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| 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' : pd.Series(['Apples', 'Apples', 'Apples', 'Apples', 'Oranges', 'Oranges', 'Oranges', 'Oranges']), \
'Method' : pd.Series(['Abstract', 'Surreal', 'Abstract', 'Surreal', 'Abstract', 'Surreal', 'Abstract', 'Surreal'])}
df=pd.DataFrame(d)
# Create full table with correct margin calculation
table_full=pd.crosstab( \
[df.Fruit], \
[df.Method], \
margins=True)
# Create table based on filtered database. Notice margins are no longer correct.
table_filtered=pd.crosstab( \
[df.Fruit[df.Fruit=='Apples']], \
[df.Method], \
margins=True)
```
Output:
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} | 2 | 2014-04-04T19:09:34Z | 2014-06-21T14:30:35Z | 2014-04-06T18:32:34Z | CONTRIBUTOR | null | Otherwise writing fails with errors about len() applied to a float or NoneType.
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} | 8 | 2014-04-04T19:51:55Z | 2014-06-21T09:55:59Z | 2014-04-05T20:18:58Z | MEMBER | null | closes #6762
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| null | 5 | 2014-04-04T20:20:46Z | 2014-04-08T15:37:04Z | 2014-04-07T14:35:50Z | CONTRIBUTOR | null | Hello.
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 open instance of pandas.io.pytables.HDFStore)
Until that functionality is implemented, does anyone know if there is a way to work-around it? - perhaps via the underlying PyTables implementation of something similar?
In case it is relevant, I'm executing `pandas.computation.expressions.set_use_numexpr(False)`
near the start of my application to avoid the crashing issue mentioned in #6720
I'm not familiar enough with the source to be able propose a fix myself at this time.
Thanks!
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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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} | 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.
Note that:
``` python
df.T.sum(axis=1).values # array([6, -1.4781891805780178, 'abc'], dtype=object)
```
Maybe `com._coerce_to_dtypes` should just return the list?
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} | 9 | 2014-04-05T10:36:08Z | 2014-06-13T15:26:22Z | 2014-04-30T00:16:31Z | CONTRIBUTOR | null | fopen() in Windows doesn't accept utf-8 encoded filename with multibyte characters, so need to convert it to filesystem encoding.
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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} | 6 | 2014-04-05T13:22:04Z | 2014-04-06T00:45:11Z | 2014-04-05T15:24:51Z | NONE | null | Hello,
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_datetime(datetime.datetime.now())
@buddha[J:T26]|6> offset = pd.to_timedelta(10, unit='s')
@buddha[J:T26]|7> offset
<7> numpy.timedelta64(10000000000,'ns')
@buddha[J:T26]|8> base
<8> Timestamp('2014-04-05 13:13:40.374000', tz=None)
@buddha[J:T26]|9> base + offset
<9> Timestamp('2014-04-05 13:13:41.784065408', tz=None)
```
If I try to do the same by creating a datetime.timedelta then everything works as expected.
```
@buddha[J:T26]|10> offset2 = datetime.timedelta(seconds = 10)
@buddha[J:T26]|11> base + offset2
<11> Timestamp('2014-04-05 13:13:50.374000', tz=None)
```
This is really confusing and hard to spot. I think that pd.to_datetime and pd.to_timedelta should be compatible, but if they are not, please make the error easier to spot.
Thanks,
Adam
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} | 3 | 2014-04-05T14:10:05Z | 2014-06-23T14:03:11Z | 2014-06-23T14:03:11Z | CONTRIBUTOR | null | http://nbviewer.ipython.org/urls/umich.box.com/shared/static/oh717lkxczhseep71lao.ipynb
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} | 6 | 2014-04-05T14:27:01Z | 2014-06-17T18:33:53Z | 2014-04-16T13:04:41Z | CONTRIBUTOR | null | PR to fix #5824.
- 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).
- Fixes a test that fails after the switch due to differences in how fractions are computed. (Created a `np.timedelta64` directly since `to_timedelta` doesn't support enough precision.)
Let me know if you'd like to see any changes..
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| null | 7 | 2014-04-05T15:28:06Z | 2014-08-05T22:20:25Z | 2014-04-05T18:06:13Z | CONTRIBUTOR | null | Fixes #6782 by converting inf values to string (default is `'inf'`).
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 decision _for_ the writers. This also
(implicitly) fixes an issues where round-tripping an Excel file with an
inf value would cause an `OverflowError` and fail to read back.
cc @jmcnamara and @arthurgerigk-rocket
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} | 23 | 2014-04-05T19:32:39Z | 2014-06-14T21:54:29Z | 2014-04-08T22:37:52Z | CONTRIBUTOR | null | Fixes #6641
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
.\pandas\core\format.py:{442, 369}
.\pandas\core\frame.py:{1387, 1342, 1283}
- [x] Deprecate nanRep in favor of na_rep (#275) -- v0.5.0
.\pandas\core\frame.py:{1285, 1133}
.\pandas\core\series.py:{910}
- [x] Deprecate unique in HDFStore (#3256) -- v0.11.0
.\pandas\io\pytables.py:{691}
- [x] Deprecate time_rule favor freq (#1042) -- v0.8.0
.\pandas\stats\moments.py:{554}
- [x] Deprecate inferTimeRule in favor of infer_freq (#391) -- v0.8.0
.\pandas\tseries\frequencies.py:{247}
- [x] Deprecate DateRange in favor of DatetimeIndex (no issue number, commit 6fe2db57) -- v0.8.0
.\pandas\core\daterange.py:{24} (will be done in #6816)
- [x] Deprecate 'name' in favor of 'symbols' (no issue, commit b921d1a2) -- v0.11.0
.\pandas\io\data.py:{344}
- [x] Deprecate offset in favor of freq (no issue, commit 31363905c20) -- v0.8.0
.\pandas\tseries\index.py:{180}
##### Deprecated since 0.12 or after
Issue #6581 documents the following for future removal. **These are not included in this PR**
- [ ] ~~Deprecate na_last in favor of na_position (#5231, #3917)~~
.\pandas\core\series.py:{1770}
- [ ] ~~Deprecate rolling_corr_pairwise and expanding_corr_pairwise (#4950)~~
.\pandas\stats\moments.py:{301,957}
- [ ] ~~Deprecate broadcasting TimeSeries along DataFrame index (#2304)~~
.\pandas\core\frame.py:{2837}
- [ ] ~~Deprecate load and save in favor of pickle and to_pickle (#3787)~~
.\pandas\core\common56.py:{2740, 2756}
.\pandas\core\generic.py:{960, 967}
- [ ] ~~Deprecate timeRule and offset in favor of freq (#4853, #4864)~~
.\pandas\core\datetools.py:{55}
- [ ] ~~Deprecate colSpace in favor of col_space (no issue, commit 4a5a677c)~~
.\pandas\core\frame.py:{1292,1346,1392}
- [ ] ~~Deprecate year/month parameters in finance option signatures (#3822, #3817)~~
.\pandas\io\data.py:{588,811,848}
- [ ] ~~Remove kind from read_excel (#4713)~~
.\pandas\io\excel.py:{103}
- [ ] ~~Deprecate infer_types to have no effect (#4770)~~
.\pandas\io\html.py:{830}
- [ ] ~~Deprecate table keyword in HDFStore (#4645)~~
.\pandas\io\pytables.py:{1124}
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| null | 1 | 2014-04-06T01:23:58Z | 2014-04-06T14:29:40Z | 2014-04-06T14:28:34Z | CONTRIBUTOR | null | Weird to deprecate offset keyword argument and not offset property. See #6813 for more context. If we do this, need to:
1. Add getter/setter/deleter property that warns on usage.
2. Migrate all code that refers to `Timestamp.offset` to use `Timestamp.freq` instead.
Then we could remove a few versions from now (hopefully pre-1.0)
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} | 2 | 2014-04-06T01:30:57Z | 2014-06-14T20:22:26Z | 2014-04-08T23:05:55Z | CONTRIBUTOR | null | Deprecate DateRange in favor of DatetimeIndex, perfromed in commit 6fe2db57. Deprecation warning was first given in v0.8.
Related: #6641, #6813
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} | 19 | 2014-04-06T08:47:22Z | 2014-09-16T14:33:52Z | 2014-04-22T21:58:47Z | CONTRIBUTOR | null | This PR implements a modified version of Welford's method to compute
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.
The formulas to update these two variables when adding or removing an
item from the sequence are well known, see e.g.
http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
The formulas used when both adding one and removing one item I have
not seen explicitly worked out anywhere, but are not too hard to come
up with if you put pen to (a lot of) paper.
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} | 2 | 2014-04-06T13:59:40Z | 2014-06-26T02:50:17Z | 2014-04-07T12:37:33Z | MEMBER | null | ```
>>> 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
>>> idx + datetime.timedelta(hours=3)
[2010-11-02 04:00:00-07:00, ..., 2010-11-02 06:00:00-07:00]
Length: 3, Freq: H, Timezone: US/Pacific
>>> idx + np.timedelta64(3, 'h') # incorrect
[2010-11-02 11:00:00-07:00, ..., 2010-11-02 13:00:00-07:00]
Length: 3, Freq: H, Timezone: US/Pacific
```
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"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/24"
} | 10 | 2014-04-06T19:01:40Z | 2014-04-28T23:21:56Z | 2014-04-28T23:21:56Z | CONTRIBUTOR | null | related: https://github.com/pydata/pandas/pull/6605
```
panel_shift | 263.2827 | 0.0746 | 3528.0756 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [e84efe5] : Merge pull request #6825 from cpcloud/replace-dict-vbenches
BENCH: add vbench for issue 6697
Base [d10a658] : RLS: set released to True. v0.13.1
```
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| null | 6 | 2014-04-06T19:53:06Z | 2014-07-10T00:07:04Z | 2014-06-10T07:08:28Z | CONTRIBUTOR | null | Not really for merging, but I wanted to see how [docformatter](https://github.com/myint/docformatter) handled codebase (e.g. any bugs). Perhaps we can make these changes incrementally.
cc #6248
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| 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_stock_price(year=2014)
/usr/local/lib/python2.7/dist-packages/pandas-0.13.1_347_g71c83e0-py2.7-linux-x86_64.egg/pandas/io/data.pyc in get_near_stock_price(self, above_below, call, put, month, year, expiry)
792
793 try:
--> 794 df = getattr(self, name)
795 except AttributeError:
796 meth_name = 'get_{0}_data'.format(nam[:-1])
UnboundLocalError: local variable 'name' referenced before assignment
```
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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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| 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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| 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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| null | 1 | 2014-04-07T08:41:01Z | 2014-04-10T07:54:07Z | 2014-04-07T08:58:20Z | NONE | null | I am tring to pass {'constant_memory' = True} with xlsxengine (to increase speed).
```
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: "'Workbook' object has no attribute 'fileclosed'" in <bound method Workbook.__del__ of <xlsxwriter.workbook.Workbook object at 0x10dcf68d0>> ignored
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-54-d2dacd68b48f> in <module>()
----> 1 writer=pd.ExcelWriter('raw_data_dump.xlsx', engine='xlsxwriter', engine_kwargs={'constant_memory':True})
2 raw_data.to_excel(writer, sheet_name='Sheet1')
3 writer.save()
/usr/local/Cellar/python/2.7.4/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/io/excel.pyc in __init__(self, path, engine, date_format, datetime_format, **engine_kwargs)
724 **engine_kwargs)
725
--> 726 self.book = xlsxwriter.Workbook(path, **engine_kwargs)
727
728 def save(self):
TypeError: __init__() got an unexpected keyword argument 'engine_kwargs'
```
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| null | 1 | 2014-04-07T09:08:24Z | 2014-07-16T09:00:53Z | 2014-04-07T10:02:06Z | MEMBER | null | Small fix, avoiding repeating a sentence.
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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.randn(10,2)))
>>> store.append('df/foo2',DataFrame(np.random.randn(10,2)))
>>> store.get_group('df')
... 0 1
... 0 -0.495847 -1.449251
... 1 -0.494721 1.572560
... 2 1.219985 0.280878
... 3 -0.419651 1.975562
... 4 -0.489689 -2.712342
... 5 -0.022466 -0.238129
... 6 -1.195269 -0.028390
... 7 -0.192648 1.220730
... 8 1.331892 0.950508
... 9 -0.790354 -0.743006
... 0 -0.761820 0.847983
... 1 -0.126829 1.304889
... 2 0.667949 -1.481652
... 3 0.030162 -0.111911
... 4 -0.433762 -0.596412
... 5 -1.110968 0.411241
... 6 -0.428930 0.086527
... 7 -0.866701 -1.286884
... 8 -0.649420 0.227999
... 9 -0.100669 -0.205232
...
... [20 rows x 2 columns]
```
I never did a pull request before, nor tried to dive into pandas code, but I guess that a possible implementation (inspired in [this answer](http://stackoverflow.com/a/22897898/630598)) would be something like:
```
class HDFStore():
...
def get_group(self, group_name):
return pd.concat([ self.select(node._v_pathname) for node in self.get_node(group_name) ])
...
```
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} | 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 errorbars actually drawn.
- There are some types which expected to work, but results in `ValueError`.
- `float` (MPL can accept it)
- `unicode` (in Python 2.x)
- Other list-like, such as `iterator`
- A bug related to time series.
```
>>> s = Series(rand(6), index=range(6), name='x')
>>> err_df = DataFrame(rand(6, 3) / 10, index=range(6), columns=['x', 'y', 'z'])
>>> s.plot(yerr=err_df) # This works, errorbar appears using err_df x column
>>> s.index = tm.makeDateIndex(k=6)
>>> s.plot(yerr=err_df) # But this doesn't, errorbar doesn't appear
```
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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.onOffset(ts)
[21]: True
```
`onOffset` doesn't do anything with `n`, so it think January is on the offset when only April and October are expected to be on the offset. Shouldn't onOffset know about n? I can look into this, if it should. I'd like to refamilarize myself with this code a bit.
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| 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
586.5 586.5 586.8 586.8 587.2 587.6 587.1 586.8 586.9 586.7
586.5 586.4 586.3 586.2 586.2
........
586.2 585.9 585.9 586.4 586.3 586.2 586.4 586.3 586.2 586.4
586.4 585.9 585.8 585.5 585.5 585.5 585.6 586.0 586.4 586.6
586.8 587.0 587.1 587.1 587.1 586.8 586.7 586.3 586.0 586.2
586.1 586.4 586.7 586.9 587.3 587.0 587.3 587.0 587.0 586.8
586.6 586.5 586.2 586.3 586.0
I want to get the data of 45 columns \* 10 rows ,but I can only get 10 column \* 50 lines of data by the "read_csv" method,because each row with a carriage return,so a row of data is divided into five rows,how to fix it?
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} | 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):
"""
Parameters
---------------
key : the resulting group for the table
generator : a list-like / iterator / generator to source objects
if string-like then read these from the same file name (and same key name)
index : boolean, default True,
create an index on the resulting table
append : boolean, default False
if True, append to the resulting table, otherwise remove first
"""
# remove the group if it exists (and not appending)
if key in self and not append:
self.remove(key)
for obj in generator:
# a string - treat as a file-name
if isinstance(obj, compat.string_types):
with get_store(obj, mode='r') as s:
obj = s.get(key)
self.append(key, obj, index=False)
if index:
self.create_index(columns=index)
```
HDF files
```
with get_store('concat.hdf',mode='w') as store:
store.concat('data',['file1.hdf','file2','hdf'])
```
Objects
```
with get_store('concat.hdf',mode='w') as store:
store.concat('data',[DataFrame(...), DataFrame(....)])
```
Generator
```
def f():
for f in files:
df = read_csv(f)
yield df
with get_store('concat.hdf',mode='w') as store:
store.concat('data',f)
```
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} | 10 | 2014-04-08T13:12:00Z | 2014-07-07T07:08:31Z | 2014-07-07T07:08:31Z | CONTRIBUTOR | null | Warning. NIt-picky here.
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 numpy docstring standard and the lines are not 79 characters so they wrap on my terminal (not obvious in the below but tough to grok in the terminal). Any else find this hard to read? Any thoughts on a better format? I think fixing the line lengths, moving the types to the same line as the option, and moving the default/currently to a new indented line at end of the description would go a long way.
```
display.chop_threshold: [default: None] [currently: None]
: float or None
if set to a float value, all float values smaller then the given threshold
will be displayed as exactly 0 by repr and friends.
display.colheader_justify: [default: right] [currently: right]
: 'left'/'right'
Controls the justification of column headers. used by DataFrameFormatter.
display.column_space: [default: 12] [currently: 12]No description available.
display.date_dayfirst: [default: False] [currently: False]
: boolean
When True, prints and parses dates with the day first, eg 20/01/2005
display.date_yearfirst: [default: False] [currently: False]
: boolean
When True, prints and parses dates with the year first, eg 2005/01/20
display.encoding: [default: UTF-8] [currently: UTF-8]
: str/unicode
Defaults to the detected encoding of the console.
Specifies the encoding to be used for strings returned by to_string,
these are generally strings meant to be displayed on the console.
display.expand_frame_repr: [default: True] [currently: True]
: boolean
Whether to print out the full DataFrame repr for wide DataFrames across
multiple lines, `max_columns` is still respected, but the output will
wrap-around across multiple "pages" if it's width exceeds `display.width`.
display.float_format: [default: None] [currently: None]
: callable
The callable should accept a floating point number and return
a string with the desired format of the number. This is used
in some places like SeriesFormatter.
See core.format.EngFormatter for an example.
display.height: [default: 60] [currently: 60]
: int
Deprecated.
(Deprecated, use `display.max_rows` instead.)
```
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} | 2 | 2014-04-08T13:16:01Z | 2017-07-01T06:54:06Z | 2017-07-01T06:54:06Z | CONTRIBUTOR | null | xref #6842
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_printoptions(suppress=True)
In [80]: df.as_matrix()
Out[80]:
array([[ 10., 0.],
[ 20., -0.],
[ 30., 0.],
[ 40., -0.]])
From the docstring, it appears that the 'chop_threshold' option can be
used to do the same thing (and with a threshold as a parameter), but
in this case, it does something weird (perhaps a bug?):
In [81]: pd.set_option('chop_threshold', 1e-8)
In [82]: df
Out[82]:
0 1
0 10
1 20
2 30
3 40
```
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https://api.github.com/repos/pandas-dev/pandas/issues/6840 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6840/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6840/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6840/events | https://github.com/pandas-dev/pandas/pull/6840 | 31,070,647 | MDExOlB1bGxSZXF1ZXN0MTQ0OTk0MTc= | 6,840 | Additional documentation for holiday calendars | {
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} | 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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} | 17 | 2014-04-08T14:14:06Z | 2017-05-15T21:15:59Z | 2014-04-10T19:23:13Z | CONTRIBUTOR | null | Doc formatting issue brought up in https://github.com/pydata/pandas/pull/6661#issuecomment-39748330
@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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| 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'.
```
col_0 col_1 col_2 col_3
2013-01-18 3.132256e+00 NaN NaN NaN
2013-01-21 3.513191e+00 NaN NaN NaN
2013-01-22 2.581368e+00 NaN NaN NaN
2013-01-23 1.751014e+00 NaN NaN NaN
2013-01-24 2.508899e+00 NaN NaN NaN
2013-01-25 4.085701e+00 NaN NaN NaN
2013-01-28 3.400867e+00 NaN NaN NaN
2013-01-29 3.030346e+00 NaN NaN NaN
2013-01-30 2.934522e+00 NaN NaN NaN
2013-01-31 6.548878e-01 NaN NaN NaN
2013-02-01 4.743263e-01 NaN NaN NaN
2013-02-04 5.372891e-02 NaN NaN NaN
2013-02-05 8.271173e-02 NaN NaN NaN
2013-02-06 9.247646e-03 NaN NaN NaN
2013-02-07 1.499508e-03 NaN NaN NaN
2013-02-08 6.927766e-04 NaN NaN NaN
2013-02-11 7.232857e-05 NaN NaN NaN
2013-02-12 3.274444e-05 NaN NaN NaN
2013-02-13 4.520291e-07 NaN NaN NaN
2013-02-14 2.092958e-16 NaN NaN NaN
2013-02-15 0.000000e+00 6.815180 NaN NaN
2013-02-18 NaN 7.581717 NaN NaN
2013-02-19 NaN 15.039587 NaN NaN
2013-02-20 NaN 10.740931 NaN NaN
2013-02-21 NaN 4.101203 NaN NaN
2013-02-22 NaN 9.078457 NaN NaN
2013-02-25 NaN 13.372686 NaN NaN
2013-02-26 NaN 4.394059 NaN NaN
2013-02-27 NaN 4.621631 NaN NaN
2013-02-28 NaN 5.737833 NaN NaN
2013-03-01 NaN 3.454591 NaN NaN
2013-03-04 NaN 2.521326 NaN NaN
2013-03-05 NaN 9.589268 NaN NaN
2013-03-06 NaN 8.321083 NaN NaN
2013-03-07 NaN 8.069279 NaN NaN
2013-03-08 NaN 16.680768 NaN NaN
2013-03-11 NaN 9.940089 NaN NaN
2013-03-12 NaN 5.922898 NaN NaN
2013-03-13 NaN 2.206807 NaN NaN
2013-03-14 NaN 9.449342 NaN NaN
2013-03-15 NaN 0.000000 5.331617 NaN
2013-03-18 NaN NaN 3.320303 NaN
2013-03-19 NaN NaN 2.480900 NaN
2013-03-20 NaN NaN 2.787095 NaN
2013-03-21 NaN NaN 2.157647 NaN
2013-03-22 NaN NaN 2.795969 NaN
2013-03-25 NaN NaN 0.884890 NaN
2013-03-26 NaN NaN 0.425347 NaN
2013-03-27 NaN NaN 0.376083 NaN
2013-03-28 NaN NaN 0.332580 NaN
2013-04-02 NaN NaN 0.689754 NaN
2013-04-03 NaN NaN 0.256095 NaN
2013-04-04 NaN NaN 0.157584 NaN
2013-04-05 NaN NaN 0.084014 NaN
2013-04-08 NaN NaN 0.023838 NaN
2013-04-09 NaN NaN 0.007898 NaN
2013-04-10 NaN NaN 0.064029 NaN
2013-04-11 NaN NaN 0.047283 NaN
2013-04-12 NaN NaN 0.010981 NaN
2013-04-15 NaN NaN 0.000285 NaN
... ... ... ...
[64 rows x 4 columns]
```
```
df.values
array([[ 3.13225571e+00, nan, nan,
nan],
[ 3.51319052e+00, nan, nan,
nan],
[ 2.58136815e+00, nan, nan,
nan],
[ 1.75101397e+00, nan, nan,
nan],
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nan],
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nan],
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nan],
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nan],
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nan],
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nan],
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nan],
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nan],
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nan],
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nan],
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nan],
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nan],
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nan],
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nan],
[ nan, nan, 4.25347224e-01,
nan],
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nan],
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nan],
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nan],
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nan],
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nan],
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nan],
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nan],
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nan],
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nan],
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nan],
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nan],
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nan],
[ nan, nan, 6.65980697e-23,
nan],
[ nan, nan, 0.00000000e+00,
4.83556226e+00]])
```
```
df.index.values
array(['2013-01-18T00:00:00.000000000+0000',
'2013-01-21T00:00:00.000000000+0000',
'2013-01-22T00:00:00.000000000+0000',
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```
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| null | 6 | 2014-04-08T16:34:49Z | 2014-04-09T12:33:25Z | 2014-04-09T02:43:28Z | CONTRIBUTOR | null | I have a problem when writing some data into an SQL database in sqlite3 through the to_sql (and write_frame) method. I reduced the problem with only a few of the data, and can't reproduce with random data.
Let take the following csv file:
``` bash
$ cat /tmp/file.csv
objid,stellarmass_r-z
1237661976011866148,10.92719608
1237662237467279416,10.680375280000003
1237658491208794136,10.354045940000002
1237658629157879827,10.19681978
1237658629695537327,10.28878822
1237661971185270795,10.18847402
1237658630770720842,10.18515162
1237664291001860141,10.18533078
1237658630769737814,10.05068674
1237661070337703967,10.02273786
1237654606947155983,10.174803600000002
1237662236930342972,9.8948685
1237661070337245268,9.971118260000004
1237661950255366168,10.083547300000003
1237661068728139808,9.867372900000003
1237662236392947770,10.139080600000002
1237661811738935340,9.98884806
1237661971722141706,10.13716722
1237658628620877841,9.7847806
1237658492818948135,10.01117626
1237661976548016173,9.987120960000002
1237661970648399911,9.96022516
1237661950255169580,9.97642532
1237661974936944710,10.07319712
1237658629695537161,9.89437576
1237664292076584979,9.845949080000002
1237662236393865225,9.95083568
1237661948645212163,9.805305520000003
1237668589187301427,9.8581666
1237661069263372384,10.162577240000003
1237661977085542412,9.8445422
1237658629159845928,9.94675
1237661812812415036,9.71412302
1237661974935830545,9.97575908
1237655125009629193,9.71575088
1237661813348106345,9.61460742
1237654605873676293,9.826896680000004
1237662525766828100,9.813211940000002
1237662239077498913,9.835223640000002
1237668588650692644,9.72306616
1237654605873479684,9.822821340000004
1237661972796604601,9.55444232
1237661951329435708,9.76327192
1237661970112643097,9.52970244
1237658491745599505,9.64106222
1237661975474012252,9.8955774
1237658491744944240,9.81476334
1237658628621860878,9.66009456
1237661972260061194,9.53882956
1237658628621074512,9.48091508
1237661971184877624,9.68729046
1237662236930474097,9.78220464
1237655126620045406,9.757867780000002
1237661813349220495,9.76147096
1237661971185795108,9.6245947
1237654606409760858,9.506159320000004
1237661813886746643,9.336260700000002
1237661069264617519,9.65304386
1237664289392558172,9.63690456
1237668623012200453,9.6114549
1237662238541283500,9.44454652
1237658628620877914,9.35708458
1237668588650299428,9.53530032
1237662525767352343,7.849380880000003
1237661972796866572,9.518680880000002
1237655126083174431,9.7224362
1237662526839914522,8.855016099999999
1237658629696913469,9.23278334
1237661976547491868,9.5446055
1237661971186188308,9.6738568
1237661950791057448,9.3330057
1237662238004609184,9.34737388
1237658491208466514,-4.552070699999998
1237658629695275065,9.35274034
1237654605873479808,9.389559000000002
1237661970649710733,9.20148508
1237661974399483956,9.023542599999999
1237661812275740750,9.42462462
1237661971723059346,9.27588078
1237654606410547205,9.43386082
1237661815485562947,9.40952882
1237658629159976966,9.402022700000002
1237661976010752146,9.19800654
1237654605873414182,9.43241374
1237661971185991818,9.26299112
1237655126620831784,8.90218828
1237661974937010350,9.1063516
1237661976010227741,9.23574972
1237655126620045511,9.1018017
1237661811202457690,9.337958960000002
1237655126083240130,9.48160272
1237664290465316880,9.180347439999998
1237654606410219714,9.379238660000002
1237655126082977857,9.3410276
1237661971723452464,9.182900720000003
1237655125546565757,9.38576674
1237662239079268544,9.30403386
1237662524694528046,9.64007364
1237661950256218200,9.18688404
1237661069263437997,9.26908462
```
Then with pandas I read it, and put it into sqlite3 database, and read it again to see if they are identical:
``` python
>>> import pandas as pd
>>> import numpy as np
>>> import sqlite3
>>> data = pd.read_csv("/tmp/file.csv")
>>> print(len(np.unique(data.objid)))
100
>>> conn = sqlite3.connect("/tmp/file.db")
>>> data.to_sql("DATA", conn, if_exists="replace")
>>> result = pd.read_sql("SELECT objid FROM DATA;", conn)
>>> print(len(np.unique(result.objid)))
98
```
The problem disappear if there is no column "stellarmass_r-z" which is a float64 in the data dataframe. It should be a dtype problem but can't point it out! I tried it on an other system with different version of sqlite3 so the problem isn't really the version... (3.7.9 and 3.8.4.3). No problem if I use to_hdf and to_csv.
## INSTALLED VERSIONS
commit: None
python: 3.4.0.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.8-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: fr_FR.utf8
pandas: 0.13.1
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.13.3
statsmodels: None
IPython: 2.0.0
sphinx: 1.2.2
patsy: None
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.2
bottleneck: None
tables: 3.1.0
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
sqlalchemy: None
lxml: None
bs4: None
html5lib: None
bq: None
apiclient: None
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} | 1 | 2014-04-09T01:20:28Z | 2014-06-23T03:13:56Z | 2014-04-09T01:58:22Z | CONTRIBUTOR | null | {
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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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} | 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 database file isn't written, but the structure of the table is created (I assume a pylab environment set with ipython...):
``` python
>>> import pandas as pd
>>> import sqlite3
>>> data = pd.DataFrame({"galid":randint(2**63-1, size=100), "objid": randint(2**63-1, size=100), "alpha": rand(100)})
>>> conn = sqlite3.connect("/tmp/bidulechouette.db")
>>> data.to_sql("DATA", conn, if_exists="replace", flavor="sqlite", index=False)
>>> # just to be sure...
>>> conn.close()
>>> conn = sqlite3.connect("/tmp/bidulechouette.db")
>>> result = pd.read_sql("SELECT objid FROM DATA;", conn)
>>> len(result)
0
```
I tried it on two different systems with different versions of sqlite3 and python(3.2 and 3.4).
If I kill ipython and redo the same without the if_exists option on the same database file, it complains that the table already exists, even if I manually remove the database file of sqlite3. This lets me suppose, that somewhere, a reference to the database is kept, but it's weird because ipython is killed... Or the file in which it write isn't the good one, since with a lot of data, it takes a long time as it is writing the data somewhere, explaining the problem of existing table in a deleted database...
## INSTALLED VERSIONS
commit: None
python: 3.4.0.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.8-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: fr_FR.utf8
pandas: 0.13.1-605-g61ea0a3
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.13.3
statsmodels: None
IPython: 2.0.0
sphinx: 1.2.2
patsy: None
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.2
bottleneck: None
tables: 3.1.0
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
bq: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
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} | 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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} | 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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| 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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| null | 11 | 2014-04-09T15:59:23Z | 2014-07-16T09:01:15Z | 2014-04-23T02:21:47Z | CONTRIBUTOR | null | The primary argument used when plotting histograms is `bins`. This argument is not in the pandas documentation so you do not see it when auto-tabbing for documentation. I have added `bins=10` as an argument to the dataframe and series histogram function while also adding a line of documentation. `10` is the matplotlib default bin value so this is only making `bins` visible, not adding or changing any plotting functionality.
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} | 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 matplotlib
import matplotlib.pyplot as plt
matplotlib.rcParams['savefig.dpi'] = 300
pd.set_option('display.mpl_style', 'default')
val = np.random.normal(size=1000000)
val2 = np.random.normal(scale = .5, size=1000000)
df = pd.DataFrame({'A' : val , 'B' : val2 , 'C' : val2})
df.hist(bins = 80, log=True,histtype='step')
plt.figure()
df.A.hist(bins = 80 , log=True,histtype='step',normed=True )
df.B.hist(bins = 80 , log=True,histtype='step',normed=True )
```
The secondary grid looks too thick and confusing; IMHO it damages readability. This problem shows up only with log scaled hist.
Consider the following as a comparison
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} | 9 | 2014-04-09T19:49:44Z | 2014-10-05T02:31:10Z | 2014-10-05T02:31:10Z | CONTRIBUTOR | null | maybe a function `nbytes()` to return memory usage
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.values.nbytes) for b in df._data.blocks ]))
Out[6]:
datetime64[ns] 8000
int64 8000
float64 8000
object 8000
dtype: int64
In [7]: df.index.nbytes
Out[7]: 8000
In [8]: df.columns.nbytes
Out[8]: 32
```
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} | 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.py:469: FutureWarning: scalar indexers should not be floating point unless a floating index
Out[1]: 1
In [5]: Series(1,np.arange(5)).iloc[3.0]
pandas/core/index.py:463: FutureWarning: scalar indexers should not be floating point unless a floating index
Out[5]: 1
# these are Float64Indexes, so integer or floating point is acceptable
In [3]: Series(1,np.arange(5.))[3]
Out[3]: 1
In [4]: Series(1,np.arange(5.))[3.0]
Out[4]: 1
```
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| null | 1 | 2014-04-10T07:54:07Z | 2014-04-24T08:04:14Z | 2014-04-24T08:04:14Z | NONE | null | Issue related to #6831
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_name)
writer.save()`
Traceback:
```
Exception AttributeError: "'Workbook' object has no attribute 'fileclosed'" in <bound method Workbook.__del__ of <xlsxwriter.workbook.Workbook object at 0x108657110>> ignored
TypeError Traceback (most recent call last)
<ipython-input-20-315e59384c18> in <module>()
----> 1 writer=pd.ExcelWriter('raw_data_dump.xlsx', engine='xlsxwriter', constant_memory=True)
2 for asset_name, frame in export_fuel_data.groupby('asset_name'):
3 frame.to_excel(writer, sheet_name=asset_name)
4 writer.save()
/usr/local/Cellar/python/2.7.4/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/io/excel.pyc in __init__(self, path, engine, date_format, datetime_format, **engine_kwargs)
724 **engine_kwargs)
725
--> 726 self.book = xlsxwriter.Workbook(path, **engine_kwargs)
727
728 def save(self):
TypeError: __init__() got an unexpected keyword argument 'constant_memory'
```
## INSTALLED VERSIONS
commit: None
python: 2.7.4.final.0
python-bits: 64
OS: Darwin
OS-release: 13.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: fr_BE.UTF-8
pandas: 0.13.1
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.12.0
statsmodels: 0.5.0
IPython: 2.0.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.2
bottleneck: None
tables: 3.1.0
numexpr: 2.3.1
matplotlib: 1.3.0
openpyxl: 1.6.2
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: 0.5.3
sqlalchemy: 0.8.2
lxml: 3.2.1
bs4: 4.1.3
html5lib: None
bq: None
apiclient: None
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} | 2 | 2014-04-10T08:28:05Z | 2014-04-10T12:08:34Z | 2014-04-10T12:08:34Z | MEMBER | null | http://pandas-docs.github.io/pandas-docs-travis/timeseries.html#time-deltas-conversions
```
In [253]: to_timedelta(np.arange(5),unit='d')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-253-bab05b276f33> in <module>()
----> 1 to_timedelta(np.arange(5),unit='d')
/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pandas/tseries/timedeltas.pyc in to_timedelta(arg, box, unit)
61 return Series(values, index=arg.index, name=arg.name, dtype='m8[ns]')
62 elif is_list_like(arg):
---> 63 return _convert_listlike(arg, box=box, unit=unit)
64
65 # ...so it must be a scalar value. Return scalar.
/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pandas/tseries/timedeltas.pyc in _convert_listlike(arg, box, unit)
42 elif is_integer_dtype(arg):
43 # these are shortcutable
---> 44 value = arg.astype('timedelta64[{0}]'.format(unit)).astype('timedelta64[ns]')
45 else:
46 try:
TypeError: Invalid datetime unit in metadata string "[d]"
```
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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/pandas-docs-travis/whatsnew.html#hdfstore, with the `unique` attribute that was removed recently in #6813
I don't think we should start updating the older whatsnew docs (they are a kind of historical overview), but we should do something as those doc build warnings are annoying. A proposal:
- after a release (or after some time), we run whatsnew/release once with the pandas version of that release, and replace all `ipython` directory blocks to literal `code-blocks` with output.
- we maybe add a warning at the top of each whatsnew that the code examples can be outdated
An alternative is to add `:okexcept:` to those ipython code-blocks; this would solve the warnings during the doc build, but not the 'faulty' output in the docs itself.
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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 store by just passing in the new data? If so, is there an example somewhere?
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} | 0 | 2014-04-10T11:49:28Z | 2014-06-29T14:30:56Z | 2014-04-10T12:08:34Z | CONTRIBUTOR | null | closes #6855
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} | 0 | 2014-04-10T12:34:45Z | 2014-04-12T00:27:51Z | 2014-04-12T00:27:51Z | CONTRIBUTOR | null | see #6848
Series.sort defaults to `inplace=True`
Series.order default to `inplace=False`
as they are inverses of each other
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} | 6 | 2014-04-10T13:22:02Z | 2014-09-12T20:34:29Z | 2014-08-05T15:43:04Z | CONTRIBUTOR | null | closes #1134
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 True
B True
C True
dtype: bool
```
Here we have a missing value, so it's `nan` in the comparisons
```
In [6]: s3 = Series(index=["C", "B"], data=[3,2])
In [7]: s3
Out[7]:
C 3
B 2
dtype: int64
In [8]: s1 == s3
Out[8]:
A False
B True
C True
dtype: bool
In [9]: s1>s3
Out[9]:
A False
B False
C False
dtype: bool
In [10]: s1<s3
Out[10]:
A False
B False
C False
dtype: bool
```
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} | 1 | 2014-04-10T13:54:47Z | 2014-06-22T23:19:02Z | 2014-04-12T00:27:51Z | CONTRIBUTOR | null | closes #6859
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| null | 1 | 2014-04-10T15:06:03Z | 2014-04-10T15:10:30Z | 2014-04-10T15:10:30Z | NONE | null | Here is my test code:
```
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 "/usr/local/lib/python2.7/dist-packages/pandas/core/indexing.py", line 1028, in __getitem__
return self._getitem_axis(key, axis=0)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/indexing.py", line 1218, in _getitem_axis
return self._get_slice_axis(key, axis=axis)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/indexing.py", line 1210, in _get_slice_axis
typ='iloc')
File "/usr/local/lib/python2.7/dist-packages/pandas/core/indexing.py", line 77, in _slice
typ=typ)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/frame.py", line 1859, in _slice
slobj, axis=axis, raise_on_error=raise_on_error)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/internals.py", line 2630, in get_slice
_check_slice_bounds(slobj, new_axes[axis])
File "/usr/local/lib/python2.7/dist-packages/pandas/core/indexing.py", line 1490, in _check_slice_bounds
raise IndexError("out-of-bounds on slice (start)")
IndexError: out-of-bounds on slice (start)
```
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} | 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 `None`:
```
if len(self.index) > (max_rows or 1000):
result = self._tidy_repr(min(30, max_rows - 4))
```
Since the doc string for `get_options` says
```
display.max_rows: [default: 60] [currently: 60]
...
'None' value means unlimited.
```
I think `_tidy_repr` should not be called when `max_rows` is None.
This PR seems simple enough but my main concern is that this PR stomps on GH1467 which explicitly changed `> max_rows` to `> (max_rows or 1000)` and I don't know what the purpose of this was.
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| null | 6 | 2014-04-10T18:33:42Z | 2014-04-11T11:56:48Z | 2014-04-11T11:56:48Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/22995828/json-extension-file-timestamp-pandas-python
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", "2012-03-01T00:06:52+00:00", "2012-03-01T00:11:23+00:00", "2012-03-01T00:12:47+00:00", "2012-03-01T00:12:54+00:00", "2012-03-01T00:16:14+00:00", "2012-03-01T00:17:31+00:00", "2012-03-01T00:21:23+00:00", "2012-03-01T00:21:26+00:00", "2012-03-01T00:22:25+00:00", "2012-03-01T00:28:24+00:00", "2012-03-01T00:31:21+00:00", "2012-03-01T00:32:20+00:00", "2012-03-01T00:33:32+00:00", "2012-03-01T00:35:21+00:00", "2012-03-01T00:38:14+00:00", "2012-03-01T00:39:24+00:00", "2012-03-01T00:43:12+00:00", "2012-03-01T00:46:13+00:00", "2012-03-01T00:46:31+00:00", "2012-03-01T00:48:03+00:00", "2012-03-01T00:49:34+00:00", "2012-03-01T00:49:54+00:00", "2012-03-01T00:55:19+00:00", "2012-03-01T00:56:27+00:00", "2012-03-01T00:56:32+00:00"]'
In [44]: pd.read_json(js).head(2) # strings !
Out[44]:
0
0 2012-03-01T00:05:55+00:00
1 2012-03-01T00:06:23+00:00
In [45]: pd.read_json(js, convert_dates=[0]).head(2)
Out[45]:
0
0 2012-03-01 00:05:55
1 2012-03-01 00:06:23
In [46]: pd.read_json(js, convert_dates=[0]).iloc[0, 0]
Out[46]: Timestamp('2012-03-01 00:05:55', tz=None)
```
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| 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
sphinx: 1.2.1
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2013b
bottleneck: None
tables: 3.1.0
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: 1.8.2
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: 0.5.2
sqlalchemy: 0.9.2
lxml: 3.3.1
bs4: 4.3.1
html5lib: None
bq: None
apiclient: None
I have a list of tuples which contain date ranges, like so:
```
[('2012-01-01', '2012-01-25'),
('2012-01-26', '2012-02-20'),
('2012-02-21', '2012-03-16'),
('2012-03-17', '2012-04-11')...]
def pull_unsampled(date_list):
df123 = []
for start, end in date_list:
df123.append(ga.read_ga(metrics,
dimensions = dimensions,
start_date = start,
end_date = end,
account_id = account_id, profile_id = profile_id))
print start, end
return df123
print pull_unsampled(unsampled_date_ranges)
```
The above function appends each GA pull for each date range to a list. For some reason the yearMonth column is formatted improperly until 2013 date ranges are reached. For example, the first date range of ('2012-01-01', '2012-01-25') returns this:
```
[ visits transactionsPerVisit \
deviceCategory operatingSystem yearMonth
desktop (not set) 2020-12-01 137 0.000000
Chrome OS 2020-12-01 20 0.000000
Google TV 2020-12-01 5 0.000000
Linux 2020-12-01 363 0.275482
Macintosh 2020-12-01 111467 0.909686
Nintendo Wii 2020-12-01 2 0.000000
Playstation 3 2020-12-01 1 0.000000
SunOS 2020-12-01 3 0.000000
Windows 2020-12-01 98147 0.996464
mobile Android 2020-12-01 2976 0.302419
BlackBerry 2020-12-01 1051 0.000000
Nokia 2020-12-01 2 0.000000
Samsung 2020-12-01 1 0.000000
SymbianOS 2020-12-01 4 0.000000
Windows Phone 2020-12-01 46 0.000000
iPad 2020-12-01 2 0.000000
iPhone 2020-12-01 24754 0.379737
iPod 2020-12-01 331 0.000000
tablet Android 2020-12-01 189 0.529101
iPad 2020-12-01 24973 0.828895
```
And the last date range , ('2014-03-06', '2014-03-31'), returns this:
```
deviceCategory operatingSystem yearMonth
desktop (not set) 201403 1.127708 1.013683
Chrome OS 201403 159.150562 3.811236
Google TV 201403 0.000000 1.000000
Linux 201403 431.220165 8.705575
Macintosh 201403 219.625070 5.193008
Nintendo Wii 201403 0.000000 1.000000
Playstation 3 201403 65.000000 3.000000
Windows 201403 186.650353 4.077768
Xbox 201403 0.000000 1.000000
mobile (not set) 201403 51.151515 2.212121
Android 201403 143.776396 3.195142
BlackBerry 201403 132.644037 3.049541
Nokia 201403 0.400000 1.000000
Series40 201403 5.071429 1.214286
SymbianOS 201403 0.000000 1.000000
Windows Phone 201403 172.685446 3.305164
iOS 201403 126.005593 2.954413
tablet Android 201403 183.609062 3.746048
BlackBerry 201403 224.421053 3.789474
iOS 201403 217.915084 4.462462
```
I did some testing and it does not seem to be an issue with the GA response, and likely and issue with the ga.io request.
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} | 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.
```
======================================================================
ERROR: test_pickle (pandas.tseries.tests.test_timeseries.TestTimeSeries)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/benjamin/workspace/pandas/pandas/tseries/tests/test_timeseries.py", line 1827, in test_pickle
p = pick.loads(pick.dumps(NaT))
File "/usr/lib/python2.7/pickle.py", line 1382, in loads
return Unpickler(file).load()
File "/usr/lib/python2.7/pickle.py", line 858, in load
dispatch[key](self)
File "/usr/lib/python2.7/pickle.py", line 1133, in load_reduce
value = func(*args)
TypeError: __new__() takes exactly one argument (2 given)
```
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} | 6 | 2014-04-11T00:09:10Z | 2014-04-23T23:24:50Z | 2014-04-23T23:24:50Z | CONTRIBUTOR | null | Floats below 1e-10 seem to all be receiving the same rank, incorrectly:
```
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 1.000000e-20
3 1.000000e-15
4 1.000000e-10
5 1.000000e-05
6 1.000000e-04
7 1.000000e-03
8 1.000000e-02
9 1.000000e-01
dtype: float64
In [6]: series.rank()
Out[6]:
0 2.5
1 2.5
2 2.5
3 2.5
4 5.0
5 6.0
6 7.0
7 8.0
8 9.0
9 10.0
dtype: float64
In [7]: from scipy import stats
In [8]: stats.rankdata(series)
Out[8]: array([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])
In [13]: pandas.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.3.final.0
python-bits: 64
OS: Darwin
OS-release: 10.8.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.13.1
Cython: 0.19.1
numpy: 1.8.0
scipy: 0.12.0.dev-1d5c886
statsmodels: 0.5.0
IPython: 1.2.1
sphinx: 1.2.2
patsy: 0.2.0
scikits.timeseries: None
dateutil: 2.2
pytz: 2013b
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.2.0
openpyxl: 1.5.7
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xlwt: None
xlsxwriter: None
sqlalchemy: 0.6.6
lxml: None
bs4: None
html5lib: None
bq: None
apiclient: None
```
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| null | 6 | 2014-04-11T00:21:40Z | 2014-04-11T00:43:23Z | 2014-04-11T00:43:23Z | NONE | null | I have pandas 0.13.1.
```
>>> 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
a b e
NaN c d NaN
row1 0 0 0 0
[1 rows x 4 columns]
```
This produces weird results:
```
>>> x['a']
d
row1 0
[1 rows x 1 columns]
>>> x[('a',)]
d
row1 0
[1 rows x 1 columns]
```
This works as expected:
```
>>> x['b']
c d
row1 0 0
[1 rows x 2 columns]
```
This will make everything work:
```
>>> test = MultiIndex.from_tuples([('a',''), ('b', 'c'), ('b', 'd'), ('e','')])
>>> test
MultiIndex(levels=[[u'a', u'b', u'e'], [u'', u'c', u'd']],
labels=[[0, 1, 1, 2], [0, 1, 2, 0]])
>>> x = DataFrame(0, index=['row1'], columns=test)
>>> x
a b e
c d
row1 0 0 0 0
[1 rows x 4 columns]
>>> x['a']
row1 0
Name: a, dtype: int64
>>> x[('a', '')]
row1 0
Name: (a, ), dtype: int64
```
This, however, is surprising:
```
>>> x[('a',)]
row1 0
Name: (a,), dtype: int64
```
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} | 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/statsmodels/statsmodels/commit/36bd0c9f8f2431d461d779c09f97f31d49033c97
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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')
# import pickle
# with open('series.pickle', 'rb') as f: s1 = pickle.load(f)
s2 = pandas.read_pickle('series.pickle')
```
crashes at the last line with long stack trace. If replace "pytz.utc" by "datetime.timezone.utc", error disappears. Commented code fragment works properly in both cases.
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.0.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: 0.13.1
Cython: None
numpy: 1.8.1
scipy: 0.13.3
statsmodels: 0.5.0
IPython: None
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.2
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
sqlalchemy: 0.9.4
lxml: 3.3.4
bs4: 4.3.2
html5lib: 0.999
bq: None
apiclient: None
Traceback (most recent call last):
File "C:\Program Files\Python34\lib\site-packages\pandas\io\pickle.py", line 43, in try_read
return pc.load(fh, encoding=encoding, compat=False)
File "C:\Program Files\Python34\lib\site-packages\pandas\compat\pickle_compat.py", line 89, in load
return up.load()
File "C:\Program Files\Python34\lib\pickle.py", line 1036, in load
dispatch[key[0]](self)
File "C:\Program Files\Python34\lib\site-packages\pandas\compat\pickle_compat.py", line 18, in load_reduce
if type(args[0]) is type:
IndexError: tuple index out of range
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Program Files\Python34\lib\site-packages\pandas\io\pickle.py", line 49, in read_pickle
return try_read(path)
File "C:\Program Files\Python34\lib\site-packages\pandas\io\pickle.py", line 46, in try_read
return pc.load(fh, encoding=encoding, compat=True)
File "C:\Program Files\Python34\lib\site-packages\pandas\compat\pickle_compat.py", line 89, in load
return up.load()
File "C:\Program Files\Python34\lib\pickle.py", line 1036, in load
dispatch[key[0]](self)
File "C:\Program Files\Python34\lib\pickle.py", line 1316, in load_newobj_ex
obj = cls.__new__(cls, *args, **kwargs)
TypeError: Required argument 'shape' (pos 1) not found
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Program Files\Python34\lib\site-packages\pandas\io\pickle.py", line 43, in try_read
return pc.load(fh, encoding=encoding, compat=False)
File "C:\Program Files\Python34\lib\site-packages\pandas\compat\pickle_compat.py", line 89, in load
return up.load()
File "C:\Program Files\Python34\lib\pickle.py", line 1036, in load
dispatch[key[0]](self)
File "C:\Program Files\Python34\lib\site-packages\pandas\compat\pickle_compat.py", line 18, in load_reduce
if type(args[0]) is type:
IndexError: tuple index out of range
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "H:/My Documents/PyCharm/LFDM/CalcFlowRate/tpicle.py", line 17, in <module>
s2 = pandas.read_pickle('series.pickle')
File "C:\Program Files\Python34\lib\site-packages\pandas\io\pickle.py", line 52, in read_pickle
return try_read(path, encoding='latin1')
File "C:\Program Files\Python34\lib\site-packages\pandas\io\pickle.py", line 46, in try_read
return pc.load(fh, encoding=encoding, compat=True)
File "C:\Program Files\Python34\lib\site-packages\pandas\compat\pickle_compat.py", line 89, in load
return up.load()
File "C:\Program Files\Python34\lib\pickle.py", line 1036, in load
dispatch[key[0]](self)
File "C:\Program Files\Python34\lib\pickle.py", line 1316, in load_newobj_ex
obj = cls.__new__(cls, *args, **kwargs)
TypeError: Required argument 'shape' (pos 1) not found
Process finished with exit code 1
```
---
Best regards,
Andrew
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} | 1 | 2014-04-11T19:08:27Z | 2014-06-27T15:47:52Z | 2014-04-11T22:33:18Z | CONTRIBUTOR | null | closes #5267
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} | 5 | 2014-04-11T23:28:59Z | 2014-06-24T10:25:33Z | 2014-04-13T07:14:55Z | MEMBER | null | NaT affected by some datetime related ops unexpectedly.
# 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
>>> pd.NaT - pd.offsets.Hour(1))
OverflowError: Python int too large to convert to C long
>>> pd.NaT - pd.Timestamp('2011-01-01')
-734779 days, 0:00:00
# numpy
>>> np.datetime64('nat') + np.timedelta64(1, 'h')
NaT
>>> np.datetime64('nat') - np.timedelta64(1, 'h')
NaT
>>> np.datetime64('nat') - np.datetime64('2011-01-01')
NaT
```
# Timezone
Closes #5546.
```
# current results
>>> idx = pd.DatetimeIndex(['2011-01-01 00:00', '2011-01-02 00:00', pd.NaT])
>>> idx.tz_localize('Asia/Tokyo')
<class 'pandas.tseries.index.DatetimeIndex'>
[2011-01-01 00:00:00+09:00, ..., 2262-04-10 00:12:43.145224192+09:00]
Length: 3, Freq: None, Timezone: Asia/Tokyo
>>> idx = pd.DatetimeIndex(['2011-01-01 00:00', '2011-01-02 00:00', pd.NaT], tz='US/Eastern')
>>> idx.tz_convert('Asia/Tokyo')
<class 'pandas.tseries.index.DatetimeIndex'>
[2011-01-01 14:00:00+09:00, ..., 2262-04-11 14:12:43.145224192+09:00]
```
_Note_ I fixed `DatetimeIndex`, and I leave `NatType` still doesn't have `tz_localize` and `tz_convert` methods `Timestamp` has. Is it should be added?
# Offsets
These have `apply` method which accepts `Timestamp`, but it cannot handle `Nat`.
```
# current result
>>> pd.offsets.Hour(1).apply(pd.NaT)
2262-04-11 01:12:43.145224192
```
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} | 11 | 2014-04-12T00:20:27Z | 2016-02-12T11:28:44Z | 2015-03-08T16:47:48Z | NONE | null | I am running into an issue trying to run OLS using pandas 0.13.1.
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 due to the silly regression specification, as I have noticed the pandas.ols estimates are inconsistent for other specifications as well.
Has anyone else encountered this problem?
```
import pandas as pd
import statsmodels.formula.api
In [1]: pd.ols(y=test.exret,x=test.exret).beta
Out[1]:
x 0.003107
intercept 0.006438
dtype: float64
In [2]: sm.ols(formula="exret ~ exret", data=test).fit().params
Out[2]:
Intercept -3.469447e-18
exret 1.000000e+00
dtype: float64
In [3]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.11.0-19-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.13.1
Cython: 0.20.1
numpy: 1.8.0
scipy: 0.13.3
statsmodels: 0.5.0
IPython: 2.0.0-dev
sphinx: 1.2.1
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2013b
bottleneck: None
tables: 3.1.0
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: 1.8.2
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: 0.5.2
sqlalchemy: 0.9.2
lxml: 3.3.1
bs4: 4.3.1
html5lib: None
bq: None
apiclient: None
```
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} | 8 | 2014-04-12T15:25:24Z | 2014-06-30T19:39:24Z | 2014-04-13T19:39:44Z | CONTRIBUTOR | null | I added a call to commit method for pushing data into the SQL database and not only in the memory.
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 create a temporary file used as database. The NamedTemporaryFile documentation states that it should be cross platforms but I don't have any Windows to test it. Normally doesn't break something. I never have done a pull request before, so let me know if something is bad. It's my first time and I'm happy to do it with pandas...
Closes #6846
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} | 2 | 2014-04-13T00:23:08Z | 2014-07-05T03:07:29Z | 2014-04-27T23:57:20Z | MEMBER | null | Modified these 2 points:
- 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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| null | 1 | 2014-04-13T13:06:01Z | 2014-04-13T13:19:03Z | 2014-04-13T13:19:03Z | NONE | null | I have a set of code that works with 0.13.0 and with 0.13.1. The code snippet is attached. I've highlighted the row in the code snippet. In one I have saved the date in the other just "1". The output is in the snippet of the csv attached. To point out the second output snippet shows that the "elif" saves the date correctly when the "if" statement saves "1".
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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]:
A B C
2013-01-01 0.313664 0.606445 0.048081
2013-01-02 0.907785 0.004429 0.374456
2013-01-03 0.916584 0.067639 0.467712
2013-01-04 0.712824 0.687895 0.397960
In [12]: df.reset_index()
Out[12]:
index A B C
0 2013-01-01 0.313664 0.606445 0.048081
1 2013-01-02 0.907785 0.004429 0.374456
2 2013-01-03 0.916584 0.067639 0.467712
3 2013-01-04 0.712824 0.687895 0.397960
In [12]: df.reset_index(name='datetime')
Out[12]:
datetime A B C
0 2013-01-01 0.313664 0.606445 0.048081
1 2013-01-02 0.907785 0.004429 0.374456
2 2013-01-03 0.916584 0.067639 0.467712
3 2013-01-04 0.712824 0.687895 0.397960
```
For multi-index, you should provide a list.
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} | 23 | 2014-04-13T23:12:00Z | 2014-06-14T15:46:38Z | 2014-04-14T10:55:43Z | MEMBER | null | closes #6471
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| null | 3 | 2014-04-14T16:50:01Z | 2014-04-15T20:33:36Z | 2014-04-15T20:33:36Z | CONTRIBUTOR | null | Using PANDAS 0.13.1.
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 replace `cols` with `headers` even in the `else` on line 1091.
Happy to take a stab at fixing this, but didn't know if I'm missing some interaction between the variables that makes this not a bug. Thanks!
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| 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/18bd0d64bf1fcdc7e86e743332dab29e9a155909/pandas/io/sql.py#L808):
- no multi-index support: so depending on the `con` type (dbapi connection or sqlalchemy connection), writing a multi-index dataframe will work or generate an error.
- before, in 0.13.1 and before, there was actually no support for writing the index (it was just not written), so this is actually an **API change** for the legacy mode (because now writing the index is set to True by default), for `write_frame` (as `to_sql` did not yet exist)
We could also opt to remove this entirely from the legacy mode (leave it as it was). However this is also somewhat complicated, as it is not easy to detect when the `index` keyword is specified by the user in legacy mode (in order to warn that this is ignored), as it is set to True by default. But it seems to me that we should either support it fully (with multi-index as for sqlalchemy based), or not.
But maybe more in general: how do we see the 'legacy'? Just keep it for backwards compatibility? Or is it useful to have something that is not dependent on sqlalchemy? (so also enhance it? or only bug fixes?)
@hayd @mangecoeur @danielballan
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| 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.TimeSeries(data=200.0, index=pd.date_range('2000-01-01', periods=1000))
ts3 = pd.TimeSeries(data=300.0, index=pd.date_range('2000-01-01', periods=1000))
df = pd.DataFrame({'ts1': ts1, 'ts2': ts2, 'ts3': ts3})
dt = ts1.index[500]
```
Here is a table showing the results of IPython's %timeit function.
| test | 0.12 | 0.13.1 |
| --- | --- | --- |
| ts1[dt] | 3.78 | 8.5 |
| ts1.ix[dt] | 11.8 | 30.7 |
| ts1.loc[dt] | 12.7 | 37.7 |
| ts1[dt]=1 | 1.86 | 4.32 |
| ts1.ix[dt]=1 | 12.5 | 65.9 |
| ts1.loc[df]=1 | 36.2 | 65.7 |
| ts1[:dt] | 78.2 | 101 |
| ts1.ix [:dt] | 53.1 | 106 |
| ts1.loc [:dt] | 59.5 | 101 |
| df.ix[dt] | 45.3 | 77.9 |
| df.ix [:dt] | 63.3 | 85.9 |
I did not see up-to-date data on http://pandas.pydata.org/pandas-docs/vbench/vb_indexing.html - am I looking at the right benchmark data? Most charts end in June 2012.
Can someone confirm this slowdown?
I am using numpy 1.8.1 by the way - let me know if you need any other version numbers.
Thanks in advance!
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| null | 7 | 2014-04-14T20:25:59Z | 2014-07-16T09:01:52Z | 2014-04-15T22:32:19Z | MEMBER | null | Closes #6881.
@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[3]:
(...)
DataError: No numeric types to aggregate
```
But any operation that works on numerical data is also well defined on timedeltas. This PR relaxes the restriction on those methods designated "numeric only" to accept numeric dtypes _and_ `timedelta64`. I include a unit test with one simple case each for `DataFrameGroupBy` and `SeriesGroupBy`.
```
In [2]: df.groupby('A').mean()
Out[2]:
E
A
bar 00:00:03
foo 00:00:02
```
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} | 12 | 2014-04-15T09:12:43Z | 2014-04-30T12:40:32Z | 2014-04-30T12:40:32Z | CONTRIBUTOR | null | (Context: `pandas version 0.13.1 running on 2.7.6 |Anaconda 1.9.1 (64-bit)| (default, Nov 11 2013, 10:49:15) [MSC v.1500 64 bit (AMD64)]`)
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)
```
and get:
```
c:\Anaconda\lib\site-packages\pandas\core\reshape.pyc in get_dummies(data, prefix, prefix_sep, dummy_na)
971 if prefix is not None:
972 dummy_cols = ['%s%s%s' % (prefix, prefix_sep, str(v))
--> 973 for v in levels]
974 else:
975 dummy_cols = levels
UnicodeEncodeError: 'ascii' codec can't encode character u'\xe9' in position 19: ordinal not in range(128)
```
Issue would appear to be the call to `str(v)` - if `v` is a unicode string with non-ascii, this is liable to explode.
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} | 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 that can be done to improve the speed.
Here's some quick benchmarking code (couldn't figure out how to work with the vbench suite):
```
import timeit
setup2 = """import pandas
import scipy.stats
import numpy
numpy.random.seed(154)
s = pandas.Series(numpy.random.normal(size=10000))"""
print "pandas:", timeit.repeat(stmt='s.rank()', setup=setup2, repeat=3, number=10000)
print "scipy:", timeit.repeat(stmt='scipy.stats.rankdata(s)',
setup=setup2, repeat=3, number=10000)
```
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| null | 3 | 2014-04-15T19:58:11Z | 2014-04-25T13:34:41Z | 2014-04-25T13:34:41Z | NONE | null | Following behaviour:
``` 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=0, na_values=['n.v.'])
df.rename(columns={'Unnamed: 3': 'Load'}, inplace=True)
df.Load = df.Load.interpolate()
df.count
```
shows me, that everything is pretty ok:
``` python
<bound method DataFrame.count of <class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 35044 entries, 2013-01-01 00:00:00 to 2013-12-31 23:45:00
Data columns (total 2 columns):
bis 35044 non-null values
Load 35044 non-null values
dtypes: float64(1), object(1)>
```
But when I try to plot it with `df.Load.plot()` I get the error message:
``` python
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-431-abfff097d131> in <module>()
----> 1 df.Load.plot()
/Library/Python/2.7/site-packages/pandas/tools/plotting.pyc in plot_series(series, label, kind, use_index, rot, xticks, yticks, xlim, ylim, ax, style, grid, legend, logx, logy, secondary_y, **kwds)
1728 secondary_y=secondary_y, **kwds)
1729
-> 1730 plot_obj.generate()
1731 plot_obj.draw()
1732
/Library/Python/2.7/site-packages/pandas/tools/plotting.pyc in generate(self)
854 self._compute_plot_data()
855 self._setup_subplots()
--> 856 self._make_plot()
857 self._post_plot_logic()
858 self._adorn_subplots()
/Library/Python/2.7/site-packages/pandas/tools/plotting.pyc in _make_plot(self)
1242 lines = []
1243 labels = []
-> 1244 x = self._get_xticks(convert_period=True)
1245
1246 plotf = self._get_plot_function()
/Library/Python/2.7/site-packages/pandas/tools/plotting.pyc in _get_xticks(self, convert_period)
1032 x = index._mpl_repr()
1033 elif is_datetype:
-> 1034 self.data = self.data.reindex(index=index.order())
1035 x = self.data.index._mpl_repr()
1036 else:
/Library/Python/2.7/site-packages/pandas/core/series.pyc in reindex(self, index, method, level, fill_value, limit, copy, takeable)
2644
2645 # GH4246 (dispatch to a common method with frame to handle possibly duplicate index)
-> 2646 return self._reindex_with_indexers(new_index, indexer, copy=copy, fill_value=fill_value)
2647
2648 def _reindex_with_indexers(self, index, indexer, copy, fill_value):
/Library/Python/2.7/site-packages/pandas/core/series.pyc in _reindex_with_indexers(self, index, indexer, copy, fill_value)
2648 def _reindex_with_indexers(self, index, indexer, copy, fill_value):
2649 new_values = com.take_1d(self.values, indexer, fill_value=fill_value)
-> 2650 return Series(new_values, index=index, name=self.name)
2651
2652 def reindex_axis(self, labels, axis=0, **kwargs):
/Library/Python/2.7/site-packages/pandas/core/series.pyc in __new__(cls, data, index, dtype, name, copy)
490 else:
491 subarr = subarr.view(Series)
--> 492 subarr.index = index
493 subarr.name = name
494
/Library/Python/2.7/site-packages/pandas/lib.so in pandas.lib.SeriesIndex.__set__ (pandas/lib.c:29541)()
AssertionError: Index length did not match values
```
But index as well as `Load` got 35044. Bug?
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} | 32 | 2014-04-16T01:16:37Z | 2014-06-16T17:30:28Z | 2014-04-23T22:23:11Z | CONTRIBUTOR | null | closes #6607
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)
For example, the commonly used option `sep='\s+'` causes a fallback to python which could be avoided by automatically translating this to the equivalent `delim_whitespace=True`, which is supported by the C engine.
There are some issues with the C parser that need to be fixed in order not to break tests with `sep='\s+'` which previously fell back to python:
The C parser does not correctly handle leading whitespace with `delim_whitespace=True` (#3374).
There is a related bug when parsing files with \r-delimited lines and missing values:
``` python
In [5]: data = 'a b c\r2 3\r4 5 6'
In [6]: pd.read_table(StringIO(data), delim_whitespace=True)
Out[6]:
a b c
0 2 3 4
1 NaN 5 6
[2 rows x 3 columns]
```
# Summary of changes
- **Raise `ValueError` when user specifies `engine='c'` with C-unsupported options:**
``` python
In [1]: import pandas as pd
In [2]: from pandas.compat import StringIO
In [3]: data = ' a\tb c\n 1\t2 3\n 4\t5 6'
In [4]: pd.read_table(StringIO(data), engine='c', sep='\s')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
. . .
ValueError: the 'c' engine does not support regex separators
```
- **Raise `ValueError` when fallback to python parser causes python-unsupported options to be ignored:**
``` python
In [5]: pd.read_table(StringIO(data), sep='\s', dtype={'a': float})
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
. . .
ValueError: Falling back to the 'python' engine because the 'c' engine does not support regex separators, but this causes 'dtype' to be ignored as it is not supported by the 'python' engine. (Note the 'converters' option provides similar functionality.)
```
- **Warn (new class ParserWarning) when C-unsupported options cause a fallback to python:**
``` python
In [6]: pd.read_table(StringIO(data), skip_footer=1)
pandas/io/parsers.py:615: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support skip_footer; you can avoid this warning by specifying engine='python'.
ParserWarning)
Out[6]:
a b c
0 1 2 3
[1 rows x 2 columns]
```
- **Raise ValueError when the user specifies both `sep` and `delim_whitespace=True`:**
``` python
In [7]: pd.read_table(StringIO(data), sep='\s', delim_whitespace=True)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
. . .
ValueError: Specified a delimiter with both sep and delim_whitespace=True; you can only specify one.
```
- **Translate `sep='\s+'` to `delim_whitespace=True` when there are no other C-unsupported options:**
``` python
In [8]: pd.read_table(StringIO(data), sep='\s+', engine='c')
Out[8]:
a b c
0 1 2 3
1 4 5 6
[2 rows x 3 columns]
```
- **Fix handling of leading whitespace in the C parser (#3374) (add test)**
``` python
# Old behavior
In [3]: pd.__version__
Out[3]: '0.13.1-663-g21565a3'
In [4]: pd.read_table(StringIO(data), delim_whitespace=True)
Out[4]:
Unnamed: 0 a b c
0 1 2 3 NaN
1 4 5 6 NaN
[2 rows x 4 columns]
```
``` python
# New behavior
In [9]: pd.read_table(StringIO(data), delim_whitespace=True)
Out[9]:
a b c
0 1 2 3
1 4 5 6
[2 rows x 3 columns]
```
- **Fix bug in handling of \r-delimited files (add test)**
(Old behavior shown above)
``` python
# New behavior
In [10]: data = 'a b c\r2 3\r4 5 6'
In [11]: pd.read_table(StringIO(data), delim_whitespace=True)
Out[11]:
a b c
0 2 3 NaN
1 4 5 6
[2 rows x 3 columns]
```
- **Copy tests in `ParserTests` that fall back to python to `TestPythonParser`; leave copies of these tests in `ParserTests` with the assertion that they raise a `ValueError` when run under other engines**
- **Add description of `engine` option to docstrings of `read_table` and `read_csv`**
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} | 36 | 2014-04-16T07:41:20Z | 2014-06-25T08:40:22Z | 2014-05-14T22:25:15Z | CONTRIBUTOR | null | This adds a info_verbose to the options. There's a small section in faq and basic introduction. Entry in v0.14 is still missing.
Closes #6568
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| null | 6 | 2014-04-16T09:56:20Z | 2014-05-05T13:24:39Z | 2014-05-05T13:24:35Z | NONE | null | Counting the cells that were updated when an update is performed to a dataframe.
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-counting-the-number-of-cells-updated
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| null | 3 | 2014-04-16T20:16:27Z | 2014-04-17T07:30:47Z | 2014-04-17T07:30:47Z | NONE | null | hi there
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 ?
Thanks for your help
Pierre
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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"""
In [5]: pd.read_table(StringIO(text), delim_whitespace=True)
---------------------------------------------------------------------------
CParserError Traceback (most recent call last)
. . .
CParserError: Error tokenizing data. C error: Expected 6 fields in line 3, saw 9
```
This (partially) works if `delim_whitespace=True` is replaced with `sep='\s+', engine='python'` (although columns A-D are lost):
``` python
In [6]: pd.read_table(StringIO(text), sep='\s+', engine='python')
Out[6]:
E
one two three four
a b 10.0032 5 0.3640
q 20.0000 4 0.1744
x q 30.0000 3 2.5838
[3 rows x 1 columns]
```
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} | 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 Traceback (most recent call last)
<ipython-input-69-5300763cc7b7> in <module>()
----> 1 portfolio_data_all.xs(cusip_etf, level=field_stock_id, copy=False)
C:\Python27\lib\site-packages\pandas\core\generic.pyc in xs(self, key, axis, level, copy, drop_level)
1261
1262 if not copy and not isinstance(loc, slice):
-> 1263 raise ValueError('Cannot retrieve view (copy=False)')
1264
1265 # level = 0
ValueError: Cannot retrieve view (copy=False)
```
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} | 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]:
MultiIndex(levels=[[u'a', u'b'], [u'c', u'd']],
labels=[[0, 0, 1, 1], [0, 1, 0, 1]])
# Old behavior, casted MultiIndex to an Index
In [23]: df_multi.set_index(df_multi.index)
Out[23]:
0 1
a c 0.903672 -2.201395
d 0.692797 0.016163
b c -0.315266 0.364278
d -0.497458 0.531440
[4 rows x 2 columns]
# New behavior
In [24]: df_multi.set_index(df_multi.index)
Out[24]:
0 1
a c 0.903672 -2.201395
d 0.692797 0.016163
b c -0.315266 0.364278
d -0.497458 0.531440
[4 rows x 2 columns]
```
The 'old' and 'new' behaviour seems exactly the same, so it not clear what has changed.
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| null | 4 | 2014-04-17T00:23:53Z | 2014-04-17T01:33:19Z | 2014-04-17T01:33:19Z | CONTRIBUTOR | null | Version: '0.13.1-648-gad1f47d'
``` 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 10:00:00.458000,2455550.916671965,6867.0,0.98631\n2010-12-20 10:00:00.586000,2455550.916673446,6867.0,0.98631\n'
```
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} | 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 groupby. However, it does not have an optimized standard error method, meaning users who want to compute error ranges have to rely on the unoptimized scipy method.
Since computing error ranges is such a common operation, I think it would be very useful if there was an optimized `sem` method like there is for `std`.
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} | 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 users to create their own columns or a separate pandas object to hold error ranges (see, e.g. #5638) or manually creating and using `uncertainties` objects.
I think it would be very helpful if there was an aggregate method that would aggregate data to data with an error range (such as an `uncertanties` array). By default, it could use `mean` to get the center values and and `sem` (standard error of the mean) or `std` to get the error ranges, but it would probably be good for users to be able to specify their own functions for calculating the center values and/or the error ranges.
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} | 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 see that v0.13 always uses native python pickle to load, even when compat=False. In 0.12 it uses cPickle to load. Seems like something is wrong with the logic in pandas/compat/pickle_compat.py
A workaround (if you know you don't need compatibility mode) is to use cPickle.load(open('foo.pickle')) instead of pandas.read_pickle('foo.pickle').
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} | 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? Or is it really just 'legacy' and we don't develop it further (only bug fixes)?
I think it is important to discuss this (and 'formally' decide on this), as there are some issues with this:
- If it is mainly for backwards compatibility, are we also planning to deprecate it? (which is not necessarily the consequence, if we just regard it as what the name says: legacy)
- If it is not only for backwards compatibility, why do we raise a warning?:
```
UserWarning: Not an SQLAlchemy engine,
attempting to use as legacy DBAPI connection
```
As this implies to me that it is not really preferred to use this.
- Do we want to keep the functionality equal to the sqlalchemy version? (as we are using the same functions for both, it would make it complex if it is not)
- Eg, we could easily provide a `read_table` functionality to legacy mode, by just doing a `read_sql("SELECT * FROM specified_table", ..)`.
- We should clarify the docs on this status: http://pandas-docs.github.io/pandas-docs-travis/io.html#legacy (as it is not really clear now)
- If we decide we want to fully support this, we should maybe try to find another name than 'legacy' .. :-)
Maybe the bottom line we should ask ourselves is: do we want that we can really _recommend_ it to users to use the legacy sqlite without sqlalchemy version? (and in that case I think it should be fully supported and not regarded as 'legacy')
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