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https://api.github.com/repos/pandas-dev/pandas/issues/4401
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17,349,760
MDU6SXNzdWUxNzM0OTc2MA==
4,401
MultiIndex get_level_values method to expensive in time
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5
2013-07-29T16:41:35Z
2014-05-29T18:30:27Z
2014-05-29T18:30:27Z
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In [287]: %timeit zip(*df.index)[0] 100000 loops, best of 3: 6.48 µs per loop In [288]: %timeit df.index.get_level_values(0) 10000 loops, best of 3: 39.4 µs per loop
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17,355,290
MDU6SXNzdWUxNzM1NTI5MA==
4,402
better error report when both style and color are passed to Series.plot
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3
2013-07-29T18:29:47Z
2013-07-31T15:33:34Z
2013-07-31T15:33:34Z
NONE
null
The following command returns an error message for a Series ``` python s.plot(style='k--', color='k') ``` It says that the color '[k]' is not recognized. I am using pandas 0.11 so it may have been fixed in 0.12.
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MDU6SXNzdWUxNzM3NTI2OA==
4,403
Dataframe rename issue.
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31
2013-07-30T03:03:33Z
2013-08-16T02:10:14Z
2013-07-31T01:06:11Z
NONE
null
I just upgrage from 0.11 to 0.12 version. And meet dataframe rename error caused by upgrading. (It worked well in 0.11) . ``` >>> df4 TClose RT TExg STK_ID RPT_Date 600809 20130331 22.02 0.0454 0.0422 >>> df5 STK_ID RPT_Date STK_Name TClose STK_ID RPT_Date 600809 20120930 600809 20120930 山西汾酒 38.05 20121231 600809 20121231 山西汾酒 41.66 20130331 600809 20130331 山西汾酒 30.01 >>> k=pd.merge(df4, df5, how='inner', left_index=True, right_index=True) >>> k TClose_x RT TExg STK_ID RPT_Date STK_Name TClose_y STK_ID RPT_Date 600809 20130331 22.02 0.0454 0.0422 600809 20130331 山西汾酒 30.01 >>> k.rename(columns={'TClose_x':'TClose', 'TClose_y':'QT_Close'}) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "d:\Python27\lib\site-packages\pandas\core\base.py", line 40, in __repr__ return str(self) File "d:\Python27\lib\site-packages\pandas\core\base.py", line 20, in __str__ return self.__bytes__() File "d:\Python27\lib\site-packages\pandas\core\base.py", line 32, in __bytes__ return self.__unicode__().encode(encoding, 'replace') File "d:\Python27\lib\site-packages\pandas\core\frame.py", line 668, in __unicode__ self.to_string(buf=buf) File "d:\Python27\lib\site-packages\pandas\core\frame.py", line 1556, in to_string formatter.to_string() File "d:\Python27\lib\site-packages\pandas\core\format.py", line 294, in to_string strcols = self._to_str_columns() File "d:\Python27\lib\site-packages\pandas\core\format.py", line 239, in _to_str_columns str_columns = self._get_formatted_column_labels() File "d:\Python27\lib\site-packages\pandas\core\format.py", line 435, in _get_formatted_column_labels dtypes = self.frame.dtypes File "d:\Python27\lib\site-packages\pandas\core\frame.py", line 1696, in dtypes return self.apply(lambda x: x.dtype) File "d:\Python27\lib\site-packages\pandas\core\frame.py", line 4416, in apply return self._apply_standard(f, axis) File "d:\Python27\lib\site-packages\pandas\core\frame.py", line 4491, in _apply_standard raise e TypeError: ("'NoneType' object is not iterable", u'occurred at index TExg') >>> df4.dtypes TClose float64 RT float64 TExg float64 dtype: object >>> df5.dtypes STK_ID object RPT_Date object STK_Name object TClose float64 dtype: object >>> ```
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17,397,108
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4,404
fix the excel reader: hours & header
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73
2013-07-30T13:57:58Z
2014-06-17T12:47:53Z
2013-08-21T23:15:09Z
CONTRIBUTOR
null
in the excel.py there is a fix enabling reading xlsx files with both datemodes: (see #4332) in the parsers.py there is the fix for readinh the header even if there are additional rows (to be skipped) between a header and the data (see: #4340) I hope this PR is adequate. If not, please let me know. I can supply the sample file for testing proving that it works. See also: https://github.com/timmie/example_code_data
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string truncation when using Series.astype(str)
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6
2013-07-30T16:49:30Z
2016-10-12T23:04:03Z
2013-08-03T03:29:56Z
NONE
null
When converting a pandas Series object to type string using `astype(str)`, long strings are truncated to 64 characters silently. Pandas version: 0.12 Numpy version: 1.7.1 ``` python In [1]: s = '0123456789' * 10 In [2]: tmp = np.array([s]).astype(str) tmp Out[2]: array([ '0123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789'], dtype='|S100') In [3]: tmp = pd.Series([s]).astype(str) tmp[0] Out[3]: '0123456789012345678901234567890123456789012345678901234567890123' In [4]: len(tmp[0]) Out[4]: 64 ```
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4,406
BUG: Fix an issue with the csv cparser when usecols is used
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6
2013-07-30T18:13:27Z
2014-06-24T15:22:39Z
2013-07-30T23:10:06Z
CONTRIBUTOR
null
closes #3192 This pull request fixes a problem arising from using the csv parser with the usecols option. In that case, all the dtypes will be converted to object. This code fixes that issue, resulting in the correct dtypes. Thanks, Tiago
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4,407
Add Support For Reading XBRL Files in Pandas
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3
2013-07-30T18:24:04Z
2016-11-11T10:25:39Z
2016-11-11T10:25:29Z
CONTRIBUTOR
null
XBRL (http://en.wikipedia.org/wiki/XBRL) is the standard format for financial filings. As of 2009, the SEC requires companies to file in XBRL format (http://xbrl.sec.gov/)
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version 0.12.0: problems interacting subplots and DataFrame().hist()
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5
2013-07-30T21:54:35Z
2016-10-12T23:04:03Z
2013-07-31T15:33:33Z
NONE
null
I run Python 2.7.5 on Windows 7 64-bit and IPython 0.13.2. I have a data frame with some portfolio weights and I do the following: ``` def weight_histograms(field, name, bins=100): if flag_inline: plt.figure(figsize=(16, 8)) else: plt.figure() plt.subplot(121) portfolio_data[field].hist(normed=True, bins=bins) plt.title('{}: All'.format(name)) plt.subplot(122) portfolio_data.ix[portfolio_data[field] != 0, field].hist(normed=True, bins=bins) plt.title('{}: Non-Zero'.format(name)) ``` With pandas 0.11.0, running ``` weight_histograms(field_benchmark_weight, 'Benchmark Weights') ``` in IPython with ``` %pylab inline ``` and ``` flag_inline=True ``` I get ![pandas0 11 0](https://f.cloud.github.com/assets/1903683/882420/59ad630a-f962-11e2-94e0-3ade62951d54.jpg) Doing the same thing with pandas 0.12.0 results in ![pandas0 12 0](https://f.cloud.github.com/assets/1903683/882423/68766666-f962-11e2-80be-9131f5f0d03d.jpg)
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API: HDFStore closing is buggy
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14
2013-07-30T23:08:27Z
2013-08-02T00:33:27Z
2013-08-02T00:33:27Z
CONTRIBUTOR
null
Testing and playing with HDFStores a lot, I have found several times that a store.close() leaves things hanging, like this: ``` python store.close() # no output store ClosedFileError: the file object is closed df.to_hdf('test.h5','df',data_columns=['A','B'],mode='w',table=True) ValueError: The file 'test.h5' is already opened. Please close it before reopening in write mode. # The store was opened with 'test.h5' as filename. ``` pandas version 0.12.0
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17,428,324
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4,410
BUG: Fix an issue in merging blocks where the resulting DataFrame had partially set _ref_locs
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0
2013-07-31T00:17:47Z
2014-06-19T20:25:43Z
2013-07-31T01:06:11Z
CONTRIBUTOR
null
closes #4403
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4,411
Panel data lost when calling .values
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5
2013-07-31T00:59:51Z
2013-09-20T22:58:55Z
2013-09-20T22:58:55Z
NONE
null
Using Pandas 0.11 (this does not happen on 0.09): ``` python In [114]: print panel <class 'pandas.core.panel.Panel'> Dimensions: 7 (items) x 6 (major_axis) x 14 (minor_axis) Items axis: ticker1 to ticker7 Major_axis axis: 2013-07-23 00:00:00 to 2013-07-30 00:00:00 Minor_axis axis: fut_trading_hrs to exp_date In [115]: panel['ticker1']['exp_date'] Out[115]: 2013-07-23 2013-09-17 00:00:00 2013-07-24 2013-09-17 00:00:00 2013-07-25 2013-09-17 00:00:00 2013-07-26 2013-09-17 00:00:00 2013-07-29 2013-09-17 00:00:00 2013-07-30 2013-09-17 00:00:00 Name: exp_date, dtype: datetime64[ns] In [116]: panel.minor_xs('exp_date') __# where did the data go!?__ Out[116]: ticker1 ticker2 ticker3 2013-07-23 NaN NaN NaN 2013-07-24 NaN NaN NaN 2013-07-25 NaN NaN NaN 2013-07-26 NaN NaN NaN 2013-07-29 NaN NaN NaN 2013-07-30 NaN NaN NaN ticker4 ticker5 ticker6 2013-07-23 NaN NaN NaN 2013-07-24 NaN NaN NaN 2013-07-25 NaN NaN NaN 2013-07-26 NaN NaN NaN 2013-07-29 NaN NaN NaN 2013-07-30 NaN NaN NaN ticker7 2013-07-23 NaN 2013-07-24 NaN 2013-07-25 NaN 2013-07-26 NaN 2013-07-29 NaN 2013-07-30 NaN In [117]: panel['ticker1']['exp_date'] __# even this is gone now!__ Out[117]: 2013-07-23 NaN 2013-07-24 NaN 2013-07-25 NaN 2013-07-26 NaN 2013-07-29 NaN 2013-07-30 NaN Name: exp_date, dtype: object ```
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4,412
DOC: Update docs to reflect actual version support.
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3
2013-07-31T01:24:12Z
2013-08-10T12:07:18Z
2013-08-10T12:07:18Z
CONTRIBUTOR
null
I don't think this is true anymore (from install.html on pandas site) > Officially Python 2.6 to 2.7 and Python 3.1+, although Python 3 support is less well tested. Python 2.4 support is being phased out since the userbase has shrunk significantly. Continuing Python 2.4 support will require either monetary development support or someone contributing to the project to maintain compatibility. I'm pretty sure that 2.4 (and 2.5) will fail, if only because the `from __future__ import with_statement` have been removed from many files. I also think we're at the point where we can say that Python 3.1+ support is actually there (this could be a change in docs for v0.13)
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DeprecationWarning: height has been deprecated
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1
2013-07-31T02:14:13Z
2013-08-02T09:37:02Z
2013-08-02T09:37:02Z
CONTRIBUTOR
null
I just upgraded to the latest stable version of Pandas: ``` import pandas as pd import numpy as np pd.DataFrame(np.random.random((10,5))) ``` returns the following warning: ``` /opt/python/virtualenvs/work/lib/python2.7/site-packages/pandas/core/config.py:570: DeprecationWarning: height has been deprecated. ``` Why? And how can I silence it?
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https://api.github.com/repos/pandas-dev/pandas/issues/4414
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17,433,401
MDExOlB1bGxSZXF1ZXN0NzI4MjM0Ng==
4,414
BUG/VIS: fix Series.hist so that users can create hist subplots without the mpl API
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5
2013-07-31T03:56:52Z
2014-06-25T07:26:53Z
2013-07-31T15:33:34Z
MEMBER
null
closes #4408. closes #4402.
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17,450,038
MDU6SXNzdWUxNzQ1MDAzOA==
4,415
pd.HDFStore issues PerformanceWarnings for string columns
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7
2013-07-31T13:03:36Z
2013-07-31T15:42:46Z
2013-07-31T15:42:46Z
NONE
null
I get the following performance warning > C:\Python27\lib\site-packages\pandas\io\pytables.py:1795: PerformanceWarning: > your performance may suffer as PyTables will pickle object types that it cannot map > directly to c-types [inferred_type->mixed,key->block2_values] > > warnings.warn(ws, PerformanceWarning) for the following DataFrame > MSCI.ACWI float64 > MarketCap float64 > alpha float64 > gics_code float64 > investable bool > issuer_country object > universe bool > weight_benchmark float64 > weight_portfolio float64 > weight_active float64 > dtype: object The culprit is issuer_country column, which is a string column. I find it surprising. Is there a way to get around this?
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MDU6SXNzdWUxNzQ1NTY4NA==
4,416
run all tests now that travis is faster for all versions
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18
2013-07-31T14:50:47Z
2013-08-15T01:18:00Z
2013-08-07T17:50:22Z
MEMBER
null
I think it could be time to start running all tests (slow + not slow) since travis has been sped up quite a bit by wheels and now py3 builds don't use `2to3`
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17,457,644
MDExOlB1bGxSZXF1ZXN0NzI5NTE5MA==
4,417
API: GH4409 HDFStore adds an is_open property / CLOSED message
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2
2013-07-31T15:22:50Z
2014-06-16T14:00:58Z
2013-08-02T00:33:27Z
CONTRIBUTOR
null
closes #4409 closes #4367
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17,457,688
MDU6SXNzdWUxNzQ1NzY4OA==
4,418
make doc no longer works
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0
2013-07-31T15:23:34Z
2013-08-01T11:41:40Z
2013-08-01T11:41:40Z
MEMBER
null
there's a relative import issue
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https://api.github.com/repos/pandas-dev/pandas/issues/4419
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17,458,560
MDExOlB1bGxSZXF1ZXN0NzI5NTY5NA==
4,419
FIX: doc imports
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37
2013-07-31T15:38:31Z
2014-07-16T08:21:17Z
2013-08-01T11:41:40Z
MEMBER
null
Bunch of doc build cleanups closes #4418.
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17,462,433
MDU6SXNzdWUxNzQ2MjQzMw==
4,420
question: filling missing values in one column with values from another column
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13
2013-07-31T16:44:57Z
2013-08-01T10:29:45Z
2013-08-01T10:28:27Z
NONE
null
Is there a more concise way of accomplishing the following? ``` portfolio_data[FIELD_NAME_COUNTRY] = portfolio_data[FIELD_NAME_ISSUER_COUNTRY] countries = portfolio_data[FIELD_NAME_COUNTRY] flag_no_country = countries.isnull() countries[flag_no_country] = portfolio_data.ix[flag_no_country, FIELD_NAME_MSCI_COUNTRY] portfolio_data[FIELD_NAME_COUNTRY] = countries ```
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17,475,686
MDU6SXNzdWUxNzQ3NTY4Ng==
4,421
ENH/API: DataFrame's isin should accept DataFrames
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14
2013-07-31T20:43:46Z
2014-05-01T13:52:50Z
2013-10-17T20:49:58Z
CONTRIBUTOR
null
From [this](http://stackoverflow.com/questions/17979327/drop-rows-with-multiple-keys-in-pandas/17979614?noredirect=1#comment26286280_17979614) SO question. The user had two dataframes and wanted to use the second as the values argument to `isin`. We'd need to think about how to handle the other index. In this case the user only cared about the columns, not the index labels. Previous issues/PRs: #4258 and #4211
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MDU6SXNzdWUxNzQ3NjcwNA==
4,422
BLD: Improve import order in `pandas/__init__.py`
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1
2013-07-31T21:03:32Z
2013-08-10T01:52:13Z
2013-08-10T01:52:13Z
CONTRIBUTOR
null
I'm going to look into this at some point and see if it can be made better. You end up with cryptic error messages on importing pandas if there are any kind of top level errors because (for some reason), issues with nearly _any_ `*.py` file cause the exception below. My theory is that it's because the C-extensions import from the pure python parts of pandas, but silently fail instead of giving the better error. ``` bash ====================================================================== ERROR: Failure: ImportError (cannot import name hashtable) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python27\lib\site-packages\nose\loader.py", line 413, in loadTestsFromName addr.filename, addr.module) File "C:\Python27\lib\site-packages\nose\importer.py", line 47, in importFromPath return self.importFromDir(dir_path, fqname) File "C:\Python27\lib\site-packages\nose\importer.py", line 94, in importFromDir mod = load_module(part_fqname, fh, filename, desc) File "D:\development\GitHub\pandas\pandas\__init__.py", line 6, in <module> from . import hashtable, tslib, lib ImportError: cannot import name hashtable ---------------------------------------------------------------------- Ran 1 test in 0.000s FAILED (errors=1) ``` I'm not sure whether this is really possible to improve, but I definitely hit this error when it turned out there was actually a circular import on `Series` or a syntax error or something else. Mostly this would require moving C-extension imports out of the top level of files, but if it means better and more debuggable errors, would be an improvement. It's also possible that I'm wrong on this, but we can see.
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4,423
Add join to the pandas namespace
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3
2013-08-01T00:25:40Z
2013-10-18T00:21:35Z
2013-09-28T17:03:06Z
CONTRIBUTOR
null
Currently `merge` is a function in the pandas namespace, and it is also available as a DataFrame instance method, with the calling DataFrame being implicitly considered the left object in the join. However, the related DataFrame.join method, which uses merge internally for the index-on-index and index-on-column(s) joins, but joins on indexes by default rather than trying to join on common columns is NOT in the pandas namespace. Adding join to the pandas name space will avoid having to write: ``` if len(values) == 0: joined = None elif len(values) == 1: joined = values[0] else: joined = values[0].join(values[1:]) ```
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4,424
pytables interface does not provide kwargs to create_index at append
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6
2013-08-01T01:46:48Z
2013-08-01T18:52:50Z
2013-08-01T18:52:50Z
CONTRIBUTOR
null
The following last lines from `_write_to_group()` from io/pytables.py show that kwargs are available but are not provided to create_index(). This prevents the user from creating a `kind='full'` index at `append` time: ``` python s.write(obj = value, append=append, complib=complib, **kwargs) if s.is_table and index: s.create_index(columns = index) ```
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providing kwargs to create_index at store.append()
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4
2013-08-01T01:52:06Z
2013-08-04T06:53:01Z
2013-08-01T18:52:39Z
CONTRIBUTOR
null
This should enable the creation of a 'full' index at append time.
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17,498,363
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4,426
json in toplevel namespace
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6
2013-08-01T09:35:42Z
2017-07-09T02:30:15Z
2017-07-09T02:30:02Z
CONTRIBUTOR
null
Has the json module always been here (I thought it was only np and datetime)? ``` pd.json ``` Should it be? confusingly I can't actually see where it's coming from...
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DR/NWK: 0.12.0: nosetests: one fail
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Python 2.7.5, Windows 7 64-bit > .........................F.........SSS.....SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS > SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS.SSS.................S............ > ................................................................................ > ................................................................................ > ................................................................................ > ................................................................................ > ................................................................................ > .......................SSSS......................S.............................. > ..........................SSSSSSSSSS...................SS....................... > ................................................................................ > ...........................................................................S.... > ...................SSSSSSS.........................................S............ > ..................................S............................................. > ................................................................................ > ................................................................................ > ................................................................................ > ................................................................................ > ................................................................................ > ......................can't invoke "event" command: application has been destro > yed > while executing > "event generate $w <<ThemeChanged>>" > (procedure "ttk::ThemeChanged" line 6) > invoked from within > "ttk::ThemeChanged" > ................................................................................ > ................................................................................ > ................................................................................ > ............S................................................................... > ................................................................................ > .S.............................................................................. > ................................................................................ > ................................................................................ > ................................................................................ > ................................................................................ > ................................................................................ > ................................................................................ > .............................................S.................................. > ................................................................................ > ................................................................................ > ................................................................................ > ................................................................................ > ................................................................................ > ..................................S............................................. > ............................................S................................... > ................................................................................ > ...........................................S..................S...S............S > SS.........S............SS............SS.....SSSS............................... > ................................................................................ > ................................................................................ > ................................................................................ > ................................................................................ > # .......................................... > ## FAIL: Throws an exception when DataReader can't get a 200 response from > > Traceback (most recent call last): > File "C:\Python27\lib\site-packages\pandas\util\testing.py", line 765, in netw > ork_wrapper > return t(_args, *_kwargs) > File "C:\Python27\lib\site-packages\pandas\io\tests\test_data.py", line 370, i > n test_fred > 15984.1) > AssertionError: DATE > 2013-01-01 16535.3 > Name: GDP, dtype: float64 != 15984.1 > --- > > Ran 3661 tests in 179.605s > > FAILED (SKIP=137, failures=1)
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17,499,455
MDU6SXNzdWUxNzQ5OTQ1NQ==
4,428
Add stack argument to str.findall
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4
2013-08-01T10:05:20Z
2020-05-03T19:53:58Z
2020-05-03T19:53:58Z
CONTRIBUTOR
null
Some of the string methods return a Series of lists (e.g. findall) which isn't that convenient for doing more analysis: ``` df = pd.DataFrame([['@a @b'], ['@a'], ['@c']], columns=['tweets']) at_mentions = df.tweets.str.findall('@[a-zA-Z0-9_]+') In [3]: at_mentions Out[3]: 0 [@a, @b] 1 [@a] 2 [@c] Name: tweets, dtype: object ``` I think it would be nice to be able to stack the results: ``` In [4]: at_mentions.apply(pd.Series).stack() Out[4]: tweets 1 0 @a 1 @b 2 0 @a 3 0 @c dtype: object ``` _see this [SO answer](http://stackoverflow.com/a/17989510/1240268)_ Could just do it directly like that, or perhaps make more efficient. Thoughts?
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0.12.0 doesn't work with statsmodels 0.5.0
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21
2013-08-01T10:14:54Z
2013-08-19T20:29:23Z
2013-08-02T15:26:32Z
NONE
null
``` ols = pd.ols(y=portfolio_weights, x=alphas, intercept=intercept) ``` > --- > > ImportError Traceback (most recent call last) > <ipython-input-53-2a147656afb3> in <module>() > 1 # Regress weights on alphas > ----> 2 ols = pd.ols(y=portfolio_weights, x=alphas, intercept=intercept) > 3 #ols = pd.ols(y=portfolio_weights, x=alphas, intercept=intercept, weights=weights) > 4 print(ols.summary) > > C:\Python27\lib\site-packages\pandas\stats\interface.pyc in ols(**kwargs) > 133 klass = MovingOLS > 134 > --> 135 return klass(**kwargs) > > C:\Python27\lib\site-packages\pandas\stats\ols.pyc in **init**(self, y, x, intercept, weights, nw_lags, nw_overlap) > 51 import statsmodels.api as sm > 52 except ImportError: > ---> 53 import scikits.statsmodels.api as sm > 54 > 55 self._x_orig = x > > ImportError: No module named scikits.statsmodels.api
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17,508,359
MDExOlB1bGxSZXF1ZXN0NzMyMjkxMw==
4,430
TST: better assertion messages on test failures (GH4397)
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9
2013-08-01T13:56:39Z
2014-07-16T08:21:21Z
2013-08-01T14:31:05Z
CONTRIBUTOR
null
closes #4397 a start at least
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4,431
date_range not defined error
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2013-08-01T16:14:05Z
2013-08-01T16:48:41Z
2013-08-01T16:48:41Z
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http://pandas.pydata.org/pandas-docs/stable/10min.html Setting ``` In [63]: s1 = pd.Series([1,2,3,4,5,6],index=date_range('20130102',periods=6)) --------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-63-bfe1d6561bb0> in <module>() ----> 1 s1 = pd.Series([1,2,3,4,5,6],index=date_range('20130102',periods=6)) NameError: name 'date_range' is not defined ``` Python - pandas newbie, any help kindly appreciated. I assume i can't move on in the 10 min tutorial if i can't get pass this error.
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4,432
BUG: Fixes issue #3334: brittle margin in pivot_table.
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2013-08-01T18:41:53Z
2014-06-23T03:12:36Z
2013-08-01T18:47:28Z
CONTRIBUTOR
null
closes #3334 Brittle pivot margins Issue was that pivot tables that use all columns of the original DataFrame in rows and cols failed on the margin computation. This is a special case for margins: one should use the index itself as the value. ``` >> df = DataFrame({'Response' : ['Y', 'N' ,'N', 'Y', 'Y', 'N'], 'Type' : ['A', 'A', 'B', 'B', 'B', 'C']}) >> pivot_table(df, rows='Response',cols='Type',aggfunc=len,margins=True) >> Type A B C All >> Response >> N 1 1 1 3 >> Y 1 2 NaN 3 >> All 2 3 1 6 ```
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MDExOlB1bGxSZXF1ZXN0NzMzMjI5OQ==
4,433
BUG: Fixes issue #3334: brittle margin computation in pivot_table
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2013-08-01T19:08:57Z
2013-08-01T19:14:11Z
2013-08-01T19:14:11Z
CONTRIBUTOR
null
Adds support for margin computation when all columns are used in rows and cols. Issue was that pivot tables that use all columns of the original DataFrame in rows and cols failed on the margin computation. This is a special case for margins: one should use the index itself as the value. ``` >> df = DataFrame({'Response' : ['Y', 'N' ,'N', 'Y', 'Y', 'N'], 'Type' : ['A', 'A', 'B', 'B', 'B', 'C']}) >> pivot_table(df, rows='Response',cols='Type',aggfunc=len,margins=True) >> Type A B C All >> Response >> N 1 1 1 3 >> Y 1 2 NaN 3 >> All 2 3 1 6 ``` This is my first PR to Pandas, so apologies for anything out of the ordinary.
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4,434
ENH: Richer options for `interpolate` and `resample`
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27
2013-08-01T20:21:41Z
2013-10-09T20:30:19Z
2013-10-09T20:30:19Z
CONTRIBUTOR
null
related #1892, #1479 Is there any interest in giving interpolate and resample (to higher frequency) some additional methods? For example: ``` python from scipy import interpolate df = pd.DataFrame({'A': np.arange(10), 'B': np.exp(np.arange(10) + np.random.randn())}) xnew = np.arange(10) + .5 In [46]: df.interpolate(xnew, method='spline') ``` Could return something like ``` python In [47]: pd.DataFrame(interpolate.spline(df.A, df.B, xnew, order=4), index=xnew) Out[47]: 0 0.5 1.044413 1.5 0.798392 2.5 3.341909 3.5 8.000314 4.5 22.822819 5.5 60.957659 6.5 166.844351 7.5 451.760621 8.5 1235.969910 9.5 0.000000 # falls outside the original range so interpolate.spline sets it to 0. ``` I have never used the DataFrame's interpolate, but a quick glance says that something like the above wouldn't be backwards compatible with the current calling convention. Maybe a different name? This may be confusing two issues: interpolating over missing values and interpolating / predicting non-existent values. Or are they similar enought that they can be treated the same. I would think so. These are just some quick thoughts before I forget. I haven't spent much time thinking a design through yet. I'd be happy to work on this in a month or so. Also does this fall in the realm of statsmodels?
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COMPAT: numpy compat with array methods in >= 1.7
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0
2013-08-02T00:23:37Z
2014-05-08T23:10:20Z
2013-09-29T19:20:49Z
CONTRIBUTOR
null
This is and API change in numpy >= 1.7, but need to work around. Numpy now looks at the structure for a p`rod` method (if the function is not operating on a numpy array), if it has one, then it passes the arguments `axis,dtype,out`, currently these types of methods only aceept `axis`, need to accept `dtype,out` as dummy arguments for compat affects `mean,sum,prod,std`...prob others ``` In [1]: df = DataFrame(randn(10,2)) In [2]: np.prod(df) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-2-99feb18ea783> in <module>() ----> 1 np.prod(df) /usr/local/lib/python2.7/site-packages/numpy/core/fromnumeric.pyc in prod(a, axis, dtype, out, keepdims) 2111 return _methods._prod(a, axis=axis, dtype=dtype, 2112 out=out, keepdims=keepdims) -> 2113 return prod(axis=axis, dtype=dtype, out=out) 2114 else: 2115 return _methods._prod(a, axis=axis, dtype=dtype, TypeError: prod() got an unexpected keyword argument 'dtype' In [3]: np.prod(df.values) Out[3]: -2.2566177751865827e-07 In [4]: np.prod(df.values,axis=1) Out[4]: array([-0.00912739, 0.00569453, 0.58508784, 0.87462665, -0.32556754, 0.12015118, 1.01860104, -0.15380337, -0.54518287, -2.5393832 ]) ```
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DOC: Fix typo
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2
2013-08-02T03:03:21Z
2014-07-16T08:21:25Z
2013-08-02T03:43:08Z
CONTRIBUTOR
null
Fixes minor typo in groupby documentation
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4,437
BUG: fix string truncation for astype(str)
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2
2013-08-02T04:07:23Z
2014-06-13T02:10:37Z
2013-08-03T03:29:56Z
MEMBER
null
closes #4405.
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17,543,850
MDU6SXNzdWUxNzU0Mzg1MA==
4,438
formatting_info is not passed to xlrd.open_workbook.when using ExcelFile
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1
2013-08-02T04:11:13Z
2013-09-28T20:43:47Z
2013-09-28T20:43:47Z
CONTRIBUTOR
null
I will submit PR
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17,543,903
MDExOlB1bGxSZXF1ZXN0NzM0MjgyNg==
4,439
ENH Pass kwds from ExcelFile ctr to xlrd.open_workbook. For example, thi...
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35
2013-08-02T04:14:51Z
2014-06-19T06:15:44Z
2013-09-24T03:43:05Z
CONTRIBUTOR
null
...s allows setting formatting_info=True (GH4438) closes #4438
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17,544,189
MDExOlB1bGxSZXF1ZXN0NzM0Mjk3Ng==
4,440
BUG: allow cumprod and cumsum to work with bool dtypes
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5
2013-08-02T04:30:40Z
2014-06-14T00:02:11Z
2013-08-07T19:17:18Z
MEMBER
null
closes #4170.
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17,544,269
MDU6SXNzdWUxNzU0NDI2OQ==
4,441
rank correlation in rolling_corr?
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2
2013-08-02T04:36:00Z
2018-07-06T22:52:56Z
2018-07-06T22:52:55Z
MEMBER
null
from [this](http://stackoverflow.com/q/17998284/564538) SO question
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4,442
.loc function failed
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2
2013-08-02T07:04:25Z
2013-08-02T09:54:24Z
2013-08-02T09:53:38Z
NONE
null
env: windows 7 64bit, pandas 0.12 ``` python In [106]: res Out[106]: <class 'pandas.core.frame.DataFrame'> Int64Index: 93705 entries, 13 to 93713 Columns: 3561 entries, 2) to 3561) dtypes: float64(3561) In [107]: res.loc[12] Out[107]: 0 2) 3.362026 3) 1.574658 4) 1.905283 6) 2.069734 8) 4.777519 9) 4.316618 11) 2.231455 ...... ``` my res's index starts with 13, but the numeric index loc[12] has values.
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MDU6SXNzdWUxNzU1NTQwOQ==
4,443
MultiIndex loc for value not in index
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8
2013-08-02T11:18:29Z
2013-08-02T14:00:11Z
2013-08-02T13:10:35Z
CONTRIBUTOR
null
example: ``` In [11]: s = pd.Series({(0, 0): 1, (0, 1): 2, (0, 3): 3, (1, 0): 1, (1, 2): 4, (3, 0): 5}) In [12]: s.loc[(1, 0):] Out[12]: (1, 0) 1 (1, 2) 4 (3, 0) 5 dtype: int64 In [13]: s.loc[(1, 1):] KeyError: 'start bound [(1, 1)] is not the [index]' ``` Should this work? cc @jreback _From [SO question](http://stackoverflow.com/questions/18015094/slice-pandas-series-with-elements-not-in-the-index)._
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17,558,230
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4,444
get_dummies docs
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20
2013-08-02T12:45:33Z
2013-10-21T22:23:36Z
2013-10-21T22:23:25Z
CONTRIBUTOR
null
Should put this in the online docs and elaborate / add an example to the docstring.
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4,445
TST: test updates for testing
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2013-08-02T12:49:38Z
2014-06-22T03:02:33Z
2013-08-02T12:49:43Z
CONTRIBUTOR
null
fixup testing as @jtratener suggest
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4,446
get_dummies with NaN
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1
2013-08-02T13:06:41Z
2013-08-26T23:43:59Z
2013-08-26T23:43:59Z
CONTRIBUTOR
null
get_dummies seems to get caught out by NaNs ``` In [11]: s1 = pd.Series(['a', 'a', np.nan, 'c', 'c', 'c']) In [12]: s1 Out[12]: 0 a 1 a 2 NaN 3 c 4 c 5 c dtype: object In [13]: pd.get_dummies(s1) Out[13]: a c 0 1 0 1 1 0 2 0 1 3 0 1 4 0 1 5 0 1 ``` A rogue c has been used as the NaN value, I think expected is: ``` In [14]: pd.get_dummies(s1[s1.notnull()]) Out[14]: a c 0 1 0 1 1 0 3 0 1 4 0 1 5 0 1 ```
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MDU6SXNzdWUxNzU2MjgxNg==
4,447
Filter of grouped dataframe raises ValueError
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15
2013-08-02T14:26:57Z
2013-10-13T23:37:53Z
2013-10-13T23:37:53Z
NONE
null
see also #4527 When trying to filter a grouped dataframe with more than 2 columns raises an ValueError: ``` python In [90]: dff = pd.DataFrame({'A': np.arange(8), 'B': list('aabbbbcc'), 'C': np.arange(8)}) In [91]: dff.groupby('B').filter(lambda x: len(x) > 2) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-91-89d79df28299> in <module>() ----> 1 dff.groupby('B').filter(lambda x: len(x) > 2) C:\Anaconda\lib\site-packages\pandas\core\groupby.pyc in filter(self, func, dropna, *args, **kwargs) 2092 res = path(group) 2093 -> 2094 if res: 2095 indexers.append(self.obj.index.get_indexer(group.index)) 2096 ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() In [93]: pd.__version__ Out[93]: '0.12.0' ```
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4,448
10 min to pandas: Reshaping: NameError - name 'lzip' not defined
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11
2013-08-02T15:17:00Z
2016-10-12T23:04:04Z
2013-08-02T18:43:57Z
NONE
null
I am up to reshaping and got the below error. ## In [127]: tuples = lzip(*[['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]) NameError Traceback (most recent call last) <ipython-input-127-e86111d4f47b> in <module>() ----> 1 tuples = lzip(*[['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]) NameError: name 'lzip' is not defined Any help kindly appreciated. TIA
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4,449
10 min to pandas: Plotting: NameError - name 'plt' not defined
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11
2013-08-02T18:58:56Z
2013-08-02T23:21:23Z
2013-08-02T23:21:23Z
NONE
null
10 min to pandas, p to Plotting and got the below In [187]: df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=['A', 'B', 'C', 'D']) In [188]: df = df.cumsum() In [189]: plt.figure(); df.plot(); plt.legend(loc='best')--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-189-7376f1bcd0b9> in <module>() ----> 1 plt.figure(); df.plot(); plt.legend(loc='best') NameError: name 'plt' is not defined In [190]:
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4,450
DOC: lrange, lzip --> list(range and list(zip
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4
2013-08-02T21:04:09Z
2014-06-29T23:18:48Z
2013-08-02T23:17:27Z
CONTRIBUTOR
null
Internally, definitely want to use `lzip` and `lrange`, but for clarity I think it's better to just use the written out versions of them, especially since they aren't really key components of pandas. (and generally would not expect someone working through an example to care about compatibility issues). This only changes docs. cc @cpcloud
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4,451
TST: pandas/util/testing : test improvements and cleanups
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16
2013-08-02T21:25:27Z
2014-07-16T08:21:34Z
2013-08-02T23:21:57Z
CONTRIBUTOR
null
A few things 1. Make with-statement-capable `assertRaises` and `assertRaisesRegexp` (which I want to use later in a cleanup of some of the parser tests). 2. Earlier introduction of `assert_attr_equal` changed it so that, if one object had an attribute that was `None` and the other did not have that attribute at all, it would pass. This is now fixed. 3. combines `assert_panel_equal` and `assert_panel4d_equal`, because they are both doing the same thing. Now it's just `assert_panelnd_equal` with specific assert functions for each. 4. Switched unnecessary `check_index_freq` check in `pandas.io.tests.test_pytables:TestHDFStore.test_index_types` to be `check_series_type`, (because none of the tests ever produce objects with `freqstr`) 5. Removed `check_index_freq` argument and corresponding code from `assert_series_equal` since it's not being actively used anywhere in the test suite
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4,452
pip --proxy=foo.com install pandas fails to download numpy dependency
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9
2013-08-02T23:12:25Z
2018-06-28T02:28:14Z
2018-06-28T02:28:14Z
NONE
null
I believe the problem here is that setup.py is trying to install numpy without using the proxy server passed to pip. ``` pip-2.7 install --proxy=http://sampleproxy.com:3128 pandas Downloading/unpacking pandas Downloading pandas-0.12.0.zip (3.4MB): 3.4MB downloaded Running setup.py egg_info for package pandas Download error on https://pypi.python.org/simple/numpy/: [Errno 110] Connection timed out -- Some packages may not be found! Couldn't find index page for 'numpy' (maybe misspelled?) ``` Installing numpy using `pip --proxy=proxy.com:3128 install numpy` and then install pandas as above worked fine.
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should read_html parse the `src` attribute of an `img` tag?
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1
2013-08-02T23:14:13Z
2014-01-29T04:05:13Z
2014-01-29T04:05:13Z
MEMBER
null
had this thought while looking at #3606
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ENH: allow column oriented table storage in HDFStore
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22
2013-08-02T23:16:39Z
2021-04-11T02:56:19Z
null
CONTRIBUTOR
null
renamed `carray` package: https://github.com/Blosc/bcolz Soliciting any comments on this proposal to create a columnar access table in `HDFStore`. This is actually very straightforward to do. need a new kw argument to describe the type of format for storage: ``format='s|t|c'` (also allows expansion in the future to other formats) - `s` is the `Storer` format (e.g. `store['df'] = value`), implied currently with `put`` - `t` is the `Table` format (e.g. `store.append('df',value)`, created with `table=True` when using `put` or using `append` - `c` is a `CTable` format (new), which is a column oriented table so will essentially deprecate `append=,table=` keywords (or just translate them) to a `format=` kw. ``` df.to_hdf('test.h5','df',format='c') ``` Will have a master node which holds the structure. Will store a format with a single column from a `DataFrame` in a sub-node of the master. advantages: - index(s) are kept in their own columns (this is true with `Table` now) - allows easy delete/add of columns (somewhat tricky in the `Table` format) - allows appends (interesting twist is that have to keep the indices in sync) - selection is straightforward as everything is indexed the same - selecting a small number of columns relative to the total should be faster than an equivalent `Table` - API will be the same as current. This is essentially an extension of the `append_as_multiple / select_as_multiple` multi-table accssors. - can be included/coexist alongside existing `Table/Storer`s disadvantages: - selecting lots of columns will be somewhat slower that an equivalent `Table` - requires syncing of all the indices (the coordinates of all rows) - delete operations will be somewhat slower than an equivalent `Table` There are actually 2 different formats that could be used here, I propose just the single-file for now. However, The sub-nodes could be spread out in a directory and stored as separate files. This allows concurrent access with some concurrent reads allowed (this is pretty tricky, so hold off on this for now). This `CTable` format will use the existing `PyTables` infrastructure under the hood; it is possible to use the `ctable` module however http://carray.pytables.org/docs/manual/ (this is basically what BLAZE uses under the hood for its storage backend)
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4,455
awkward repring on python3 bytes types
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0
2013-08-03T01:02:40Z
2013-08-03T20:01:22Z
2013-08-03T20:01:22Z
MEMBER
null
might be an edge case as i'm not sure how useful storing byte strings in `Series` objects is, nonetheless they are repring like `tuple` objects in Python 3 ``` In [37]: import string In [38]: d = string.digits * 10 In [39]: s = Series([d, tm.rands(10), tm.rands(10000)]) In [40]: s Out[40]: 0 0123456789012345678901234567890123456789012345... 1 c0HYcb0BlY 2 IghbSU964QRgNimc0jZn2etcTFhRgHnHQepH1dwAqVZLar... dtype: object In [41]: b = lambda x, encoding='utf8': bytes(x, encoding=encoding) In [42]: s.map(b) Out[42]: 0 (48, 49, 50, 51, 52, 53, 54, 55, 56, 57, ...) 1 (99, 48, 72, 89, 99, 98, 48, 66, 108, 89) 2 (73, 103, 104, 98, 83, 85, 57, 54, 52, 81, ...) dtype: object ``` PR coming soon with a couple of other output formatting fixes as well regarding py3 compat
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4,456
BUG/OFMT: fix repring of python3 bytes objects
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3
2013-08-03T01:21:45Z
2014-06-18T13:51:07Z
2013-08-03T20:01:22Z
MEMBER
null
closes #4455.
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4,457
PERF: enhance HDFStore Table writing performance
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2
2013-08-03T17:38:27Z
2014-07-16T08:21:39Z
2013-08-05T12:06:32Z
CONTRIBUTOR
null
Took out all of the cython code, turns out you can assign directly to numpy recarrays per column if you put in the correct shape yourself ``` Invoked with : --ncalls: 5 --repeats: 5 ------------------------------------------------------------------------------- Test name | head[ms] | base[ms] | ratio | ------------------------------------------------------------------------------- write_store_table_mixed | 45.6208 | 121.5352 | 0.3754 | write_store_table_panel | 59.4140 | 99.5456 | 0.5969 | write_store_table | 40.2264 | 65.8978 | 0.6104 | write_store_table_dc | 143.3910 | 174.9424 | 0.8196 | read_store_table_wide | 16.8930 | 17.5310 | 0.9636 | query_store_table_wide | 9.6692 | 9.7564 | 0.9911 | write_store_table_wide | 117.8906 | 118.8232 | 0.9922 | read_store_table_mixed | 4.7604 | 4.7478 | 1.0026 | query_store_table | 4.2646 | 4.2320 | 1.0077 | read_store_table | 2.3678 | 2.3178 | 1.0216 | read_store_table_panel | 20.9388 | 20.3526 | 1.0288 | ------------------------------------------------------------------------------- Test name | head[ms] | base[ms] | ratio | ------------------------------------------------------------------------------- Ratio < 1.0 means the target commit is faster then the baseline. Seed used: 1234 Target [d11041d] : PERF: enhance HDFStore Table writing performance Base [aca1a42] : Merge pull request #4437 from cpcloud/fix-astype-calls ```
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Get dummies
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14
2013-08-04T17:32:01Z
2014-06-17T22:55:05Z
2013-08-26T23:43:59Z
CONTRIBUTOR
null
fixes #4446, #4444 Added new functionality dummy_na (thoughts?). it's slightly different to a possible dropna argument, which I haven't included (which can be achieved using `pd.get_dummies(s.dropna())`. Example: ``` In [3]: s = ['a', 'b', np.nan] In [4]: pd.get_dummies(s) Out[4]: a b 0 1 0 1 0 1 2 0 0 In [5]: pd.get_dummies(s, dummy_na=True) Out[5]: a b NaN 0 1 0 0 1 0 1 0 2 0 0 1 In [6]: pd.get_dummies(pd.Series(s).dropna()) # different Out[6]: a b 0 1 0 1 0 1 ``` Note: atm there is a (strange) test Failure with above example, not quite sure what's going on: ``` res_na = get_dummies(s, dummy_na=True) exp_na = DataFrame({nan: {0: 0.0, 1: 0.0, 2: 1.0}, 'a': {0: 1.0, 1: 0.0, 2: 0.0}, 'b': {0: 0.0, 1: 1.0, 2: 0.0}}).iloc[:, [1, 2, 0]] # need to reorder cols assert_frame_equal(res_na, exp_na) -> assert(left.columns.equals(right.columns)) (Pdb) left a b NaN 0 1 0 0 1 0 1 0 2 0 0 1 (Pdb) right a b NaN 0 1 0 0 1 0 1 0 2 0 0 1 ```
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6
2013-08-04T19:10:44Z
2014-06-26T11:48:10Z
2013-08-05T01:44:08Z
CONTRIBUTOR
null
The change is minor but so easy I thought I'd submit it: If you switch the list comprehensions to generators, it sidesteps leaking the temporary variables into the class variable scope. This will be microscopically slower, but they are just class variables so it really doesn't matter. fixes #3440 https://travis-ci.org/danbirken/pandas/builds/9836459 ``` import numpy as np from pandas import DataFrame, Panel df0 = DataFrame(np.zeros([3,4])) df1 = DataFrame(np.ones([3,4])) p = Panel({'a':df0, 'b':df1}) In [1]: p.a Out[1]: 0 1 2 3 0 0 0 0 0 1 0 0 0 0 2 0 0 0 0 In [2]: p.b Out[2]: 0 1 2 3 0 1 1 1 1 1 1 1 1 1 2 1 1 1 1 ```
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5
2013-08-05T02:06:35Z
2014-06-20T16:03:18Z
2013-08-05T11:41:19Z
CONTRIBUTOR
null
I came across this problem when I was doing some work for #4362. When a frame with mixed dtypes including Timestamps has dupe column labels the values array returned is different, as illustrated below (master branch): ``` python In [3]: df = pd.DataFrame([[pd.Timestamp('20130101'),3.5],[pd.Timestamp('20130102'),4.5]], columns=['x', 'x'], index=[1,2]) In [4]: df Out[4]: x x 1 2013-01-01 00:00:00 3.5 2 2013-01-02 00:00:00 4.5 In [5]: df.values Out[5]: array([[1356998400000000000L, 3.5], [1357084800000000000L, 4.5]], dtype=object) In [6]: df = pd.DataFrame([[pd.Timestamp('20130101'),3.5],[pd.Timestamp('20130102'),4.5]], columns=['x', 'y'], index=[1,2]) In [7]: df Out[7]: x y 1 2013-01-01 00:00:00 3.5 2 2013-01-02 00:00:00 4.5 In [8]: df.values Out[8]: array([[datetime.datetime(2013, 1, 1, 0, 0), 3.5], [datetime.datetime(2013, 1, 2, 0, 0), 4.5]], dtype=object) ``` The included changes should fix the problem. This is my first time messing about in pandas internals so any feedback is appreciated! Possibly related to #4377
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32
2013-08-05T09:23:07Z
2017-08-24T09:53:17Z
2015-01-18T21:39:22Z
CONTRIBUTOR
null
## Fix Issue #4160 While renamed a `MultiIndex`, the `mapper` walk through every tuple in the MultiIndex and every element in a tuple. So the mapper { (index1, index2): (new_index1, new_index2) } didn't work for the MultiIndex case. This pull request is to modify it to walk through every tuple in a MultiIndex and use the mapper to map each tuple. Also, the test case has been added.
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2013-08-05T13:23:02Z
2014-07-16T08:21:48Z
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CONTRIBUTOR
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11
2013-08-05T13:48:34Z
2013-08-16T19:28:19Z
2013-08-16T19:28:19Z
CONTRIBUTOR
null
All of the following current fail because of in-place dtype conversions ``` In [2]: s = Series([1,2,3]) In [3]: s.iloc[0] = np.nan ValueError: cannot convert float NaN to integer In [4]: s.loc[0] = np.nan ValueError: cannot convert float NaN to integer In [5]: s[0] = np.nan TypeError: 'int' object is not iterable ```
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ER: compound dtypes - DataFrame constructor/astype
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37
2013-08-05T15:04:16Z
2021-06-06T00:01:38Z
null
NONE
null
xref #9133, maybe allow a dict of dtypes to be passed as well I trying to use the dtype argument in the DataFrame constructor to set the types of several columns, and I'm getting incorrect types. Everything works well, however, when the dtypes come from the recarray itself. ``` In [61]: data = [(1,1.2), (2,2.3)] dtype = [('a','i4'),('b','f4')] a = np.array(data, dtype=dtype) pd.DataFrame(a).dtypes Out [61]: a int32 b float32 dtype: object ``` But if I use the dtype constructor argument, I get incorrect types: ``` In [65]: pd.DataFrame(data, dtype=dtype).dtypes Out [65]: 0 object 1 object dtype: object ``` The astype() member function doesn't work either: ``` In [75]: pd.DataFrame(data).astype(dtype) Truncated Traceback (Use C-c C-x to view full TB): c:\Anaconda\lib\site-packages\pandas\core\common.pyc in take_nd(arr, indexer, axis, out, fill_value, mask_info, allow_fill) 491 indexer = _ensure_int64(indexer) 492 if not allow_fill: --> 493 dtype, fill_value = arr.dtype, arr.dtype.type() 494 mask_info = None, False 495 else: TypeError: function takes exactly 1 argument (0 given) ``` I'm using Pandas 0.11.0 from Anaconda. Thanks in advance.
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DOC: split out Categorical in groupby.rst to its own bullet(and add examples?)
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1
2013-08-05T17:59:02Z
2014-07-25T22:43:48Z
2014-07-25T22:43:48Z
CONTRIBUTOR
null
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17,652,046
MDU6SXNzdWUxNzY1MjA0Ng==
4,466
ENH: Ignoring empty lines in files
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6
2013-08-05T19:02:01Z
2014-09-19T17:56:24Z
2014-09-19T17:56:24Z
NONE
null
Currently pandas preserves empty lines when reading CSV files. I think the default should be to ignore empty lines, with a possible optional parameter to retain them. Implementing this in the C and Python parsers should be an easy extension of my work on #2685 , but I wanted some feedback before I do so.
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API: make selecting coordinate / masking a bit easier in HDFStore
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3
2013-08-05T19:04:57Z
2013-08-05T23:05:05Z
2013-08-05T23:05:05Z
CONTRIBUTOR
null
From the PyTables ML Select where month=5 from the index (this could be done internally maybe) big issues is that Coordinates is sort of 'private' here, make where take a boolean array / coordinates ``` # create a frame In [45]: df = DataFrame(randn(1000,2),index=date_range('20000101',periods=1000)) In [53]: df Out[53]: <class 'pandas.core.frame.DataFrame'> DatetimeIndex: 1000 entries, 2000-01-01 00:00:00 to 2002-09-26 00:00:00 Freq: D Data columns (total 2 columns): 0 1000 non-null values 1 1000 non-null values dtypes: float64(2) # store it as a table In [46]: store = pd.HDFStore('test.h5',mode='w') In [47]: store.append('df',df) # select out the index (a datetimeindex in this case) In [48]: c = store.select_column('df','index') # get the coordinates of matching index In [49]: coords = c[pd.DatetimeIndex(c).month==5] # select those rows In [51]: from pandas.io.pytables import Coordinates In [50]: store.select('df',where=Coordinates(coords.index,None,None)) Out[50]: <class 'pandas.core.frame.DataFrame'> DatetimeIndex: 93 entries, 2000-05-01 00:00:00 to 2002-05-31 00:00:00 Data columns (total 2 columns): 0 93 non-null values 1 93 non-null values dtypes: float64(2) ```
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17,655,872
MDU6SXNzdWUxNzY1NTg3Mg==
4,468
Support for Reading Excel Files with Hierarchical Columns Names
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4
2013-08-05T19:48:10Z
2013-10-17T22:33:19Z
2013-10-02T21:03:35Z
CONTRIBUTOR
null
related #4438 #4679 For example see the "INCOME_STATEMENT" sheet on http://shareholder.api.edgar-online.com/efx_dll/edgarpro.dll?FetchFilingXLS1?sessionid=F7D_689jk-BEFvN&ID=9277772 There are four columns in the desired DataFrame: ``` 1 2 3 4 Thirteen weeks ended Twenty-six weeks ended March 30, March 24, March 30, March 24, ``` Currently the `ExcelFile` parser only takes an int for the header parameter, whereas the html parser takes "The row (or rows for a MultiIndex) to use to make the columns headers."
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4,469
Add context to return from pandas.io.html.read_html
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42
2013-08-05T20:12:21Z
2014-01-24T20:14:06Z
2014-01-24T20:14:06Z
CONTRIBUTOR
null
Currently `pandas.io.html.read_html` returns a list of DataFrames. This offers no context as to where in the source HTML the table was found. For example, a user might be interested in the title or caption of the table. For example in an SEC 10-Q filing (see for example: http://apps.shareholder.com/sec/viewerContent.aspx?companyid=GMCR&docid=9277772#A13-6685_110Q_HTM_UNAUDITEDCONSOLIDATEDSTATEMENTSOF_223103), there are many tables. The user might be interested in one or more of them. They are not returned in a "standard" order such that without the title provided in the HTML document, finding the desired table in the return list is difficult. My suggestion is to offer some way of linking the returned table to the context in which it was found.
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ENH: allow where to be a list/array or a boolean mask of locations (GH4467)
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0
2013-08-05T21:53:53Z
2014-06-28T10:41:55Z
2013-08-05T23:05:05Z
CONTRIBUTOR
null
closes #4467
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lib.maybe_convert_objects will fail on uint64 values that exceed int64 max
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18
2013-08-05T22:58:49Z
2016-12-20T13:49:05Z
2016-12-20T13:49:05Z
MEMBER
null
xref #11440 for addtl tests Observed in the wild. cc @blais
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BUG: Boxplot to return axes, not dict
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25
2013-08-05T23:02:19Z
2014-07-06T10:05:31Z
2014-05-10T23:57:57Z
NONE
null
Fixes #4264.
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17,671,458
MDU6SXNzdWUxNzY3MTQ1OA==
4,473
TestDataFrame.test_from_records_sequencelike: segfault on armel
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24
2013-08-06T03:21:47Z
2014-01-21T22:11:39Z
2014-01-21T22:11:39Z
CONTRIBUTOR
null
gory details ``` Starting program: /usr/bin/python2.7-dbg /usr/bin/nosetests -s -v -a \!network pandas/tests/test_frame.py:TestDataFrame.test_from_records_sequencelike [Thread debugging using libthread_db enabled] Using host libthread_db library "/lib/arm-linux-gnueabi/libthread_db.so.1". Program received signal SIGILL, Illegal instruction. Program received signal SIGILL, Illegal instruction. test_from_records_sequencelike (pandas.tests.test_frame.TestDataFrame) ... Program received signal SIGSEGV, Segmentation fault. 0xb59b5478 in __pyx_pf_6pandas_3lib_122is_string_array (__pyx_self=0x0, __pyx_v_values=0x1727668) at pandas/lib.c:32348 32348 __Pyx_INCREF((PyObject*)__pyx_t_3); (gdb) bt #0 0xb59b5478 in __pyx_pf_6pandas_3lib_122is_string_array (__pyx_self=0x0, __pyx_v_values=0x1727668) at pandas/lib.c:32348 #1 0xb59b4248 in __pyx_pw_6pandas_3lib_123is_string_array (__pyx_self=0x0, __pyx_v_values=<numpy.ndarray at remote 0x1727668>) at pandas/lib.c:32136 #2 0x0009e498 in PyCFunction_Call (func=<built-in function is_string_array>, arg=(<numpy.ndarray at remote 0x1727668>,), kw=0x0) at ../Objects/methodobject.c:101 #3 0x0002e178 in PyObject_Call (func=<built-in function is_string_array>, arg=(<numpy.ndarray at remote 0x1727668>,), kw=0x0) at ../Objects/abstract.c:2529 #4 0xb59abe48 in __pyx_pf_6pandas_3lib_108infer_dtype (__pyx_self=0x0, __pyx_v__values=<numpy.ndarray at remote 0x1727668>) at pandas/lib.c:30465 #5 0xb59a81d8 in __pyx_pw_6pandas_3lib_109infer_dtype (__pyx_self=0x0, __pyx_v__values=<numpy.ndarray at remote 0x1727668>) at pandas/lib.c:29761 #6 0x00152aa4 in call_function (pp_stack=0xbeffaa74, oparg=1) at ../Python/ceval.c:4009 #7 0x0014d524 in PyEval_EvalFrameEx ( f=Frame 0x12bf850, for file /home/yoh/pandas/pandas-0.12.0/pandas/core/common.py, line 1220, in _possibly_cast_to_datetime (value=<numpy.ndarray at remote 0x1727668>, dtype=None, coerce=False, v=<numpy.ndarray at remote 0x1727668>), throwflag=0) at ../Python/ceval.c:2666 ``` python backtrace ``` (gdb) py-bt #7 Frame 0x12bf850, for file /home/yoh/pandas/pandas-0.12.0/pandas/core/common.py, line 1220, in _possibly_cast_to_datetime (value=<numpy.ndarray at remote 0x1727668>, dtype=None, coerce=False, v=<numpy.ndarray at remote 0x1727668>) inferred_type = lib.infer_dtype(v) #11 Frame 0x12a37b0, for file /home/yoh/pandas/pandas-0.12.0/pandas/core/series.py, line 3318, in _try_cast (arr=<numpy.ndarray at remote 0x1727668>, take_fast_path=True) arr = com._possibly_cast_to_datetime(arr, dtype) #15 Frame 0x115d810, for file /home/yoh/pandas/pandas-0.12.0/pandas/core/series.py, line 3349, in _sanitize_array (data=<numpy.ndarray at remote 0x1727668>, index=<Int64Index(name=None) at remote 0x17289d8>, dtype=None, copy=False, raise_cast_failure=False, _try_cast=<function at remote 0x1728a30>, subarr=<numpy.ndarray at remote 0x1727668>) subarr = _try_cast(data, True) #19 Frame 0x17579c0, for file /home/yoh/pandas/pandas-0.12.0/pandas/core/frame.py, line 5936, in _homogenize (data=[<numpy.ndarray at remote 0x1723ea8>, <numpy.ndarray at remote 0x1723f68>, <numpy.ndarray at remote 0x17273e8>, <numpy.ndarray at remote 0x1727668>, <numpy.ndarray at remote 0x17271a8>, <numpy.ndarray at remote 0x17274e8>, <numpy.ndarray at remote 0x1727228>, <numpy.ndarray at remote 0x17270e8>], index=<Int64Index(name=None) at remote 0x17289d8>, dtype=None, _sanitize_array=<function at remote 0x10f1b90>, oindex=None, homogenized=[<numpy.ndarray at remote 0x1723ea8>, <numpy.ndarray at remote 0x1723f68>, <numpy.ndarray at remote 0x17273e8>], v=<numpy.ndarray at remote 0x1727668>) raise_cast_failure=False) #23 Frame 0x17571e0, for file /home/yoh/pandas/pandas-0.12.0/pandas/core/frame.py, line 5675, in _arrays_to_mgr (arrays=[<numpy.ndarray at remote 0x1723ea8>, <numpy.ndarray at remote 0x1723f68>, <numpy.ndarray at remote 0x17273e8>, <numpy.ndarray at remote 0x1727668>, <numpy.ndarray at remote 0x17271a8>, <numpy.ndarray at remote 0x17274e8>, <numpy.ndarray at remote 0x1727228>, <numpy.ndarray at remote 0x17270e8>], arr_names=<Index(name=None) at remote 0x17286c0>, index=<Int64Index(name=None) at remote 0x17289d8>, columns=<...>, dtype=None) arrays = _homogenize(arrays, index, dtype) #27 Frame 0x17ce8a0, for file /home/yoh/pandas/pandas-0.12.0/pandas/core/frame.py, line 1124, in from_records (cls=<type at remote 0x129f680>, data=<recarray at remote 0x165dc40>, index=None, exclude=set([]), columns=<Index(name=None) at remote 0x17286c0>, coerce_float=False, nrows=None, arr_columns=<...>, arrays=[<numpy.ndarray at remote 0x1723ea8>, <numpy.ndarray at remote 0x1723f68>, <numpy.ndarray at remote 0x17273e8>, <numpy.ndarray at remote 0x1727668>, <numpy.ndarray at remote 0x17271a8>, <numpy.ndarray at remote 0x17274e8>, <numpy.ndarray at remote 0x1727228>, <numpy.ndarray at remote 0x17270e8>], result_index=None) columns) ``` and here what I think was original/generated code ``` (gdb) bt 1 #0 0xb59b5478 in __pyx_pf_6pandas_3lib_122is_string_array (__pyx_self=0x0, __pyx_v_values=0x1727668) at pandas/lib.c:32348 (More stack frames follow...) (gdb) l 32335 32330 /* "pandas/src/inference.pyx":230 32331 * 32332 * for i in range(n): 32333 * if not PyString_Check(objbuf[i]): # <<<<<<<<<<<<<< 32334 * return False 32335 * return True 32336 */ 32337 __pyx_t_13 = __pyx_v_i; 32338 __pyx_t_8 = -1; 32339 if (__pyx_t_13 < 0) { (gdb) 32340 __pyx_t_13 += __pyx_pybuffernd_objbuf.diminfo[0].shape; 32341 if (unlikely(__pyx_t_13 < 0)) __pyx_t_8 = 0; 32342 } else if (unlikely(__pyx_t_13 >= __pyx_pybuffernd_objbuf.diminfo[0].shape)) __pyx_t_8 = 0; 32343 if (unlikely(__pyx_t_8 != -1)) { 32344 __Pyx_RaiseBufferIndexError(__pyx_t_8); 32345 {__pyx_filename = __pyx_f[1]; __pyx_lineno = 230; __pyx_clineno = __LINE__; goto __pyx_L1_error;} 32346 } 32347 __pyx_t_3 = (PyObject *) *__Pyx_BufPtrStrided1d(PyObject **, __pyx_pybuffernd_objbuf.rcbuffer->pybuffer.buf, __pyx_t_13, __pyx_pybuffernd_objbuf.diminfo[0].strides); 32348 __Pyx_INCREF((PyObject*)__pyx_t_3); 32349 __pyx_t_7 = ((!(PyString_Check(__pyx_t_3) != 0)) != 0); ``` any obvious clues? ;)
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17,671,618
MDU6SXNzdWUxNzY3MTYxOA==
4,474
2to3 refactor broke test_perf.py
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3
2013-08-06T03:28:52Z
2013-08-07T00:27:58Z
2013-08-07T00:27:58Z
NONE
null
https://github.com/pydata/pandas/pull/4384 when run in HEAD mode, checking out the test_perf.py revision in git master should run without issues no matter what version of pandas you have checked out (at least as far back as 9.1, probably further). Now that the script imports `pandas.compat`, it's broken for anything older then a couple of weeks.
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MDExOlB1bGxSZXF1ZXN0NzQwNTUxNw==
4,475
Sql alchemy tests
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16
2013-08-06T03:40:33Z
2014-06-18T16:01:24Z
2014-01-19T05:05:35Z
CONTRIBUTOR
null
WIP for #4163. Not ready to merge yet New test structure for sql, and working through sql/test_sql with abstract syntax (aim is to not be sql-platform specific anywhere).
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Default to named index `name` when column named `name` not found
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1
2013-08-06T08:31:22Z
2018-06-28T02:30:00Z
2018-06-28T02:30:00Z
CONTRIBUTOR
null
There is an asymmetry in Pandas wrt. indexes vs. columns that is not present in the relational model. For example, for the purposes of merging one has to decide whether the column on which to merge is an index or not, despite the fact that indexing is incidental complexity for optimization. This leads to many of us having duplicate indexes and columns with the same name, which can cause errors and wastes memory for large data frames. A possible fix would be to address the column lookup in all the current operations that require columns or indexes so that on NameError for columns the named indexes would be fetched instead. Or one may as well go all the way relational and make _any_ `df[my_col_or_named_index]` lookup fetch a named index if the column name is not found. The latter behaviour is being implemented in the eval/query functionality by @jreback and @cpcloud in #4164, so it would also be 'least surprise' to use this policy across the board.
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17,684,106
MDU6SXNzdWUxNzY4NDEwNg==
4,477
2to3 refactor broke vbenches
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2
2013-08-06T11:03:33Z
2013-08-07T00:25:47Z
2013-08-07T00:25:47Z
NONE
null
looks like lmap is now called by some vbenches, which breaks the tests on all older versions. ``` .....................................................groupby_transform died with: name 'lmap' is not defined Skipping... groupby_transform died with: name 'lmap' is not defined Skipping... ``` #4384, #4474
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17,684,433
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4,478
BUG: revert 2/3 changes in vbsuite
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20
2013-08-06T11:12:28Z
2014-07-16T08:21:54Z
2013-08-07T00:25:47Z
CONTRIBUTOR
null
Fixes #4477, #4474
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17,691,072
MDU6SXNzdWUxNzY5MTA3Mg==
4,479
Error in parsing CSV file with read_table
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6
2013-08-06T13:53:49Z
2013-09-27T20:15:34Z
2013-09-27T20:15:34Z
NONE
null
When parsing this CSV file (http://genes.mit.edu/burgelab/yarden/test.res), pandas gives the error `CParserError: Error tokenizing data`. Test case: ``` In [1]: import pandas In [2]: data = pandas.read_table("./test.res", sep="\t") --------------------------------------------------------------------------- CParserError Traceback (most recent call last) <ipython-input-2-6b1a2e105fb1> in <module>() ----> 1 data = pandas.read_table("./test.res", sep="\t") /usr/local/lib/python2.7/dist-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/io/parsers.pyc in parser_f(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, nrows, iterator, chunksize, verbose, encoding, squeeze) 397 buffer_lines=buffer_lines) 398 --> 399 return _read(filepath_or_buffer, kwds) ... /usr/local/lib/python2.7/dist-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/io/parsers.pyc in read(self, nrows) 952 953 try: --> 954 data = self._reader.read(nrows) 955 except StopIteration: 956 if nrows is None: /usr/local/lib/python2.7/dist-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/_parser.so in pandas._parser.TextReader.read (pandas/src/parser.c:5925)() /usr/local/lib/python2.7/dist-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/_parser.so in pandas._parser.TextReader._read_low_memory (pandas/src/parser.c:6145)() /usr/local/lib/python2.7/dist-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/_parser.so in pandas._parser.TextReader._read_rows (pandas/src/parser.c:6750)() /usr/local/lib/python2.7/dist-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/_parser.so in pandas._parser.TextReader._tokenize_rows (pandas/src/parser.c:6634)() /usr/local/lib/python2.7/dist-packages/pandas-0.10.1-py2.7-linux-x86_64.egg/pandas/_parser.so in pandas._parser.raise_parser_error (pandas/src/parser.c:17055)() CParserError: Error tokenizing data. C error: Expected 2073 fields in line 4, saw 2074 ``` The pandas version was: ``` In [3]: pandas.__version__ Out[3]: '0.10.1' ``` Is this a known issue? Could I reformat the file somehow to make it work? Thanks. Best, --Yarden
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4,480
Add set operations to Series objects
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42
2013-08-06T14:57:15Z
2021-06-17T03:07:47Z
null
MEMBER
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from [this SO question](http://stackoverflow.com/questions/18079563/finding-the-intersection-between-two-series-in-pandas)
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MDU6SXNzdWUxNzY5NzkwNg==
4,481
DataFrame.roll method
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1
2013-08-06T15:47:02Z
2013-12-12T03:21:35Z
2013-12-12T03:21:35Z
MEMBER
null
from [the SO question](http://stackoverflow.com/questions/18082398/pandas-convert-upper-triangular-dataframe-by-shifting-rows-to-the-left)
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BUG/ER: Stricter testing of 'monotocity' when reindexing with ffill or bfill.
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7
2013-08-06T16:48:03Z
2014-06-14T19:56:36Z
2013-08-22T17:12:13Z
CONTRIBUTOR
null
closes #4484 Reversing an index and forward filling succeeds but produces misleading data: ``` python >>> dr = pd.date_range('2013-08-01', periods=6, freq='B') >>> df = pd.DataFrame(np.random.randn(6,1), index=dr, columns=list('A')) >>> df['A'][3] = np.nan >>> df.reindex(df.index[::-1], method='ffill') A 2013-08-08 2.127302 2013-08-07 NaN 2013-08-06 NaN 2013-08-05 NaN 2013-08-02 NaN 2013-08-01 NaN ``` This change enforces "monotocity" (well actually weakly increasing) and causes a ValueError to be raised.
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BUG/ER: misleading results when filling with a reversed index
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1
2013-08-06T17:55:25Z
2013-09-15T12:00:28Z
2013-09-15T12:00:28Z
CONTRIBUTOR
null
created by @prossahl Reversing an index and forward filling succeeds but produces misleading data: ``` >>> dr = pd.date_range('2013-08-01', periods=6, freq='B') >>> df = pd.DataFrame(np.random.randn(6,1), index=dr, columns=list('A')) >>> df['A'][3] = np.nan >>> df.reindex(df.index[::-1], method='ffill') A 2013-08-08 2.127302 2013-08-07 NaN 2013-08-06 NaN 2013-08-05 NaN 2013-08-02 NaN 2013-08-01 NaN ``` This change enforces "monotocity" (well actually weakly increasing) and causes a ValueError to be raised.
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4,485
BUG: off-by-one when doing network test repeats
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24
2013-08-06T19:32:15Z
2014-07-16T08:22:01Z
2013-09-20T02:57:56Z
CONTRIBUTOR
null
Imagine num_runs is two, then runs will take on the values [0, 1] and runs < num_uns will always be False. Hence any genuine Exception the code might raise will be silently ignored and just printed.
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Erroneous AssertionError: Index length did not match values on plot
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16
2013-08-06T21:13:06Z
2014-04-15T20:21:17Z
2013-08-07T17:22:27Z
NONE
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I have what appears to be a valid `Series` with a time series index as follows: ``` In [41]: inactive Out[41]: 2010-06-10 20:43:07 37.2 2010-06-21 06:37:28 0.0 2009-07-20 06:53:38 0.0 2012-05-17 01:50:13 27.4 2009-07-27 21:09:15 0.0 2010-05-04 09:06:54 0.0 2010-05-06 03:38:54 32.5 2010-05-09 08:33:16 56.9 2010-05-26 18:58:42 0.0 2010-05-31 18:40:41 0.0 2010-06-13 08:46:17 0.0 2010-06-16 18:27:51 0.0 2010-06-19 11:59:48 0.0 2010-06-24 15:24:26 33.4 2010-06-28 10:54:27 0.0 ... 2008-11-02 07:35:22 71.2 2008-11-05 17:33:39 97.6 2009-10-22 23:15:39 75.7 2009-10-27 20:55:07 67.5 2009-06-02 13:57:15 35.8 2009-06-06 14:15:41 25.9 2008-11-07 01:22:46 0.0 2009-10-12 17:08:20 100.0 2010-09-19 10:21:39 0.0 2010-09-24 15:46:50 0.0 2010-09-30 16:48:09 0.0 2010-10-08 21:43:10 0.0 2010-10-15 19:27:11 0.0 2010-10-28 19:10:42 0.0 2010-06-13 10:32:45 100.0 Name: pdgt10, Length: 13536, dtype: float64 ``` Note also: ``` In [43]: inactive.isnull().sum() Out[43]: 0 In [45]: inactive.index.size Out[45]: 13536 In [46]: inactive.values.size Out[46]: 13536 ``` However, when I try calling `inactive.plot()` in order to get a time series plot, I receive a puzzling `AssertionError`: ``` In [40]: inactive.plot() --------------------------------------------------------------------------- AssertionError Traceback (most recent call last) <ipython-input-40-cdcfa4d871e5> in <module>() ----> 1 inactive.plot() /Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/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) 1730 secondary_y=secondary_y, **kwds) 1731 -> 1732 plot_obj.generate() 1733 plot_obj.draw() 1734 /Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/tools/plotting.pyc in generate(self) 856 self._compute_plot_data() 857 self._setup_subplots() --> 858 self._make_plot() 859 self._post_plot_logic() 860 self._adorn_subplots() /Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/tools/plotting.pyc in _make_plot(self) 1244 lines = [] 1245 labels = [] -> 1246 x = self._get_xticks(convert_period=True) 1247 1248 plotf = self._get_plot_function() /Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/tools/plotting.pyc in _get_xticks(self, convert_period) 1034 x = index._mpl_repr() 1035 elif is_datetype: -> 1036 self.data = self.data.reindex(index=index.order()) 1037 x = self.data.index._mpl_repr() 1038 else: /Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/core/series.pyc in reindex(self, index, method, level, fill_value, limit, copy, takeable) 2649 2650 # GH4246 (dispatch to a common method with frame to handle possibly duplicate index) -> 2651 return self._reindex_with_indexers(new_index, indexer, copy=copy, fill_value=fill_value) 2652 2653 def _reindex_with_indexers(self, index, indexer, copy, fill_value): /Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/core/series.pyc in _reindex_with_indexers(self, index, indexer, copy, fill_value) 2653 def _reindex_with_indexers(self, index, indexer, copy, fill_value): 2654 new_values = com.take_1d(self.values, indexer, fill_value=fill_value) -> 2655 return Series(new_values, index=index, name=self.name) 2656 2657 def reindex_axis(self, labels, axis=0, **kwargs): /Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/core/series.pyc in __new__(cls, data, index, dtype, name, copy) 491 else: 492 subarr = subarr.view(Series) --> 493 subarr.index = index 494 subarr.name = name 495 /Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/lib.so in pandas.lib.SeriesIndex.__set__ (pandas/lib.c:29092)() AssertionError: Index length did not match values ``` As I have shown above, my index length and my values are the same length. Running a recent (<2 weeks) build from master on OS X 10.8.4 and Python 2.7.2.
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error in doc building
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6
2013-08-06T23:27:43Z
2016-10-12T23:04:04Z
2013-09-30T13:23:04Z
CONTRIBUTOR
null
http://pandas.pydata.org/pandas-docs/dev/io.html#clipboard there are error msgs
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4,488
Auto-detect field widths in read_fwf when unspecified
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14
2013-08-06T23:49:27Z
2013-09-30T11:23:28Z
2013-09-30T10:18:12Z
NONE
null
Here's a quick hack for generating the `widths` arg. It's basic, but it works for me. ``` def detect_fwf_widths(rows,wsratio=0.5,nlines=100): from collections import Counter # 2.6, is this in pd.compat? # could do a p-value thing instead, but this works fine in practice nlines = min(len(rows),nlines) # columwise, get occurence counts, use threshold to prune candidates # for "blank". counts = [{k:v for k,v in d.iteritems() if v > nlines*wsratio} for d in map(Counter,zip(*rows))] # among candidates, find the most common and choose it as "blank" # then extract only the cols where "blank" appears "frequently". c=Counter() for d in counts: c.update(d) delim=c.most_common(1)[0][0] cols = [i for i,d in enumerate(counts) if delim in d] # filter the col indexes to keep only the rightmost index of # each contiguous sequence width_list =[] lidx=0 prev = cols[0] for v in cols[1:]: if v-prev != 1: width_list.append(prev+1-lidx) lidx=prev+1 prev = v width_list.append(len(rows[0])-lidx) return width_list ``` any takers to flesh this out into a PR with tests?
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4,489
TST: test for breaking test in pytables on travis
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2013-08-07T00:42:40Z
2014-06-18T10:03:26Z
2013-08-07T00:42:51Z
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TST: reverse hdf_fix
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2013-08-07T01:04:42Z
2014-07-16T08:22:05Z
2013-08-07T01:17:10Z
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MDU6SXNzdWUxNzcyNTE0MA==
4,491
Managing pandas's hash table footprint
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4
2013-08-07T01:05:29Z
2016-09-29T22:44:20Z
2016-09-29T22:35:08Z
MEMBER
null
Many operations in pandas trigger the population of a hash table from the underlying index values. This can be very costly in memory usage, especially for very long Series / DataFrames. Example: ``` s = pd.Series(np.random.randn(10000000)) s[100] ``` Note that after indexing into `s`, the first time this happens a hash table `s.index._engine` is populated. So something larger than 80MB of space probably gets used up by this. We can have a whole discussion about the hash table infra and how it could be better than it is now (I may spend some time on this myself soon). For the time being, one solution would be to have a weakref dictionary someplace (or something of that nature) that enables us to globally destroy all hash tables. Separately, the hash table scheme can be modified to have much smaller memory footprint than it does now -- keys can be integers stored as `uint32_t` resulting in roughly 4 bytes per index value. Right now there are two arrays: one for hash keys, another for hash values. motivated by http://stackoverflow.com/questions/18070520/pandas-memory-usage-when-reindexing
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BUG: fix plotting of dup indexed objects
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2
2013-08-07T02:12:03Z
2014-06-23T05:29:07Z
2013-08-07T17:22:27Z
MEMBER
null
closes #4486.
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17,727,149
MDU6SXNzdWUxNzcyNzE0OQ==
4,493
KeyError when generating scatter plot of DataFrame columns
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34
2013-08-07T02:32:17Z
2016-10-12T23:04:04Z
2014-02-27T17:48:47Z
NONE
null
I have two columns of a `DataFrame` that I am trying to plot using `scatter` in Matplotlib. Here are the two series: ``` mmsi 210234000 52.600000 211109000 62.250000 211200350 48.333333 211201390 52.887500 211203370 41.962500 211204500 32.233333 211205780 87.050000 211205790 42.500000 211207740 38.233333 211220290 53.233333 211262460 40.100000 211262630 31.716667 211327410 54.300000 211335760 43.175000 211378120 58.361290 ... 636090986 62.812500 636091049 70.175000 636091052 45.600000 636091078 82.725000 636091137 59.075000 636091138 61.083333 636091195 75.030000 636091278 68.241667 636091394 57.700000 636091452 44.275000 636091501 63.480000 636091506 67.578571 636091545 44.500000 636091595 46.475000 636091800 61.850000 Name: pdgt10_early, Length: 235, dtype: float64 mmsi 210234000 True 211109000 True 211200350 True 211201390 True 211203370 True 211204500 True 211205780 True 211205790 True 211207740 True 211220290 True 211262460 True 211262630 True 211327410 True 211335760 True 211378120 True ... 636090986 True 636091049 True 636091052 True 636091078 True 636091137 True 636091138 True 636091195 True 636091278 True 636091394 True 636091452 True 636091501 True 636091506 True 636091545 True 636091595 True 636091800 True Length: 235, dtype: bool ``` (Actually the latter is the result of a comparison operation on the column to see if values are greater or less than some threshold value) Unfortunately, calling `scatter` on these two axes raises a `KeyError: 0` exception for some reason. ``` --------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-52-4b02ddd54f50> in <module>() 1 for sma in sma_list: ----> 2 plot_logistic('Cargo', sma, nova_traces[np.where(sma_list==sma)[0][0]].traces[1], season_a=-3) <ipython-input-51-94c541930a55> in plot_logistic(ship, sma, trace, dataset, season_a, season_b, nlines) 14 print x 15 print y ---> 16 scatter(x, y) 17 title('{0} in {1}'.format(ship, sma)) 18 /Library/Python/2.7/site-packages/matplotlib-1.4.x-py2.7-macosx-10.8-intel.egg/matplotlib/pyplot.pyc in scatter(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, hold, **kwargs) 3088 ret = ax.scatter(x, y, s=s, c=c, marker=marker, cmap=cmap, norm=norm, 3089 vmin=vmin, vmax=vmax, alpha=alpha, -> 3090 linewidths=linewidths, verts=verts, **kwargs) 3091 draw_if_interactive() 3092 finally: /Library/Python/2.7/site-packages/matplotlib-1.4.x-py2.7-macosx-10.8-intel.egg/matplotlib/axes/_axes.pyc in scatter(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, **kwargs) 3210 self.cla() 3211 -> 3212 self._process_unit_info(xdata=x, ydata=y, kwargs=kwargs) 3213 x = self.convert_xunits(x) 3214 y = self.convert_yunits(y) /Library/Python/2.7/site-packages/matplotlib-1.4.x-py2.7-macosx-10.8-intel.egg/matplotlib/axes/_base.pyc in _process_unit_info(self, xdata, ydata, kwargs) 1652 # we only need to update if there is nothing set yet. 1653 if not self.xaxis.have_units(): -> 1654 self.xaxis.update_units(xdata) 1655 #print '\tset from xdata', self.xaxis.units 1656 /Library/Python/2.7/site-packages/matplotlib-1.4.x-py2.7-macosx-10.8-intel.egg/matplotlib/axis.pyc in update_units(self, data) 1330 """ 1331 -> 1332 converter = munits.registry.get_converter(data) 1333 if converter is None: 1334 return False /Library/Python/2.7/site-packages/matplotlib-1.4.x-py2.7-macosx-10.8-intel.egg/matplotlib/units.pyc in get_converter(self, x) 135 136 if isinstance(x, np.ndarray) and x.size: --> 137 converter = self.get_converter(x.ravel()[0]) 138 return converter 139 /Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/core/series.pyc in __getitem__(self, key) 617 def __getitem__(self, key): 618 try: --> 619 return self.index.get_value(self, key) 620 except InvalidIndexError: 621 pass /Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/core/index.pyc in get_value(self, series, key) 722 """ 723 try: --> 724 return self._engine.get_value(series, key) 725 except KeyError as e1: 726 if len(self) > 0 and self.inferred_type == 'integer': /Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/index.so in pandas.index.IndexEngine.get_value (pandas/index.c:2646)() /Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/index.so in pandas.index.IndexEngine.get_value (pandas/index.c:2461)() /Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/index.so in pandas.index.IndexEngine.get_loc (pandas/index.c:3198)() /Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/hashtable.so in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:6422)() /Library/Python/2.7/site-packages/pandas-0.12.0_80_gfcaf9a6_20130729-py2.7-macosx-10.8-intel.egg/pandas/hashtable.so in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:6366)() KeyError: 0 ``` This worked like a charm prior to updating pandas and matplotlib a week or so ago. Running Python 2.7.2 on OS X 10.8.4.
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MDExOlB1bGxSZXF1ZXN0NzQzNTE3NA==
4,494
FIX: resample to single group with custom function
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4
2013-08-07T02:46:53Z
2014-06-13T23:41:52Z
2013-09-25T12:31:36Z
CONTRIBUTOR
null
Using master code, when re-sampling to a frequency that would result in a single group the use of custom aggregation functions do not work as expected. The included changes fix this, and should fix #3849 too. ``` python In [17]: import pandas as pd In [18]: import numpy as np In [19]: mysum = lambda x: x.sum() In [20]: rng = pd.date_range('2000-1-1', '2000-1-10', freq='D') In [21]: ts = pd.Series(np.random.randn(len(rng)), index=rng) In [22]: ts.resample('M', how='sum') Out[22]: 2000-01-31 0.985236 Freq: M, dtype: float64 In [23]: ts.resample('M', how=mysum) Out[23]: 2000-01-31 0 Freq: M, dtype: float64 ```
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BLD/TST: correct pytz version for tox
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8
2013-08-07T03:11:53Z
2014-07-16T08:22:10Z
2013-08-07T14:51:16Z
CONTRIBUTOR
null
Tox fails to build pandas without a specific version of pytz.
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4,496
Bug: tslib.tz_convert(vals, tz1, tz2) may raise an IndexError exception.
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1
2013-08-07T11:25:00Z
2013-08-23T13:26:54Z
2013-08-23T13:26:54Z
CONTRIBUTOR
null
The following code produces "Out of bounds on buffer access (axis 0)" exception when accessing idx.hour: --- import pandas as pd idx = pd.date_range('26/3/2011', '27/3/2011', freq='1min') idx = idx.tz_localize('UTC') idx = idx.tz_convert('Europe/Moscow') hour = idx.hour --- Traceback (most recent call last): File "test.py", line 7, in <module> hour = idx.hour File "C:\Python27\lib\site-packages\pandas\tseries\index.py", line 41, in f values = self._local_timestamps() File "C:\Python27\lib\site-packages\pandas\tseries\index.py", line 397, in _local_timestamps return tslib.tz_convert(self.asi8, utc, self.tz) File "tslib.pyx", line 1356, in pandas.tslib.tz_convert (pandas\tslib.c:21388) IndexError: Out of bounds on buffer access (axis 0) My research indicated there was a bug in the following piece of code in tslib.tz_convert(vals, tz1, tz2) in Pandas 0.12. There is no check that "(pos + 1) < len(trans)" when referencing trans[pos + 1] inside the for loop: ``` offset = deltas[pos] for i in range(n): v = utc_dates[i] if v >= trans[pos + 1]: pos += 1 offset = deltas[pos] result[i] = v + offset ``` As a result in some circumstances this code raises "Out of bounds on buffer access (axis 0)" exception. For example, in Russia last day-light saving transition took place in somewhere in 26/3/2011-27/3/2011, and there were no transitions since then. If ts_lib.tz_convert() is asked to convert time zones for interval that includes 26/3/2011-27/3/2011, it raises an exception while trying to access trans[pos + 1]. I have a working patch that solves this. Going to contribute it soon.
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Fix issue #4496: tslib.tz_convert(vals, tz1, tz2) may raise an IndexErro...
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7
2013-08-07T11:32:59Z
2014-07-16T08:22:12Z
2013-08-23T13:26:18Z
CONTRIBUTOR
null
Bug-fix for Issue #4496 Bug: tslib.tz_convert(vals, tz1, tz2) may raise an IndexError exception.
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17,745,685
MDExOlB1bGxSZXF1ZXN0NzQ0NDMxMg==
4,498
JSON native support for datetime encoding
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21
2013-08-07T12:43:08Z
2014-06-14T10:38:30Z
2013-08-15T01:14:12Z
CONTRIBUTOR
null
This adds support at the C level for encoding datetime values in JSON. I added a new parameter `date_unit` which controls the precision of the encoding, options are seconds, milliseconds, microseconds and nanoseconds. It defaults to milliseconds which should fix #4362. I also added support for NaTs and tried to detect when numpy arrays of datetime64 longs are passed. Note that datetime decoding is still handled at the Python level but I added a date_unit param here too so timestamps can be converted correctly. valgrind looks happy with the changes and all tests pass ok for me on Python 2.7 & 3.3
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DOC: fix conflicting ewma documentation
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9
2013-08-07T13:10:33Z
2014-07-02T21:24:28Z
2013-08-22T10:16:05Z
CONTRIBUTOR
null
The information in the docs, in the parameter description and in the body of the docstring is conflicting. This should hopefully make them consistent amongst themselves (and with the code).
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Docs for lurking groupby methods
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2013-08-07T14:23:42Z
2014-04-23T22:02:39Z
2014-04-23T21:36:55Z
MEMBER
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see [this SO question](http://stackoverflow.com/questions/18104704/counting-unique-values-in-a-pandas-grouped-object) related: http://stackoverflow.com/questions/21763660/api-ref-missing-dataframegroupby-objects methods like `nunique` should be documented; i bet there are more like this that you can see in tab completion but that are valid method calls.
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https://api.github.com/repos/pandas-dev/pandas/issues/4500/timeline
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