTA2OTA5Mg== | 6,839 | Bug in 'chop.threshold' option | {
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Columns where every value is below the threshold disappear. From ML.
```
In [78]: df.as_matrix()
Out[78]:
array([[ 1.00000000e+01, 7.59062963e-10],
[ 2.00000000e+01, -1.47734680e-11],
[ 3.00000000e+01, 2.18850713e-09],
[ 4.00000000e+01, -1.86628720e-11]])
In [79]: np.set_printoption... | {
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"labels_url": "htt... | 5 | 2014-04-08T13:36:40Z | 2014-07-16T09:00:59Z | 2014-04-09T00:35:49Z | CONTRIBUTOR | null | @jreback here are the additional holiday examples. Let me know if you want something else.
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https://api.github.com/repos/pandas-dev/pandas/issues/6841 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6841/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6841/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6841/events | https://github.com/pandas-dev/pandas/pull/6841 | 31,073,708 | MDExOlB1bGxSZXF1ZXN0MTQ1MDEzMDI= | 6,841 | DOC: shrink bounding box on table plot | {
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@jreback any way to view the doc build before merging into master? I was looking into `pandas/ci/build_docs.sh` but it looks likes I'd need to set up another travis job and a new GH pages site.
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https://api.github.com/repos/pandas-dev/pandas/issues/6842 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6842/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6842/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6842/events | https://github.com/pandas-dev/pandas/issues/6842 | 31,085,132 | MDU6SXNzdWUzMTA4NTEzMg== | 6,842 | Inconsistent display of numbers in dataframe | {
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] | closed | false | null | [] | null | 5 | 2014-04-08T16:12:07Z | 2015-03-08T16:46:46Z | 2015-03-08T16:46:46Z | NONE | null | Using pandas 0.13.1, I see see that there is an apparent inconsistency in the format in which floating-point numbers in my dataframe are displayed. In particular, the numbers in the first column appear to be displayed in the form '%1.8e', whereas the other numbers are displayed as '%1.6f'.
```
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https://api.github.com/repos/pandas-dev/pandas/issues/6843 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6843/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6843/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6843/events | https://github.com/pandas-dev/pandas/issues/6843 | 31,087,058 | MDU6SXNzdWUzMTA4NzA1OA== | 6,843 | Write problem in SQL database with sqlite3 | {
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Let take the following csv file:
``` bash
$ cat /tmp/file.csv
objid,stellarmass_r-z
1237661976011866148,10.9271... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6845 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6845/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6845/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6845/events | https://github.com/pandas-dev/pandas/pull/6845 | 31,123,951 | MDExOlB1bGxSZXF1ZXN0MTQ1MzIxNjk= | 6,845 | BUG/ENH: Add how kwarg to rolling_* functions [fix #6297] | {
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Figured while I was in the area, would submit a PR to do this too. Let me know if you want to see more unit tests than the one.
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"labels_url": "htt... | 18 | 2014-04-09T10:59:37Z | 2018-10-13T01:53:58Z | 2014-04-13T19:39:44Z | CONTRIBUTOR | null | UPDATE: this issue seemed actually to be a bug in the new legacy code so all databases written were only written in memory and not really committed to the database itself (see discussion below).
---
Hi everybody,
I still have a problem with writing data into a SQL database. With the following example, the resulting ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6847 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6847/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6847/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6847/events | https://github.com/pandas-dev/pandas/issues/6847 | 31,163,479 | MDU6SXNzdWUzMTE2MzQ3OQ== | 6,847 | API: default sort kind | {
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"labels_url": "htt... | 0 | 2014-04-09T14:47:28Z | 2014-09-11T00:49:44Z | 2014-04-09T23:40:11Z | CONTRIBUTOR | null | current `Series.sort` defaults to `kind='quicksort'` prob because `np.sort` does.
but `Series.order` defaults to `mergesort` (and this is present in DataFrame.sort) as well.
proposal is to make `Series.order` default to quicksort for some conformity
any objections?
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https://api.github.com/repos/pandas-dev/pandas/issues/6848 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6848/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6848/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6848/events | https://github.com/pandas-dev/pandas/pull/6848 | 31,164,020 | MDExOlB1bGxSZXF1ZXN0MTQ1NTU0ODU= | 6,848 | API: make Series.sort/order arguments in the same order | {
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"labels_url": "htt... | 9 | 2014-04-09T14:53:16Z | 2014-06-22T23:19:41Z | 2014-04-09T23:40:11Z | CONTRIBUTOR | null | - drop unused `Series.sort` order argument
- added `Series.sort na_position` argument to conform with `Series.order`
- default kind for `Series.order` is now `quicksort`, same as `Series.sort` and numpy default
closes #6847
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https://api.github.com/repos/pandas-dev/pandas/issues/6849 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6849/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6849/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6849/events | https://github.com/pandas-dev/pandas/pull/6849 | 31,168,219 | MDExOlB1bGxSZXF1ZXN0MTQ1NTgxNjc= | 6,849 | API/BUG Raise ValueError when stacking nonunique levels | {
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"gi... | [] | closed | false | null | [] | null | 1 | 2014-04-09T15:38:28Z | 2017-04-05T02:08:53Z | 2014-04-09T21:07:29Z | CONTRIBUTOR | null | Should raise a ValueError when (un)stacking a DataFrame
on a nonunique level. Previous behavior was to raise
a KeyError (not deliberately). Closes #6729.
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https://api.github.com/repos/pandas-dev/pandas/issues/6850 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6850/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6850/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6850/events | https://github.com/pandas-dev/pandas/pull/6850 | 31,170,120 | MDExOlB1bGxSZXF1ZXN0MTQ1NTkzOTM= | 6,850 | add bins argument to Histogram function | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6851 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6851/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6851/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6851/events | https://github.com/pandas-dev/pandas/issues/6851 | 31,176,425 | MDU6SXNzdWUzMTE3NjQyNQ== | 6,851 | Log scaled histograms readability with default display.mpl_style can be improved | {
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"followers_url": "https://api.github.com/users/jorisva... | 6 | 2014-04-09T17:15:36Z | 2016-03-10T09:17:14Z | 2016-03-10T09:17:14Z | CONTRIBUTOR | null | I use quite frequently log scaled histograms with `histtype='step'` option.
I noticed that with the suggested `default display.mpl_style` (which I like), readability of such histograms is quite improvable.
Consider this (from ipython notebook)
```
%pylab inline
import pandas as pd
import numpy as np
import matplotl... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6852 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6852/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6852/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6852/events | https://github.com/pandas-dev/pandas/issues/6852 | 31,188,730 | MDU6SXNzdWUzMTE4ODczMA== | 6,852 | ENH: showing memory used | {
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maybe as a tuple : the Series in [6] and then a series of the axis data?
```
In [5]: df = DataFrame({ 'float' : np.random.randn(1000), 'int' : np.random.randint(0,5,size=1000), 'date' : Timestamp('20130101'), 'string' : 'foo' })
In [6]: Series(dict([ (b.dtype,b.valu... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6853 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6853/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6853/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6853/events | https://github.com/pandas-dev/pandas/pull/6853 | 31,192,285 | MDExOlB1bGxSZXF1ZXN0MTQ1NzMyMDQ= | 6,853 | DEPR: Indexers will warn FutureWarning when used with a scalar indexer this is floating-point (GH4892) | {
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"labels_url": "htt... | 4 | 2014-04-09T20:38:47Z | 2014-07-05T04:40:12Z | 2014-04-11T22:33:35Z | CONTRIBUTOR | null | closes #4892
numpy is doing this i 1.9 IIRC, and Scipy already did it. in 0.13.3
Indexers will warn `FutureWarning` when used with a scalar indexer and a non-floating point Index
```
# non-floating point indexes can only be indexed by integers / labels
In [1]: Series(1,np.arange(5))[3.0]
pandas/core/index.p... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6854 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6854/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6854/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6854/events | https://github.com/pandas-dev/pandas/issues/6854 | 31,221,709 | MDU6SXNzdWUzMTIyMTcwOQ== | 6,854 | Excelwriter : engine_kwargs error with xlsxwriter engine | {
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Passing directly the keyword argument, as suggested by @jtratner in #6831 also raises an error :
`writer=pd.ExcelWriter('raw_data_dump.xlsx', engine='xlsxwriter', constant_memory=True)
for asset_name, frame in export_fuel_data.groupby('asset_name'):
frame.to_excel(writer, sheet_name=asset... | {
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```
In [253]: to_timedelta(np.arange(5),unit='d')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-253-bab05b276f... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6856 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6856/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6856/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6856/events | https://github.com/pandas-dev/pandas/issues/6856 | 31,224,767 | MDU6SXNzdWUzMTIyNDc2Nw== | 6,856 | DOC: freeze old whatsnew notes? | {
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... | 17 | 2014-04-10T08:52:46Z | 2021-07-19T19:35:23Z | null | MEMBER | null | A problem with using the ipython directory for the whatsnew/release parts of the docs (so that all code examples are run during the doc build), is that after a while when things change/get deprecated, these pages don't build correctly anymore, generating warnings/exceptions.
An example: http://pandas-docs.github.io/pa... | {
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... | 13 | 2014-04-10T11:29:34Z | 2016-04-09T20:19:18Z | null | CONTRIBUTOR | null | Currently I use the HDF5 interface to store timeseries and it works great for selection. However, the only way I see to update them is to select the existing stored timeseries and then merge the update with the existing data. This can obviously be expensive if the existing series is large. Is it possible to update a... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6858 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6858/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6858/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6858/events | https://github.com/pandas-dev/pandas/pull/6858 | 31,235,426 | MDExOlB1bGxSZXF1ZXN0MTQ1OTkzOTY= | 6,858 | BUG: to_timedelta not properly converting some units (GH6855) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6859 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6859/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6859/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6859/events | https://github.com/pandas-dev/pandas/issues/6859 | 31,237,968 | MDU6SXNzdWUzMTIzNzk2OA== | 6,859 | API: add inplace to Series.sort/order | {
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Series.sort defaults to `inplace=True`
Series.order default to `inplace=False`
as they are inverses of each other
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https://api.github.com/repos/pandas-dev/pandas/issues/6860 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6860/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6860/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6860/events | https://github.com/pandas-dev/pandas/pull/6860 | 31,241,668 | MDExOlB1bGxSZXF1ZXN0MTQ2MDI4OTk= | 6,860 | API: allow Series comparison ops to align before comparison (GH1134) | {
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reordered comparisons
```
In [1]: s1 = Series(index=["A", "B", "C"], data=[1,2,3])
In [2]: s1
Out[2]:
A 1
B 2
C 3
dtype: int64
In [3]: s2 = Series(index=["C", "B", "A"], data=[3,2,1])
In [4]: s2
Out[4]:
C 3
B 2
A 1
dtype: int64
In [5]: s1 == s2
Out[5]:
A ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6861 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6861/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6861/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6861/events | https://github.com/pandas-dev/pandas/pull/6861 | 31,244,455 | MDExOlB1bGxSZXF1ZXN0MTQ2MDQ1NjE= | 6,861 | API: add inplace keyword to Series.order/sort to make them inverses (GH6859) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6862 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6862/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6862/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6862/events | https://github.com/pandas-dev/pandas/issues/6862 | 31,251,561 | MDU6SXNzdWUzMTI1MTU2MQ== | 6,862 | cannot slice empty dataframe | {
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```
import pandas as pd
print 'version', pd.__version__
df = pd.DataFrame({'x': []})
print df.iloc[0:0]
```
I would expect to get an empty dataframe. Instead I get an error:
```
version 0.13.1
Traceback (most recent call last):
File "bug.py", line 6, in <module>
print df.iloc[0:0]
File "/us... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6863 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6863/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6863/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6863/events | https://github.com/pandas-dev/pandas/pull/6863 | 31,254,594 | MDExOlB1bGxSZXF1ZXN0MTQ2MTA0ODI= | 6,863 | BUG: _tidy_repr should not be called when max_rows is None | {
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"labels_url": "htt... | 14 | 2014-04-10T15:41:18Z | 2014-06-12T16:16:48Z | 2014-04-10T21:01:17Z | CONTRIBUTOR | null | This issue was raised in http://stackoverflow.com/q/22824104/190597:
```
import pandas as pd
pd.options.display.max_rows = None
result = pd.Series(range(1001))
print(result)
```
raises `TypeError: unsupported operand type(s) for -: 'NoneType' and 'int'`.
The problem occurs in series.py (line 832) when `max_rows` is... | {
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Perhaps two parts here, first date is not converted unless col is passed in this example. Also tz is lost (I thought it should be 0 somewhere e.g. utcoffset).
```
js = '["2012-03-01T00:05:55+00:00", "2012-03-01T00:06:23+00:00", "2... | {
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] | closed | false | null | [] | null | 3 | 2014-04-10T20:30:08Z | 2014-04-10T20:52:11Z | 2014-04-10T20:52:11Z | NONE | null | ## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.13.1
Cython: 0.20.1
numpy: 1.8.0
scipy: 0.13.3
statsmodels: 0.5.0
IPython: 1.1.0
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"labels_url": "htt... | 15 | 2014-04-10T21:50:56Z | 2014-06-19T01:12:42Z | 2014-04-22T19:26:07Z | CONTRIBUTOR | null | This extends the `offsets.py` module with a `CBMonthEnd` based on `CustomBusinessDay`. Tests are found in `test_offsets.py`.
A few small points still open:
- Implement CBMonthBegin
- Improve speed. Main speed drag is `DateOffset.onOffset`.
Also some of the tests are not passing, but the errors seem unrelated.
e.g... | {
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```
In [1]: import pandas
In [3]: import numpy
In [4]: series = pandas.Series([1e-100, 1e-25, 1e-20, 1e-15, 1e-10,
1e-5, 1e-4, 1e-3, 1e-2, 1e-1])
In [5]: series
Out[5]:
0 1.000000e-100
1 1.000000e-25
2 ... | {
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```
>>> from pandas import MultiIndex, DataFrame
>>> test = MultiIndex.from_tuples([('a',), ('b', 'c'), ('b', 'd'), ('e',)])
>>> test
MultiIndex(levels=[[u'a', u'b', u'e'], [u'c', u'd']],
labels=[[0, 1, 1, 2], [-1, 0, 1, -1]])
>>> x = DataFrame(0, index=['row1'], columns=test)
>>> x
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-04-11T12:26:11Z | 2016-10-12T23:04:47Z | 2014-10-02T17:02:28Z | CONTRIBUTOR | null | we have a nice build system, though switching versions of sub-libraries requires rebuilding a whl (not that big a deal really).
that said, this is somewhat simpler as conda is pulling something like a whl which they are mainting, so switching versions of sub-libraries would be pretty easy.
https://github.com/statsmod... | {
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... | 15 | 2014-04-11T13:22:52Z | 2017-07-04T15:12:40Z | 2016-07-26T07:05:15Z | NONE | null | Test program below:
``` python
import datetime, pytz, pandas
pandas.show_versions()
dates = [datetime.datetime(2014,1,1,1,1, tzinfo=pytz.utc),
datetime.datetime(2014,2,2,2,2, tzinfo=pytz.utc),
datetime.datetime(2014,3,3,3,3, tzinfo=pytz.utc)]
s = pandas.Series(dates)
s.to_pickle('series.pickle')
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# Arithmetic
Applying arithmetic ops to `NaT` is not handled properly. Based on numpy results, I understand that results should be all `NaT` as long as valid data is passed.
```
# current results
>>> pd.NaT + pd.offsets.Hour(1)
2262-04-11 01:12:43.145224192
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Here is a simple example: I want to regress a variable on itself, in this case excess returns. The intercept should be 0, and the coefficient should be 1. pandas provides the wrong estimates, while statsmodels gives the correct estimates.
This is not d... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6875 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6875/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6875/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6875/events | https://github.com/pandas-dev/pandas/pull/6875 | 31,385,127 | MDExOlB1bGxSZXF1ZXN0MTQ2ODYzMDA= | 6,875 | BUG: data written with to_sql legacy mode (sqlite/mysql) not persistent GH6846 | {
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Tests are updated too in order to avoid future regression with a closed connection between writing and reading in the database. Since all the data in a memory based database in sqlite3 should be removed when closed, I cre... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6876 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6876/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6876/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6876/events | https://github.com/pandas-dev/pandas/pull/6876 | 31,394,384 | MDExOlB1bGxSZXF1ZXN0MTQ2OTAxNzU= | 6,876 | CLN: simplify series plotting | {
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- Simplify `LinePlot._make_ts_plot` to have single flow.
- Simplify `plot_frame` and `plot_series` 's `kind` detection. (Also, setting hexbin default is not necessary because it is done in `HexBinPlot.__init__`)
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https://api.github.com/repos/pandas-dev/pandas/issues/6877 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6877/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6877/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6877/events | https://github.com/pandas-dev/pandas/issues/6877 | 31,404,220 | MDU6SXNzdWUzMTQwNDIyMA== | 6,877 | Date issue in dataframe | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6878 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6878/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6878/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6878/events | https://github.com/pandas-dev/pandas/issues/6878 | 31,420,531 | MDU6SXNzdWUzMTQyMDUzMQ== | 6,878 | ENH: name(s) argument for reset_index? | {
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... | 6 | 2014-04-13T19:52:07Z | 2021-06-11T11:32:15Z | null | MEMBER | null | An idea: to be able to specify the a name when resetting the index, which would override the index name or the default value when index name is None.
Eg something like this:
```
In [10]: df = pd.DataFrame(np.random.rand(4,3), columns=list('ABC'), index=pd.date_range('2013-01-01', periods=4))
In [11]: df
Out[11... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6879 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6879/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6879/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6879/events | https://github.com/pandas-dev/pandas/pull/6879 | 31,426,886 | MDExOlB1bGxSZXF1ZXN0MTQ2OTkwNTU= | 6,879 | ENH: Float64Index now uses Float64Hashtable as a backend | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6880 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6880/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6880/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6880/events | https://github.com/pandas-dev/pandas/issues/6880 | 31,479,905 | MDU6SXNzdWUzMTQ3OTkwNQ== | 6,880 | DataFrame.to_csv sometimes ignores `header` argument | {
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If you call `DataFrame.to_csv` with `index=False` and a `header`, the header doesn't replace the column names. With `index=True`, `header` correctly replaces the csv header.
The code behind this: https://github.com/pydata/pandas/blob/master/pandas/core/format.py#L1080
Presumably we want to repl... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6881 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6881/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6881/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6881/events | https://github.com/pandas-dev/pandas/issues/6881 | 31,491,157 | MDU6SXNzdWUzMTQ5MTE1Nw== | 6,881 | API: SQL legacy mode to_sql 'index' kwarg behaviour | {
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] | closed | false | null | [] | null | 3 | 2014-04-14T19:08:47Z | 2014-04-17T11:56:12Z | 2014-04-15T22:32:19Z | MEMBER | null | A leftover from #6735. In this PR, multi-index support was added to the new `to_sql` and `read_table` functions based on sqlalchemy. However, I did not change anything in the legacy `to_sql` functions.
This has the following consequences for the `index` handling in legacy mode (https://github.com/pydata/pandas/blob/1... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6882 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6882/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6882/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6882/events | https://github.com/pandas-dev/pandas/issues/6882 | 31,500,993 | MDU6SXNzdWUzMTUwMDk5Mw== | 6,882 | PERF: Possible performance regression for indexing from 0.12 to 0.13.1 | {
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] | closed | false | null | [] | null | 11 | 2014-04-14T20:24:58Z | 2014-04-25T13:35:18Z | 2014-04-25T13:35:18Z | NONE | null | Hey all,
Just upgraded my pandas version from 0.12 to 0.13.1 and noticed a significant performance regression for indexing operations (get, set, and windowing).
Here is my test setup code:
``` python
import pandas as pd
ts1 = pd.TimeSeries(data=100.0, index=pd.date_range('2000-01-01', periods=1000))
ts2 = pd.TimeS... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6883 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6883/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6883/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6883/events | https://github.com/pandas-dev/pandas/pull/6883 | 31,501,074 | MDExOlB1bGxSZXF1ZXN0MTQ3Mzg3MTM= | 6,883 | SQL: add multi-index support to legacy mode | {
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@danielballan Added multi-index support to legacy mode, wasn't actually not that difficult (all tests seems to pass). And at once also `index_label` kwarg is supported in legacy mode.
This just leaves the issue that this is actually an api change, so could possibly break code.
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... | 52 | 2014-04-14T21:29:37Z | 2019-06-13T21:19:40Z | 2015-05-09T16:08:30Z | CONTRIBUTOR | null | closes #5724
Currently, `timedelta64` columns can't be used in aggregate group operations. They are quietly dropped or, if no other columns are present, an exception is raised:
```
In [2]: df
Out[2]:
A E
0 foo 00:00:01
1 bar 00:00:02
2 foo 00:00:03
3 bar 00:00:04
In [3]: df.groupby('A').mean()
Out[... | {
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In my code I have a category containing lots of non-English names and want to create dummies out of it.
So I call:
``` python
dummies=pandas.get_dummies(data[cat], prefix=prefix... | {
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"labels_url": "htt... | 17 | 2014-04-15T16:24:16Z | 2014-09-24T12:59:05Z | 2014-04-23T23:24:14Z | CONTRIBUTOR | null | Okay, this fixes #6868 but does so with a bit of a performance penalty. The current pandas version performs just a tad slower than `scipy.stats.rankdata()`, but after these changes, it's about 2-3x slower. On the plus side, it does (what I think is) the right thing.
I'm no cython expert, so there may well be things th... | {
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``` python
import pandas as pd
# version '0.12.0'
!wget -O 'Vertikale_Netzlast_2013.csv' 'http://www.50hertz.com/transmission/files/sync/Netzkennzahlen/Netzlast/ArchivCSV/Vertikale_Netzlast_2013.csv'
df = pd.read_csv('Vertikale_Netzlast_2013.csv', header=6, sep=';', parse_dates=[[0, 1]], index_col... | {
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closes #3374
Currently, specifying options that are incompatible with the C parser in `read_csv` and `read_table` causes a silent fallback to the python engine. This can be confusing if the user has also passed options that are only supported by the C engine, which are then silently ignored. (See #6607)
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https://api.github.com/repos/pandas-dev/pandas/issues/6890 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6890/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6890/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6890/events | https://github.com/pandas-dev/pandas/pull/6890 | 31,619,180 | MDExOlB1bGxSZXF1ZXN0MTQ4MDk4NjQ= | 6,890 | ENH (GH6568) Add option info_verbose | {
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Closes #6568
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Currently there are no commands to count the number of cells being updated so you can put it in a log or something when `df.update(df2)` is performed.
See:
http://stackoverflow.com/questions/23102757/python-pandas-updating-dataframe-and-... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6892 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6892/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6892/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6892/events | https://github.com/pandas-dev/pandas/issues/6892 | 31,675,510 | MDU6SXNzdWUzMTY3NTUxMA== | 6,892 | how to set the different panels of pandas df.boxplot() to the same x and y scales? | {
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I am trying to plot a 4 panels figure using pandas df.boxplot() with the same x and y axis for comparison (the dataframe contains 4 columns of variable length, completed with NaN)
While trying to use plt.xlim and plt.ylim, it changes only the scales of the last plot...
How to perform this on the 4 pannels ?... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6893 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6893/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6893/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6893/events | https://github.com/pandas-dev/pandas/issues/6893 | 31,680,313 | MDU6SXNzdWUzMTY4MDMxMw== | 6,893 | read_table fails with MultiIndex input and delim_whitespace=True | {
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... | 17 | 2014-04-16T21:13:08Z | 2020-04-24T21:18:03Z | null | CONTRIBUTOR | null | Related #6889
Example:
``` python
In [4]: text = """ A B C D E
one two three four
a b 10.0032 5 -0.5109 -2.3358 -0.4645 0.05076 0.3640
a q 20 4 0.4473 1.4152 0.2834 1.00661 0.1744
x q 30 3 -0.6662 -0.5243 -0.3580 0.89145 2.5838"""... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6894 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6894/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6894/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6894/events | https://github.com/pandas-dev/pandas/issues/6894 | 31,681,442 | MDU6SXNzdWUzMTY4MTQ0Mg== | 6,894 | 0.13.1: DataFrame.xs() ValueError: Cannot retrieve view (copy=False) | {
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"html_url": "https://github.com/pandas-dev/pandas/milestone/24",
"id": 526062,
"labels_url": "htt... | 16 | 2014-04-16T21:28:31Z | 2014-04-22T13:09:16Z | 2014-04-22T13:09:16Z | NONE | null | I have python 2.7.6 and pandas 0.13.1 on Windows 7.
```
>>>portfolio_data_all.index.names
FrozenList([u'date', u'stock_id'])
>>>portfolio_data_all.xs(cusip_etf, level=field_stock_id, copy=False)
---------------------------------------------------------------------------
ValueError Traceb... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6895 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6895/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6895/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6895/events | https://github.com/pandas-dev/pandas/issues/6895 | 31,687,122 | MDU6SXNzdWUzMTY4NzEyMg== | 6,895 | DOC: Incorrect whatsnew entry for GH6459 | {
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"labels_url": "htt... | 5 | 2014-04-16T22:51:01Z | 2014-04-23T00:37:46Z | 2014-04-23T00:37:46Z | MEMBER | null | The whatsnew entry for PR #6459: http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html at IPython input prompt 22, the example code is:
`set_index` no longer converts MultiIndexes to an Index of tuples. For example, the old behavior returned an Index in this case (GH6459):
```
In [22]: df_multi.index
Out[22]:... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6896 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6896/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6896/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6896/events | https://github.com/pandas-dev/pandas/issues/6896 | 31,691,822 | MDU6SXNzdWUzMTY5MTgyMg== | 6,896 | to_csv puts extra comma before columns | {
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``` python
towrite.to_csv()
',jdate,orbit,sundist\n2010-12-20 10:00:00.074000,2455550.916667521,6867.0,0.98631\n2010-12-20 10:00:00.202000,2455550.916669002,6867.0,0.98631\n2010-12-20 10:00:00.330000,2455550.916670484,6867.0,0.98631\n2010-12-20 10:00:00.458000,2455550.916671965,6867.0,0.... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6897 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6897/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6897/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6897/events | https://github.com/pandas-dev/pandas/issues/6897 | 31,712,910 | MDU6SXNzdWUzMTcxMjkxMA== | 6,897 | ENH: Standard Error of the Mean (sem) aggregation method | {
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"followers_url": "https://api.github.com/users/jr... | 15 | 2014-04-17T09:27:47Z | 2020-04-25T12:29:37Z | 2014-06-05T10:22:55Z | CONTRIBUTOR | null | A very common operation when trying to work with data is to find out the error range for the data. In scientific research, including error ranges is required.
There are two main ways to do this: standard deviation and standard error of the mean. Pandas has an optimized std aggregation method for both dataframe and g... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6898 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6898/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6898/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6898/events | https://github.com/pandas-dev/pandas/issues/6898 | 31,715,213 | MDU6SXNzdWUzMTcxNTIxMw== | 6,898 | ENH: Aggregate to data with error range | {
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"f... | 5 | 2014-04-17T10:07:06Z | 2021-04-11T04:28:19Z | null | CONTRIBUTOR | null | As mentioned in issue #6897, working with data with error ranges is pretty much universal in science, as well as many other fields. There are python packages, like `uncertainties`, for working with this sort of data. However, pandas has no built-in tools for creating or working with data with error ranges, leaving us... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6899 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6899/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6899/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6899/events | https://github.com/pandas-dev/pandas/issues/6899 | 31,717,863 | MDU6SXNzdWUzMTcxNzg2Mw== | 6,899 | read_pickle much slower in v0.13 (not using cPickle when compat=False) | {
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"labels_url": "htt... | 11 | 2014-04-17T10:55:57Z | 2014-04-28T13:08:44Z | 2014-04-27T22:15:44Z | NONE | null | See discussion here http://stackoverflow.com/questions/23122180/is-pandas-read-pickle-performance-crippled-in-version-0-13
My test dataset has pickled file size 1.6Gb, and contains about 13 million records.
With 0.12 the file takes 146s to load; with 0.13 is takes 982s (about 6.7x longer).
Using cProfile, you can se... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6900 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6900/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6900/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6900/events | https://github.com/pandas-dev/pandas/issues/6900 | 31,720,724 | MDU6SXNzdWUzMTcyMDcyNA== | 6,900 | SQL: Status of the legacy mode? | {
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"labels_url": "htt... | 21 | 2014-04-17T11:53:34Z | 2014-05-11T09:54:15Z | 2014-05-11T09:54:15Z | MEMBER | null | To discuss: do we regard the sql legacy mode (using a DBAPI2 connection, for sqlite and mysql) as a _'first-class citizen'_ in pandas? Or is it mainly for backwards compatibility?
Is it useful to have something that is not dependent on sqlalchemy? And consequently we alo try to enhance it /accept enhancements to it? O... | {
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