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BUG: datetime with datetime ops very slow
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0
2013-12-30T21:53:13Z
2013-12-30T22:54:10Z
2013-12-30T22:54:10Z
CONTRIBUTOR
null
``` In [1]: df = pd.DataFrame({ 'date1' : pd.Timestamp('20130101'), 'date2' : pd.date_range('20120101',periods=1000000,freq='s') }) In [2]: %timeit df['date1']-df['date2'] 1 loops, best of 3: 2.17 s per loop ``` pretty easy fix; don't need to infer if its a series (as we know its dtype already) should be more like this ``` In [3]: %timeit Series(df['date1'].astype('int64')-df['date2'].astype('int64'),dtype='m8[ns]') 10 loops, best of 3: 43 ms per loop ```
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PERF: fix infer_dtype to properly infer a Series (GH5801)
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0
2013-12-30T22:20:36Z
2014-06-15T19:45:24Z
2013-12-30T22:54:10Z
CONTRIBUTOR
null
closes #5801 ``` ------------------------------------------------------------------------------ Test name | head[ms] | base[ms] | ratio | ------------------------------------------------------------------------------- timestamp_ops_diff2 | 21.7124 | 2472.3583 | 0.0088 | ------------------------------------------------------------------------------- Test name | head[ms] | base[ms] | ratio | ------------------------------------------------------------------------------- Ratio < 1.0 means the target commit is faster then the baseline. Seed used: 1234 ``` really though this was in their before....oh well
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BLD: version strings should be updated only when tagging new release
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2013-12-31T00:37:30Z
2014-06-15T18:45:46Z
2013-12-31T00:37:43Z
NONE
null
0.13.0 was the first time I tagged a release and I got it wrong. Version strings are bumped only when tagging a new release. ``` % grh v0.13.0rc1^ % sudo python ./setup.py develop % cat pandas/version.py version = '0.12.0-1189-gd9b3340' short_version = '0.12.0' ``` It makes sense since it leaves the next version undetermined until it happens. Keeping the status-quo for now, until we agree on something better.
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5,804
DOC: add a section on growing pandas ecosystem
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4
2013-12-31T01:34:26Z
2014-01-03T02:58:01Z
2014-01-03T02:58:01Z
NONE
null
Incresingly, python packages are being built on top of pandas. Often, these solve problems pandas users need addressed but which are a stretch for pandas' scope proper. #2302, and [pandas-xlsxwriter-charts](http://pandas-xlsxwriter-charts.readthedocs.org/en/latest/chart_examples.html#scatter-chart), for example. vincent, yhat/ggplot, seaborn, etc'. statsmodels too ofcourse, though it should be fairly well known. Let's give them some exposure, encourage people to do their own thing on top of pandas and help users locate existing solutions. Any more mature(-ish) packages which deserve a mention?
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update to allow for replace using timestamps with a test. fixed issue 5797
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2013-12-31T02:07:36Z
2013-12-31T14:27:36Z
2013-12-31T14:27:36Z
CONTRIBUTOR
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BUG: Series replace values using timestamps in a dict GH5797
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11
2013-12-31T14:29:00Z
2014-06-16T14:08:41Z
2014-01-02T22:58:45Z
CONTRIBUTOR
null
This fixes issue #5797.
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24,922,617
MDU6SXNzdWUyNDkyMjYxNw==
5,807
Yahoo URL has changed...
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1
2013-12-31T15:23:49Z
2013-12-31T15:39:13Z
2013-12-31T15:38:35Z
NONE
null
I believe the url for gathering data from yahoo has changed... If i create a new DataReader... ticker_data = DataReader("SPY", "yahoo") I get.. IOError: after 3 tries, Yahoo! did not return a 200 for url 'http://ichart.yahoo.com/table.csv?s=SPY&a=0&b=1&c=2010&d=11&e=31&f=2013&g=d&ignore=.csv' I believe .finance needs to be added to the URL. (Ex) 'http://ichart.finance.yahoo.com/table.csv?s=SPY&a=0&b=1&c=2010&d=11&e=31&f=2013&g=d&ignore=.csv'
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24,922,800
MDU6SXNzdWUyNDkyMjgwMA==
5,808
BUG: boolean op with empty frames should not raise
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0
2013-12-31T15:31:03Z
2014-01-04T22:30:39Z
2014-01-04T22:30:39Z
CONTRIBUTOR
null
``` In [4]: DataFrame(index=[1]) & DataFrame(index=[1]) TypeError: ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'' ``` should just return an empty frame (also raises if one side is non-empty)
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24,922,994
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5,809
Fixing ichart URL for yahoo finance historical data.
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1
2013-12-31T15:38:05Z
2014-06-14T03:32:38Z
2014-01-01T18:53:49Z
NONE
null
The URL needs to be changed, as the old way no longer works.
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24,923,193
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5,810
BUG: fix issue of boolean comparison on empty DataFrames (GH5808)
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11
2013-12-31T15:46:31Z
2014-06-18T06:21:16Z
2014-01-04T22:30:39Z
CONTRIBUTOR
null
closes #5808
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24,925,705
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5,811
Update yahoo url in data.py
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2013-12-31T17:09:22Z
2014-07-27T06:23:03Z
2013-12-31T17:12:23Z
NONE
null
ichart.yahoo.com doesn't seem to be available, switched to ichart.finance.yahoo.com
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5,812
BUG: Yahoo finance changed chart base url. Updated _get_hist_yahoo
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25
2013-12-31T21:50:44Z
2014-06-12T19:28:36Z
2014-01-01T18:52:09Z
CONTRIBUTOR
null
The start of the old url was: `http://ichart.yahoo.com/` and yahoo now uses `http://ichart.finance.yahoo.com/`
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24,933,328
MDU6SXNzdWUyNDkzMzMyOA==
5,813
please add quick documentation on pandas timestamp values of month, day of week
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8
2013-12-31T22:10:15Z
2014-02-20T21:36:56Z
2014-02-20T21:36:56Z
NONE
null
Pandas appears to use values for timestamp fields where 1 = January, 2 = February, etc., (vs. 0 = January) and 0 = Monday, 1 = Tuesday, 2 = Wednesday, 3 = Thursday, 4 = Friday, 5 = Saturday, 6 = Sunday. (vs. weekday numbering that starts with 1 instead of 0, or Sunday instead of Monday.) ``` dec31 = pd.Timestamp('2013-12-30 00:00:00') dec31.weekday() ``` prints 0 in my system (Mac OSX + pandas 0.12.0) This convention doesn't appear to be in the documentation + it would really help as it is one of several possibilities.
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5,814
DOC: add way to document DatetimeIndex field attributes
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6
2014-01-01T17:11:07Z
2014-06-29T12:30:55Z
2014-01-02T20:17:17Z
MEMBER
null
Ping @rockg. The docstring is now listed in the autosummary table in api.rst. Related to issue #5813 (but does not closes it, this only documents the DatetimeIndex field in the api.rst, not more general the Timestamp values). I added the attribute docstring within the `_field_accessor` function. Is this a good approach? You can also document attributes with a docstring line beneath the definition (see Sphinx docs: http://sphinx-doc.org/ext/autodoc.html#directive-autoattribute). The problem with this is that, for the moment, this works for the sphinx autodoc (so the generated pages), but not for the autosummary (there is an open PR for this: https://bitbucket.org/birkenfeld/sphinx/pull-request/142/make-autosummary-work-with-module-class/diff).
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PERF: perf issue with dropna on frame
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16
2014-01-01T18:50:03Z
2014-07-16T08:44:54Z
2014-01-01T19:13:26Z
CONTRIBUTOR
null
took out the apply on `count` and just compute directly ``` ------------------------------------------------------------------------------- Test name | head[ms] | base[ms] | ratio | ------------------------------------------------------------------------------- frame_dropna_axis1_any | 147.5154 | 334.2137 | 0.4414 | frame_dropna_axis1_all | 251.1443 | 437.9021 | 0.5735 | frame_dropna_axis0_all | 80.6900 | 80.8613 | 0.9979 | frame_dropna_axis0_any | 58.6040 | 54.6887 | 1.0716 | ------------------------------------------------------------------------------- Test name | head[ms] | base[ms] | ratio | ------------------------------------------------------------------------------- Ratio < 1.0 means the target commit is faster then the baseline. Seed used: 1234 Target [c6e300d] : PERF: perf issue with dropna on frame Base [5e176a9] : Merge pull request #5738 from y-p/PR_json_pr_ver BLD: ci/print_versions.py learned to output json ```
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24,944,175
MDU6SXNzdWUyNDk0NDE3NQ==
5,816
@network decorator can mask failed tests
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15
2014-01-01T18:55:48Z
2014-01-28T03:13:56Z
2014-01-28T03:13:56Z
NONE
null
See discussion https://github.com/pydata/pandas/pull/5812 The skip behaviour was motivated by travis failing tests due to wonky net cinnectivity which can mask actual problems such as broken urls. Perhaps make the behavior conditional on environment (travis can be detected via environment vars)
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5,817
CLN: Make io/data urls easier to monkey-patch
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3
2014-01-02T00:46:13Z
2014-07-16T08:44:56Z
2014-01-03T00:06:25Z
CONTRIBUTOR
null
This could be useful to make pandas more resilient to basic url changes like that which happened with yahoo finance. That said, clearly wholesale API changes won't be helped by this. What do you all think, worth it to make this relatively trivial change?
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24,953,057
MDU6SXNzdWUyNDk1MzA1Nw==
5,818
DatetimeIndex columns cause reset_index() to throw AttributeError
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1
2014-01-02T06:38:36Z
2014-01-24T22:20:51Z
2014-01-24T22:20:51Z
NONE
null
It appears that if a dataframe has column headers of type DatetimeIndex, calling reset_index() throws `AttributeError: 'str' object has no attribute 'view'`. I see this both in v0.12 and the master branch. If column headers are strings or integers, reset_index() works fine. I'm guessing it's treating the new column array as type DatetimeIndex whereas it now has a string in the 0th position ('index'). Maybe the reset_index should first cast the columns as object and convert the DatetimeIndex values to strings? ``` python In [48]: df = pd.DataFrame(data=np.random.rand(2, 2), columns=pd.date_range('1/1/2013', '1/2/2013'), index=['A', 'B']) In [49]: df.reset_index() --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-49-6983677cc901> in <module>() ----> 1 df.reset_index() /home/user/environments/python3/src/pandas/pandas/core/frame.py in reset_index(self, level, drop, inplace, col_level, col_fill) 2447 else: 2448 values = _maybe_cast(self.index.values) -> 2449 new_obj.insert(0, name, values) 2450 2451 new_obj.index = new_index /home/user/environments/python3/src/pandas/pandas/core/frame.py in insert(self, loc, column, value, allow_duplicates) 1940 value = self._sanitize_column(column, value) 1941 self._data.insert( -> 1942 loc, column, value, allow_duplicates=allow_duplicates) 1943 1944 def _sanitize_column(self, key, value): /home/user/environments/python3/src/pandas/pandas/core/internals.py in insert(self, loc, item, value, allow_duplicates) 2899 2900 try: -> 2901 new_items = self.items.insert(loc, item) 2902 self.set_items_norename(new_items) 2903 /home/user/environments/python3/src/pandas/pandas/tseries/index.py in insert(self, loc, item) 1539 1540 new_index = np.concatenate((self[:loc].asi8, -> 1541 [item.view(np.int64)], 1542 self[loc:].asi8)) 1543 return DatetimeIndex(new_index, freq='infer') AttributeError: 'str' object has no attribute 'view' In [50]: df = pd.DataFrame(data=np.random.rand(2, 2), columns=[1, 2], index=['A', 'B']) In [51]: df.reset_index() Out[51]: index 1 2 0 A 0.947575 0.370406 1 B 0.664856 0.686524 [2 rows x 3 columns] In [52]: df = pd.DataFrame(data=np.random.rand(2, 2), columns=['C', 'D'], index=['A', 'B']) In [53]: df.reset_index() Out[53]: index C D 0 A 0.053455 0.599483 1 B 0.776364 0.680425 [2 rows x 3 columns] ```
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https://api.github.com/repos/pandas-dev/pandas/issues/5819
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24,953,572
MDExOlB1bGxSZXF1ZXN0MTExNzUxMjM=
5,819
BUG: Fix DatetimeIndex.insert() with strings.
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8
2014-01-02T07:14:00Z
2014-07-09T11:59:16Z
2014-01-24T22:20:52Z
CONTRIBUTOR
null
Falls back to object Index instead. (previously wasn't checking for them), but _only_ strings are allowed. Fixes #5818.
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5,820
BUG: dropna dtype comp issue related (GH5815)
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0
2014-01-02T11:54:16Z
2014-06-19T08:09:48Z
2014-01-02T12:09:55Z
CONTRIBUTOR
null
fixes dtype issue on 32-bit, related #5815
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5,821
KeyError on slicing with datetime or pandas.Timestamp
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12
2014-01-02T12:40:54Z
2017-01-12T21:28:55Z
2014-04-09T13:00:23Z
CONTRIBUTOR
null
related #6066 (on Float64Index too) The 'KeyError' on slicing was discussed multiple times but I'm still not sure if the issue below is a bug or just my misunderstanding. I'm experiencing 'KeyError' from time to time when I try to slice my dataframes with datetime or Timestamp objects, however slicing with strings works perfectly. I was unable to construct synthetic example with pandas.date_range, so I needed to upload the piece of real data where the issue appears: https://www.dropbox.com/s/ibzbwqs35tiydyc/tmp.h5 ``` df = pd.read_hdf('tmp.h5', 'data') print df.head() ``` When I try to slice it with the pandas.Timestamp objects it results in 'KeyError': ``` tt1 = pd.Timestamp('2006-11-16 18:30:00') tt2 = pd.Timestamp('2006-11-16 18:40:00') print df.ix[tt1:tt2].head() ``` Same for datetime objects: ``` tt1 = pd.Timestamp('2006-11-16 18:30:00').to_datetime() tt2 = pd.Timestamp('2006-11-16 18:40:00').to_datetime() print df.ix[tt1:tt2].head() ``` However this slicing works perfectly: ``` tt1 = pd.Timestamp('2006-11-16 18:30:00').strftime('%Y-%m-%d %H:%M:%S') tt2 = pd.Timestamp('2006-11-16 18:40:00').strftime('%Y-%m-%d %H:%M:%S') print df[tt1:tt2].head() ``` Numpy version 1.8.0 Pandas version 0.13.0
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5,822
pandas series 'astype' conversion different from numpy.array 'astype' conversion
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2
2014-01-02T16:16:14Z
2014-02-14T13:09:40Z
2014-02-14T13:09:40Z
NONE
null
I am confused by the dtype conversion function "astype" for pandas series. According to documentation, it should be the same as numpy.ndarray dtype conversion, but seems there is stil some difference. For example, if I try to convert columns of datetime type to object dtype, I got completely different results using astype function on pandas series versus on numpy array. My question is what sort of additional hanlding exists inside pandas Series dtype conversion? Example code as follows, note that pandas Series dtype conversion converts numpy.datetime64 to datetime.datetime, while numpy.array dtype conversion converts numpy.datetime64 to long. By the way, my numpy is version 1.8.0, and my pandas is version 0.12.0, and running on python 2.7. > > > import datetime > > > import numpy > > > import pandas > > > dd = pandas.DataFrame({"val": [datetime.datetime(2014, 1, 1, 9)]}) > > > dd["val"].astype("object") > > > Out[1]: > > > 0 2014-01-01 09:00:00 > > > Name: val, dtype: object > > > numpy.array(dd["val"]).astype("object") > > > Out[1]: array([1388566800000000000L], dtype=object)
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5,823
ENH: support a simultaneous mutli-index on both index AND columns in a Frame with HDFStore
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0
2014-01-02T17:52:10Z
2018-07-08T14:44:22Z
null
CONTRIBUTOR
null
http://stackoverflow.com/questions/20888226/pandas-hdfstore-tables-doesnt-accept-multiindex-columns/20888732#20888732
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5,824
Numpy 1.8 `DeprecationWarning` in compat/scipy.py
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20
2014-01-02T18:30:31Z
2014-04-16T13:04:41Z
2014-04-16T13:04:41Z
CONTRIBUTOR
null
Not sure how pressing this is, but with `DeprecationWarning` enabled, I notice that numpy 1.8 is raising a warning during the following call to `describe()`. [side note: enabled DeprecationWarning in my test suite after learning that it was changed in py2.7 to "ignore" by default.] ``` import pandas as pd import warnings warnings.simplefilter("once", DeprecationWarning) df = pd.DataFrame({"A": [1, 2, 3], "B": [1.2, 4.2, 5.2]}) print df.groupby('A')['B'].describe() ``` stdout: ``` $ python test_fail.py .../pandas/compat/scipy.py:68: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future score = values[idx] ``` Here's the full traceback with DeprecationWarning escalated to an error (`warnings.simplefilter("error", DeprecationWarning)`): ``` Traceback (most recent call last): File "test_fail.py", line 6, in <module> print df.groupby('A')['B'].describe() File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0_29_g97860a1-py2.7-linux-i686.egg/pandas/core/groupby.py", line 343, in wrapper return self.apply(curried) File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0_29_g97860a1-py2.7-linux-i686.egg/pandas/core/groupby.py", line 424, in apply return self._python_apply_general(f) File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0_29_g97860a1-py2.7-linux-i686.egg/pandas/core/groupby.py", line 427, in _python_apply_general keys, values, mutated = self.grouper.apply(f, self.obj, self.axis) File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0_29_g97860a1-py2.7-linux-i686.egg/pandas/core/groupby.py", line 883, in apply res = f(group) File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0_29_g97860a1-py2.7-linux-i686.egg/pandas/core/groupby.py", line 422, in f return func(g, *args, **kwargs) File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0_29_g97860a1-py2.7-linux-i686.egg/pandas/core/groupby.py", line 329, in curried return f(x, *args, **kwargs) File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0_29_g97860a1-py2.7-linux-i686.egg/pandas/core/series.py", line 1386, in describe lb), self.median(), self.quantile(ub), File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0_29_g97860a1-py2.7-linux-i686.egg/pandas/core/series.py", line 1316, in quantile result = _quantile(valid_values, q * 100) File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0_29_g97860a1-py2.7-linux-i686.egg/pandas/compat/scipy.py", line 68, in scoreatpercentile score = values[idx] IndexError: cannot convert index to integer ```
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5,825
ENH: Improved (JMP-like) Box Plot Hierarchy Labels
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4
2014-01-02T20:10:01Z
2014-01-23T17:03:14Z
2014-01-23T17:03:14Z
NONE
null
For a lot of work in statistics, box plots are still one of the most useful tools for at-a-glance comparison of experimental groups. JMP has a great default handler for plotting box plots when including multiple grouping levels for the data, including sane handling for missing data. Please see this example for what a simple case looks like: ![hierarchy_boxplot_jmp-style](https://f.cloud.github.com/assets/52644/1835036/9cf7909e-73e8-11e3-8e63-a9fe3405bc54.png) In terms of 'sane defaults' and making a lot of use without a lot of work, I think this would be a great model for future box plot work for pandas given its rich GroupBy functionality. When creating a box plot based on a multi-level grouping in pandas/matplotlib, the only obvious method I can see is to use labels which are tuples of each of the values for the various group levels. This makes it hard to get much out of the plot visually for grouped data set. JMP has a variety of other features for box plots that would be very useful in the future, but I believe this first visual enhancement would be more powerful for the most users.
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24,989,875
MDExOlB1bGxSZXF1ZXN0MTExOTM2MTI=
5,826
DOC: Add example to extract docstring, and re-explain change to match.
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7
2014-01-02T21:57:20Z
2014-07-16T08:44:59Z
2014-01-03T15:24:28Z
CONTRIBUTOR
null
@jreback, I added examples per your request in #5099. Also, I rewrote the explanation of how `str.match` is changing, which I left confusing and wordy before.
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24,990,966
MDU6SXNzdWUyNDk5MDk2Ng==
5,827
setup.py breaks builds
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4
2014-01-02T22:19:58Z
2014-01-04T16:25:18Z
2014-01-04T16:25:18Z
CONTRIBUTOR
null
Hello, debian builder is broken after latest edits of the setup.py https://github.com/pydata/pandas/commits/master/setup.py error ``` raceback (most recent call last): File "setup.py", line 213, in <module> if pipe.returncode != 0: NameError: name 'pipe' is not defined dh_auto_clean: python setup.py clean -a returned exit code 1 ```
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5,828
DOC: add pandas-xlsxwriter-charts ipnb to cookbook
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0
2014-01-03T02:07:56Z
2014-07-16T08:45:01Z
2014-01-03T02:58:27Z
NONE
null
cc @jmcnamara.
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5,829
DOC: add 'pandas ecosystem' section to docs
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3
2014-01-03T02:57:56Z
2014-07-14T12:38:07Z
2014-01-03T02:58:01Z
NONE
null
cc @jseabold, @glamp, @mwaskom, @kjordahl, @wrobstory Feel free to suggest a better blurb if you'd like. Replaces the `related libraries` section, which I feel is outdated. closes #5804
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test_partially_invalid_plot_data: AssertionError: TypeError not raised (python3.3, ubuntu 13.04 amd64)
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4
2014-01-03T03:30:22Z
2014-01-28T16:50:11Z
2014-01-27T00:59:19Z
CONTRIBUTOR
null
``` ====================================================================== FAIL: test_partially_invalid_plot_data (pandas.tests.test_graphics.TestDataFramePlots) ---------------------------------------------------------------------- Traceback (most recent call last): File "/tmp/buildd/pandas-0.13.0/debian/tmp/usr/lib/python3/dist-packages/pandas/tests/test_graphics.py", line 939, in test_partially_invalid_plot_data df.plot(kind=kind) File "/tmp/buildd/pandas-0.13.0/debian/tmp/usr/lib/python3/dist-packages/pandas/util/testing.py", line 1331, in __exit__ raise AssertionError("{0} not raised.".format(name)) AssertionError: TypeError not raised. ---------------------------------------------------------------------- Ran 4317 tests in 380.425s FAILED (SKIP=325, failures=1) ['Idx'] ``` seems to be just a sporadic failure (did test ok before in the same environment, ok on i386 in this round of builds), full log http://neuro.debian.net/_files/_buildlogs/pandas/0.13.0/pandas_0.13.0-1~nd13.04+1_amd64.build
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python3.2 difficulty to use pre-generated pandas/msgpack.cpp
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4
2014-01-03T04:23:58Z
2014-01-15T21:16:50Z
2014-01-10T12:26:16Z
CONTRIBUTOR
null
on all aged Debian/Ubuntus (e.g. wheeze) where I build using pre-cythonized sources python3.2 seems to become blind and tries only to look for msgpack.c, not msgpack.cpp: ``` ~/pandas-0.13.0# python3.2 setup.py build_ext running build_ext Traceback (most recent call last): File "setup.py", line 583, in <module> **setuptools_kwargs) File "/usr/lib/python3.2/distutils/core.py", line 148, in setup dist.run_commands() File "/usr/lib/python3.2/distutils/dist.py", line 917, in run_commands self.run_command(cmd) File "/usr/lib/python3.2/distutils/dist.py", line 936, in run_command cmd_obj.run() File "/usr/lib/python3.2/distutils/command/build_ext.py", line 344, in run self.build_extensions() File "setup.py", line 346, in build_extensions self.check_cython_extensions(self.extensions) File "setup.py", line 343, in check_cython_extensions """ % src) Exception: Cython-generated file 'pandas/msgpack.c' not found. Cython is required to compile pandas from a development branch. Please install Cython or download a release package of pandas. ~/pandas-0.13.0# ls -l pandas/msgpack.cpp -rw-r--r-- 1 pbuilder pbuilder 570047 Jan 3 03:44 pandas/msgpack.cpp ``` I wonder if anyone ran into this misbehavior (before I start patching left and right ;-) )
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ENH: axis keyword for groupby apply?
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10
2014-01-03T13:35:07Z
2017-08-10T23:17:37Z
2015-10-20T22:25:32Z
MEMBER
null
Inspired by this [SO question](http://stackoverflow.com/questions/20875140/apply-function-to-sets-of-columns-in-pandas-looping-over-entire-data-frame-co), I was wondering if the possibility of being able to give an axis keyword to `GroupBy.apply` would be interesting. An example. Considering this dataframe: ``` In [1]: idx = pd.MultiIndex.from_arrays([list('AAABBB'), list('xyzxyz')]) In [2]: df = pd.DataFrame(np.random.randn(5,6), columns=idx) In [3]: df Out[3]: A B x y z x y z 0 1.085834 -1.247589 0.174311 1.415633 0.788986 0.443246 1 1.036490 0.223306 -0.920102 0.068396 1.577690 -0.757387 2 -0.587843 -1.223218 -0.153409 1.863440 2.169254 0.282485 3 1.485550 0.494427 -0.257822 1.360076 -0.049386 -0.264166 4 0.232604 -0.972644 0.877186 -0.875215 1.198826 -0.370832 ``` and then groupby the first level of the columns (`A` and `B`): ``` In [4]: g = df.groupby(axis=1, level=0) ``` On the individual groups of `g`, you could do a following apply (to calculate the length of the vector (x,y,z)): ``` In [5]: df['A'].apply(lambda x: np.sqrt(x**2).sum(), axis=1) Out[5]: 0 2.507734 1 2.179897 2 1.964471 3 2.237799 4 2.082434 dtype: float64 ``` However, to do this on all groups, you need a second apply inside the apply to provide the axis keyword: ``` In [6]: g.apply(lambda g: g.apply(lambda x: np.sqrt(x**2).sum(), axis=1)) Out[6]: A B 0 2.507734 2.647866 1 2.179897 2.403474 2 1.964471 4.315179 3 2.237799 1.673628 4 2.082434 2.444873 ``` Would it be a interesting enhancement that you could directly provide this keyword like this? ``` g.apply(lambda x: np.sqrt(x**2).sum(), axis=1) ``` Or Is there another way I am missing to simply do this?
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DOC: change doc refs to 0.13.1
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2014-01-03T14:26:34Z
2014-07-16T08:45:06Z
2014-01-03T18:18:44Z
CONTRIBUTOR
null
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Joining DataFrames (or Series) and Series.
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5
2014-01-03T14:38:04Z
2014-02-18T20:24:11Z
2014-02-18T20:24:11Z
CONTRIBUTOR
null
``` pd.version.version '0.12.0-1149-g141e93a' ``` Maybe already fixed, but I'm afraid to upgrade in the middle of working on a project given past experiences. As I recall there were several ways proposed to join a series to a DataFrame with join being the preferred way. ``` df = pd.DataFrame(np.random.random((30,2)), columns=["A","B"]) series = pd.Series(np.random.random(30), name="C") ``` I know I can do ``` df["C"] = series ``` or ``` df = df.join(series) ``` But it'd be nice if the following also worked given that I'd like to write general code. ``` pd.concat((df, series), axis=1) # ok error message at least df.merge(series) # just call join under the hood? bad error message ``` This is "unexpected" that it somehow tries to append the rows. Looks like a bug to me. Should it take an axis keyword? ``` df.append(series) ```
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BUG: duplicate selection with missing values raises
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0
2014-01-03T16:47:19Z
2014-01-04T18:53:09Z
2014-01-04T18:53:09Z
CONTRIBUTOR
null
Hard to reprod. Select from a duplicate indexed axis in a frame with ix where part of the selection set is missing ``` In [22]: df = DataFrame(np.random.randn(5,5),columns=['A.1','B.1','B.2','B.3','A.2'],index=date_range('20130101',periods=5)) In [23]: df2 = df.rename(columns=lambda x: x.split('.')[0]) In [24]: df2 Out[24]: A B B B A 2013-01-01 -1.029245 -0.782139 0.584956 1.097301 -0.150675 2013-01-02 -0.723246 -0.356150 -0.441952 0.027012 -1.851583 2013-01-03 -1.001412 0.129464 0.093433 0.952615 -1.338390 2013-01-04 0.165987 0.227918 0.557940 -0.102501 -1.194053 2013-01-05 0.249493 -1.102096 -0.977755 -0.529540 0.783277 [5 rows x 5 columns] In [25]: df2.ix[:,['A','B','C']] AssertionError: Number of manager items must equal union of block items # manager items: 6, # tot_items: 14 ```
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25,025,719
MDU6SXNzdWUyNTAyNTcxOQ==
5,836
Update copyright strings in docs and repo
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0
2014-01-03T17:37:35Z
2014-01-13T16:30:15Z
2014-01-13T16:30:15Z
NONE
null
To 2008-2014. a formality, but indicates the project is active.
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25,028,248
MDU6SXNzdWUyNTAyODI0OA==
5,837
DOC: should we include links to pandas tutorials in the main docs?
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11
2014-01-03T18:31:37Z
2016-10-12T23:04:28Z
2014-01-25T17:13:33Z
CONTRIBUTOR
null
Template page is here: https://github.com/pydata/pandas/blob/master/doc/source/tutorials.rst ## Samples www.bearrelroll.com/2013/05/python-pandas-tutorial bunch on this page http://datacommunitydc.org/blog/2013/07/python-for-data-analysis-the-landscape-of-tutorials/ http://www.randalolson.com/2012/08/06/statistical-analysis-made-easy-in-python/ http://conference.scipy.org/scipy2013/tutorial_detail.php?id=109 one mentioned on #3061 http://nbviewer.ipython.org/github/twiecki/financial-analysis-python-tutorial/blob/master/1.%20Pandas%20Basics.ipynb of course from the master! @wesm http://blog.wesmckinney.com/ http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/ http://manishamde.github.io/blog/2013/03/07/pandas-and-python-top-10/ https://bitbucket.org/hrojas/learn-pandas https://github.com/pydata/pandas/issues/5994 for this: http://pandas-xlsxwriter-charts.readthedocs.org/
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25,028,561
MDExOlB1bGxSZXF1ZXN0MTEyMTQ3NTk=
5,838
DOC: minor fix in extract docstring
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4
2014-01-03T18:39:14Z
2014-06-20T01:21:42Z
2014-01-04T09:55:17Z
CONTRIBUTOR
null
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df.groupby().apply() with only one group returns wrong shape!
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19
2014-01-03T21:02:49Z
2018-05-25T17:40:08Z
2016-05-09T20:37:17Z
NONE
null
I have reached a corner case in the wonderful groupby().apply() method. A groupby with only **one** group causes the apply method to return the wrong output shape. Instead of a series with a multi-index the result is retunred as a DataFrame with last row index level as columns. :frowning: ``` python In [1]: import numpy as np ...: import pandas as pd ...: from sklearn.cluster import DBSCAN as DBSCAN ...: ...: print pd.__version__ ...: ...: # Generate Test DataFrame ...: NUM_ROWS = 1000 ...: NUM_COLS = 10 ...: col_names = ['A'+num for num in map(str,np.arange(NUM_COLS).tolist())] ...: index_cols = col_names[:5] ...: ...: # Set DataFrame to have 5 level Hierarchical Index. ...: # Sort the index! ...: df = pd.DataFrame(np.random.randint(5, size=(NUM_ROWS,NUM_COLS)), dtype=np.int64, columns=col_names) ...: df = df.set_index(index_cols).sort_index() ...: df ...: ...: # Group by first 4 index columns. ...: grp = df.groupby(level=index_cols[:4]) ...: ...: # Find index of largest group. ...: big_loc = grp.size().idxmax() ...: ...: # Create function to apply clustering on groups ...: def grp_func(df): ...: """Run clustering on subgroup and return series of results.""" ...: db = DBSCAN(eps=1, min_samples=1, metric='euclidean').fit(df.values) ...: return pd.Series(db.labels_, name='cluster_id', index=df.index.get_level_values(4)) ...: 0.12.0 In [2]: # Apply clustering on each subgroup of DataFrame ...: out_good = grp.apply(grp_func) ...: out_good ...: out_good.shape Out[2]: (1000L,) In [3]: # Select out biggest group wihile keeping index levels and try same apply ...: out_bad = df[[big_loc == a[:4] for a in df.index.values]].groupby(level=index_cols[:4]).apply(grp_func) ...: out_bad ...: out_bad.shape Out[3]: (1, 7) In [4]: out_good Out[4]: A0 A1 A2 A3 A4 0 0 0 0 0 1 3 0 1 3 0 2 1 1 3 2 3 0 3 3 1 3 0 1 1 1 0 2 0 0 2 1 4 2 3 4 0 4 1 4 0 0 ... 4 4 3 0 2 0 1 1 1 3 2 4 0 3 1 0 2 1 4 4 0 4 0 4 0 1 1 3 1 1 2 2 2 0 3 1 1 3 0 4 4 0 Name: cluster_id, Length: 1000, dtype: float64 In [5]: out_bad Out[5]: A4 1 1 2 3 3 3 4 A0 A1 A2 A3 3 0 0 3 6 5 3 0 1 4 2 # If you stack the bad result it comes out looking OK, but now I need a workaround for this corner case to use apply. In [17]: out_bad.stack() Out[17]: A0 A1 A2 A3 A4 3 0 0 3 1 3 1 6 2 5 3 1 3 4 3 0 4 2 dtype: float64 ```
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25,036,491
MDU6SXNzdWUyNTAzNjQ5MQ==
5,840
pandas.io.gbq.read_gbq() returns incorrect results
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34
2014-01-03T21:19:20Z
2014-06-30T19:30:22Z
2014-06-30T19:30:22Z
NONE
null
When using the `read_gbq()` function on a BigQuery table, incorrect results are returned. I compare the output from `read_gbq()` to that of a CSV export from BigQuery directly. Interestingly, there are the same number of rows in each output - however, there are many duplicates in the `read_gbq()` output. I'm using Pandas '0.13.0rc1-125-g4952858' on a Mac 10.9 using Python 2.7. Numpy '1.8.0'. The code I execute to load the data in pandas: `churn_data = gbq.read_gbq(train_query, project_id = projectid)` I can't share the underlying data. What additional data/info would be useful for root causing? The output data is ~400k lines.
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25,037,122
MDU6SXNzdWUyNTAzNzEyMg==
5,841
infer_freq borks on PeriodIndex
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3
2014-01-03T21:32:59Z
2014-04-05T15:36:03Z
2014-01-04T18:48:48Z
CONTRIBUTOR
null
Should PeriodIndex pass through infer_freq and return freqstr or should this be the user's problem? Either is a simple fix. The latter should just have a better error message. Right now ``` from pandas.tseries.api import infer_freq from pandas import PeriodIndex infer_freq(PeriodIndex(start="1/1/1990", periods=20, freq="M")) ```
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5,842
Add pandas cookbook to tutorials (for #5837)
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9
2014-01-03T22:42:00Z
2014-06-26T12:23:17Z
2014-01-07T11:04:01Z
CONTRIBUTOR
null
@jreback asked me to add something like this in https://github.com/jvns/pandas-cookbook/issues/1 I haven't been able to test that this displays correctly as I can't figure out how to build the Sphinx docs. If someone can tell me how to build the docs, I can test it. ``` bork@kiwi ~/c/p/pandas> pwd /home/bork/clones/pandas/pandas bork@kiwi ~/c/p/pandas> python ../doc/make.py html Error: Cannot find source directory. Building HTML failed ``` ``` bork@kiwi ~/c/p/doc> pwd /home/bork/clones/pandas/doc bork@kiwi ~/c/p/doc> python make.py html Running Sphinx v1.1.3 cannot import name hashtable Exception occurred while building, starting debugger: Traceback (most recent call last): File "/opt/anaconda/lib/python2.7/site-packages/sphinx/cmdline.py", line 188, in main warningiserror, tags) File "/opt/anaconda/lib/python2.7/site-packages/sphinx/application.py", line 102, in __init__ confoverrides or {}, self.tags) File "/opt/anaconda/lib/python2.7/site-packages/sphinx/config.py", line 216, in __init__ exec code in config File "/home/bork/clones/pandas/doc/source/conf.py", line 74, in <module> import pandas File "/home/bork/clones/pandas/pandas/__init__.py", line 6, in <module> from . import hashtable, tslib, lib ImportError: cannot import name hashtable > /home/bork/clones/pandas/pandas/__init__.py(6)<module>() -> from . import hashtable, tslib, lib ```
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5,843
API: add read_gbq to pandas.io.api so it appears in top-level name space
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7
2014-01-04T00:19:31Z
2014-01-15T21:38:28Z
2014-01-15T02:51:22Z
CONTRIBUTOR
null
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25,047,533
MDExOlB1bGxSZXF1ZXN0MTEyMjU4Mjk=
5,844
BLD: fix cythonized msgpack extension in setup.py GH5831
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4
2014-01-04T03:28:50Z
2014-06-24T04:26:01Z
2014-01-10T12:26:16Z
NONE
null
closes #5831 @yarikoptic, can you confirm this fixes the problem?
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MDU6SXNzdWUyNTA0ODc0Nw==
5,845
version dependency/tests on numexpr -- truediv etc are NA in numexpr 1.4.2
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9
2014-01-04T05:27:17Z
2014-01-26T22:58:21Z
2014-01-26T22:58:21Z
CONTRIBUTOR
null
while building for Ubuntu 12.04, bulk of tests fail due e.g. `ValueError: Unknown keyword argument 'truediv'` from numexpr and `AssertionError: Did not use numexpr as expected` in test_expressions Full log http://neuro.debian.net/_files/_buildlogs/pandas/0.13.0/pandas_0.13.0-2~nd12.04+1_i386.build
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tail throws exception on empty frame
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6
2014-01-04T15:22:19Z
2014-01-15T04:31:05Z
2014-01-15T00:34:38Z
CONTRIBUTOR
null
currently, tail throws an exception on an empty frame but head does not. ``` import pandas as pd df = pd.DataFrame() df.tail() ``` traceback: https://gist.github.com/MichaelWS/8256143 Should this be fixed by returning the frame if empty on calls to iloc? We can check for empty in tail on length as well or create a new slice with start being None)
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ENH: Improve error message for PeriodIndex to infer_freq. Closes #5841.
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3
2014-01-04T15:34:02Z
2014-07-16T08:45:16Z
2014-01-04T18:48:48Z
CONTRIBUTOR
null
closes #5841. I'm still not sure we shouldn't just return `freq` if a PeriodIndex is given. Seems easy enough.
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BUG: tail raises on empty DataFrame
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15
2014-01-04T16:18:26Z
2014-06-29T04:32:50Z
2014-01-15T00:34:38Z
CONTRIBUTOR
null
closes #5846 calling tail on an empty frame threw an exception. In addition, df.tail(0) returns self without indexing.
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BUG: Bug in selection with missing values via .ix from a duplicate indexed DataFrame failing
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0
2014-01-04T17:20:44Z
2014-07-09T13:06:17Z
2014-01-04T18:53:09Z
CONTRIBUTOR
null
closes #5835
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ENH: enhancements to Panel.apply to enable arbitrary functions and multi-dim slicing (GH1148)
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0
2014-01-04T18:03:12Z
2014-06-25T15:23:43Z
2014-01-15T02:53:46Z
CONTRIBUTOR
null
closes #1148 A reproduction of the new docs section Applying with a Panel will pass a Series to the applied function. If the applied function returns a Series, the result of the application will be a Panel. If the applied function reduces to a scalar, the result of the application will be a DataFrame. Note Prior to 0.13.1 apply on a Panel would only work on ufuncs (e.g. np.sum/np.max). ``` In [120]: import pandas.util.testing as tm In [121]: panel = tm.makePanel(5) In [122]: panel <class 'pandas.core.panel.Panel'> Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis) Items axis: ItemA to ItemC Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00 Minor_axis axis: A to D In [123]: panel['ItemA'] A B C D 2000-01-03 0.166882 -0.597361 -1.200639 0.174260 2000-01-04 -1.759496 -1.514940 -1.872993 -0.581163 2000-01-05 0.901336 -1.640398 0.825210 0.087916 2000-01-06 -0.317478 -1.130643 -0.392715 0.416971 2000-01-07 -0.681335 -0.245890 -1.994150 0.666084 [5 rows x 4 columns] ``` A transformational apply. ``` In [124]: result = panel.apply(lambda x: x*2, axis='items') In [125]: result <class 'pandas.core.panel.Panel'> Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis) Items axis: ItemA to ItemC Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00 Minor_axis axis: A to D In [126]: result['ItemA'] A B C D 2000-01-03 0.333764 -1.194722 -2.401278 0.348520 2000-01-04 -3.518991 -3.029880 -3.745986 -1.162326 2000-01-05 1.802673 -3.280796 1.650421 0.175832 2000-01-06 -0.634955 -2.261286 -0.785430 0.833943 2000-01-07 -1.362670 -0.491779 -3.988300 1.332168 [5 rows x 4 columns] ``` A reduction operation. ``` In [127]: panel.apply(lambda x: x.dtype, axis='items') A B C D 2000-01-03 float64 float64 float64 float64 2000-01-04 float64 float64 float64 float64 2000-01-05 float64 float64 float64 float64 2000-01-06 float64 float64 float64 float64 2000-01-07 float64 float64 float64 float64 [5 rows x 4 columns] ``` A similar reduction type operation ``` In [128]: panel.apply(lambda x: x.sum(), axis='major_axis') ItemA ItemB ItemC A -1.690090 1.840259 0.010754 B -5.129232 0.860182 0.178018 C -4.635286 0.545328 2.456520 D 0.764068 -3.623586 1.761541 [4 rows x 3 columns] ``` This last reduction is equivalent to ``` In [129]: panel.sum('major_axis') ItemA ItemB ItemC A -1.690090 1.840259 0.010754 B -5.129232 0.860182 0.178018 C -4.635286 0.545328 2.456520 D 0.764068 -3.623586 1.761541 [4 rows x 3 columns] ``` A transformation operation that returns a Panel, but is computing the z-score across the major_axis. ``` In [130]: result = panel.apply(lambda x: (x-x.mean())/x.std(), axis='major_axis') In [131]: result <class 'pandas.core.panel.Panel'> Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis) Items axis: ItemA to ItemC Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00 Minor_axis axis: A to D In [132]: result['ItemA'] A B C D 2000-01-03 0.509389 0.719204 -0.234072 0.045812 2000-01-04 -1.434116 -0.820934 -0.809328 -1.567858 2000-01-05 1.250373 -1.031513 1.499214 -0.138629 2000-01-06 0.020723 -0.175899 0.457175 0.564271 2000-01-07 -0.346370 1.309142 -0.912988 1.096405 [5 rows x 4 columns] ``` Apply can also accept multiple axes in the axis argument. This will pass a DataFrame of the cross-section to the applied function. ``` In [133]: f = lambda x: (x-x.mean(1)/x.std(1)) In [134]: result = panel.apply(f, axis = ['items','major_axis']) In [135]: result <class 'pandas.core.panel.Panel'> Dimensions: 4 (items) x 5 (major_axis) x 3 (minor_axis) Items axis: A to D Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00 Minor_axis axis: ItemA to ItemC In [136]: result.loc[:,:,'ItemA'] A B C D 2000-01-03 0.748886 -0.323319 -1.172352 0.370451 2000-01-04 -1.594544 -1.659365 -1.444732 -0.162764 2000-01-05 0.908832 -1.220236 0.237668 0.754405 2000-01-06 -1.024669 -0.081850 -0.792957 0.641960 2000-01-07 -0.884333 -0.472889 -1.474646 -0.671871 [5 rows x 4 columns] ``` This is equivalent to the following ``` In [137]: result = Panel(dict([ (ax,f(panel.loc[:,:,ax])) for ax in panel.minor_axis ])) In [138]: result <class 'pandas.core.panel.Panel'> Dimensions: 4 (items) x 5 (major_axis) x 3 (minor_axis) Items axis: A to D Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00 Minor_axis axis: ItemA to ItemC In [139]: result.loc[:,:,'ItemA'] A B C D 2000-01-03 0.748886 -0.323319 -1.172352 0.370451 2000-01-04 -1.594544 -1.659365 -1.444732 -0.162764 2000-01-05 0.908832 -1.220236 0.237668 0.754405 2000-01-06 -1.024669 -0.081850 -0.792957 0.641960 2000-01-07 -0.884333 -0.472889 -1.474646 -0.671871 [5 rows x 4 columns] ```
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Debian wheezy test failure: test_pytables.py:test_encoding 'f' != 'foo'
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8
2014-01-04T19:37:13Z
2014-01-22T02:14:39Z
2014-01-22T02:14:39Z
CONTRIBUTOR
null
``` ====================================================================== FAIL: test_encoding (pandas.io.tests.test_pytables.TestHDFStore) ---------------------------------------------------------------------- Traceback (most recent call last): File "/tmp/buildd/pandas-0.13.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_pytables.py", line 856, in test_encoding tm.assert_frame_equal(store['df'], df) File "/tmp/buildd/pandas-0.13.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 498, in assert_frame_equal check_less_precise=check_less_precise) File "/tmp/buildd/pandas-0.13.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 447, in assert_series_equal assert_almost_equal(left.values, right.values, check_less_precise) File "testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2561) File "testing.pyx", line 93, in pandas._testing.assert_almost_equal (pandas/src/testing.c:1803) File "testing.pyx", line 69, in pandas._testing.assert_almost_equal (pandas/src/testing.c:1499) AssertionError: 'f' != 'foo' ``` pytables 2.3.1-3
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Frequency alias bug? MS -> millisecond or MonthBegin?
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4
2014-01-04T23:31:59Z
2014-04-09T03:03:32Z
2014-04-09T03:03:32Z
CONTRIBUTOR
null
Likely take me a while to work my way through this again completely. Maybe I'm just misremembering this code, but should MS map to millisecond or month begin? (I was looking for a function to easily and generally say that M, MS, M-Jan, M-Dec, etc. are all monthly frequencies.) ``` pd.tseries.frequencies.get_standard_freq('MS') pd.tseries.frequencies.get_freq_group('MS') ``` vs. things like ``` pd.tseries.frequencies.get_offset('MS') ``` and all the rest of the code that gives freq and freqstr MS for month start code like. ``` pd.tseries.offsets.MonthBegin().freqstr ```
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ENH: Refactor code to add is_view method for Series.
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16
2014-01-05T03:37:50Z
2014-06-18T19:59:46Z
2014-01-05T04:30:22Z
CONTRIBUTOR
null
So you can check if a series is a view instead of waiting to get an error on sort or copying by default.
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Automate release notes
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15
2014-01-05T04:05:43Z
2016-11-11T10:07:45Z
2016-11-11T10:07:34Z
CONTRIBUTOR
null
Bit of a PITA to repeat the information in every PR in a release note for that PR as well. The automation tools in IPython for this work well there and in statsmodels. Would be nice to consider adding these to pandas. https://github.com/ipython/ipython/blob/master/tools/gh_api.py https://github.com/ipython/ipython/blob/master/tools/github_stats.py
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COMPAT: Ignore numexpr <= v2 (at least for now)
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29
2014-01-05T05:22:25Z
2014-01-26T22:58:21Z
2014-01-26T22:58:21Z
CONTRIBUTOR
null
Earlier versions of numexpr don't work with pandas because of API changes. For now we should not use numexpr if version too low and then revisit whether we can easily support it later.
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Should series have an is_view method?
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2014-01-05T06:01:27Z
2014-01-06T12:25:15Z
2014-01-06T12:25:15Z
NONE
null
See reverted PR in #5853 and discussion on synergy with changes to pandas re SetttingWithCopy.
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BUG: Query doesn't respect pandas.computation.expressions' numexpr settings
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2
2014-01-05T06:59:41Z
2014-01-26T22:58:21Z
2014-01-26T22:58:21Z
CONTRIBUTOR
null
or at least the query tests don't and still run even with numexpr supposedly disabled.
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COMPAT/BUG: Ignore numexpr < 2.0
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12
2014-01-05T07:14:39Z
2014-07-16T17:24:57Z
2014-01-26T18:38:17Z
CONTRIBUTOR
null
in both computation.expressions and computationr.engine. Issues with API compatibility. Fixes #5857, #5855, #5845.
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rework Series.sort is_view check on underlying ndarray before inplace sort (GH5856)
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6
2014-01-05T15:26:43Z
2014-06-25T17:54:20Z
2014-01-06T12:25:15Z
CONTRIBUTOR
null
closes #5856 related #5853 DataFrame._ixs will properly record a cache change (similar to _get_item_cache)
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COMPAT: add different numexpr versions for testing / skip encoding tests on too low tables versions
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2014-01-05T20:07:58Z
2014-07-16T08:45:29Z
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TST: activate tests in #4858 when new version of PyTables comes out
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0
2014-01-06T12:58:12Z
2014-02-13T22:47:36Z
2014-02-13T22:47:36Z
CONTRIBUTOR
null
see #4858
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MDU6SXNzdWUyNTEwODM5Mw==
5,862
API: implement __finalize__ for resample et al.
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7
2014-01-06T15:38:36Z
2014-01-15T11:56:41Z
2014-01-15T02:08:37Z
CONTRIBUTOR
null
I'm really salivating at the chance to use the `._metadata` and `__finalize__` mechanisms in pandas-0.13-dev to create my own subclass of DataFrame and to have metadata propagate after calling functions inherited from DataFrame like `dropna()`, `resample()` etc. Using the latest 0.13-dev version of Pandas, I think I might have bumped into a small bug (although I'm not sure if this is a bug or not??): `._metadata` propagates after calling `.resample(rule='D')` and `dropna()` but not after calling `.resample(rule='D', how='max')`. More details, including the full code of my subclass, are given under the "experiments" heading of this issue: https://github.com/nilmtk/nilmtk/issues/83
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"high" datetime conversion incorrect
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7
2014-01-06T21:34:29Z
2014-01-07T00:41:00Z
2014-01-07T00:41:00Z
NONE
null
Hi, I have a situation where I have "far away" end dates (3 examples included below). Normal Python strptime conversion handles this correctly, but the to_datetime functionality does a Martin McFly and ends up in the 1800s. See this example that reproduces it: ``` # input time_ser = pd.Series([np.nan, '2013-04-08 00:00:00.000', '2013-06-04 00:00:00.000', '2013-09-06 00:00:00.000', '2013-10-02 00:00:00.000', '2013-10-03 00:00:00.000', '2013-10-30 00:00:00.000', '2013-10-31 00:00:00.000', '2013-11-30 00:00:00.000', '2013-12-02 00:00:00.000', '2013-12-17 00:00:00.000', '2013-12-31 00:00:00.000', '2014-01-15 00:00:00.000', '2014-01-31 00:00:00.000', '2014-02-15 00:00:00.000', '2014-02-28 00:00:00.000', '2014-03-15 00:00:00.000', '2014-03-31 00:00:00.000', '2014-06-15 00:00:00.000', '2014-06-30 00:00:00.000', '2099-12-31 00:00:00.000', '2999-12-31 00:00:00.000', '9990-12-31 00:00:00.000', '9999-12-31 00:00:00.000']) # see last 3 results pd.to_datetime(time_ser, '%Y-%m-%d %H:%M:%S.%f') # normal python import math for x in sorted(time_ser.unique()): if isinstance(x, basestring): print '{} converts to {}'.format( x, datetime.datetime.strptime(x, '%Y-%m-%d %H:%M:%S.%f')) # This is the erroneous result: 21 1830-11-22 00:50:52.580896768 22 1807-03-30 05:56:08.066277376 23 1816-03-29 05:56:08.066277376 ``` I'm running Pandas 0.12 on Ubuntu 12.04LTS with Python 2.7.3. I could not find this issue registered yet. Kind regards, Carst
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25,138,810
MDExOlB1bGxSZXF1ZXN0MTEyNjkzMTk=
5,864
BUG: bug in to_datetime when passed a np.nan or integer datelike and a format string (GH5863)
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0
2014-01-06T23:47:36Z
2014-06-27T05:14:01Z
2014-01-07T00:41:00Z
CONTRIBUTOR
null
closes #5863
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MDU6SXNzdWUyNTE1MTM5MQ==
5,865
BUG: Corrupted excel output when zero in top left of DataFrame
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7
2014-01-07T07:10:17Z
2014-01-15T02:32:45Z
2014-01-15T02:32:45Z
NONE
null
When DataFrame().values[0,0] is a 0 integer, the excel output is corrupted. When opening the excel file in Microsoft Excel 2010 I get: ``` Excel found unreadable content in 'a.xlsx'. Do you want to recover the contents of this workbook? If you trust the source of this workbook, click Yes. ``` When I click Yes, I get: ``` Excel was able to open the file by repairing or removing the unreadable content. Removed Part: /xl/sharedStrings.xml part with XML error. (Strings) Illegal xml character. Line 1, column 95. Removed Records: Cell information from /xl/worksheets/sheet1.xml part ``` The cell that should have been 0 is now empty. Here is an example: ``` python import pandas import numpy as np a = np.arange(25).reshape(5,5) pandas.DataFrame(a).to_excel('./a.xlsx') # this is 'unreadable' pandas.DataFrame(a.astype(float)).to_excel('./afloat.xlsx') # this is OK a[0,0] = 1 pandas.DataFrame(a).to_excel('./a1.xlsx') # this is OK ``` Packages: (from 'pip list' as I couldn't find [ci/print_versions.py](https://github.com/pydata/pandas/blob/master/ci/print_versions.py) mentioned in [CONTRIBUTING](https://github.com/pydata/pandas/blob/master/CONTRIBUTING.md)) pandas (0.12.0) xlrd (0.9.2) openpyxl (1.6.2)
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5,866
DOC: Flesh out the R comparison section of docs (GH3980)
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2014-01-07T09:44:37Z
2014-07-10T09:42:20Z
2014-01-07T11:02:55Z
CONTRIBUTOR
null
@jreback added section for `aggregate`, `tapply` and `dcast`, with the `dcast` example coming from http://stackoverflow.com/questions/19237878/subsetting-a-python-dataframe. #3980
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MDU6SXNzdWUyNTE4MDA2Mw==
5,867
DOC: shouldn't the call signature changes in 0.13 for Panel.reindex (and others) be listed explicitly in the API changes?
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15
2014-01-07T16:54:39Z
2014-01-08T12:32:44Z
2014-01-07T18:55:11Z
CONTRIBUTOR
null
ie. was: `Panel.reindex(major=None, minor=None, method=None, major_axis=None, minor_axis=None, copy=True, **kwargs)` now: `Panel.reindex(items=None, major_axis=None, minor_axis=None, **kwargs)`
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5,868
BLD: fixup setup.py, initialize pipe variable to None
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5
2014-01-07T18:57:13Z
2014-07-16T08:45:37Z
2014-01-07T19:25:21Z
NONE
null
recent changes (fe9c0ae0) broke setup.py for some users: https://github.com/pydata/pandas/issues/5867#issuecomment-31759183
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5,869
BUG: groupby with datetime and cythonized functions regression from 0.12
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0
2014-01-07T20:10:08Z
2014-01-27T11:32:09Z
2014-01-07T20:44:31Z
CONTRIBUTOR
null
``` In [30]: DataFrame(dict(A = Timestamp('20130101'), B = np.arange(5))).groupby('A')['A'].max() Out[30]: A 2013-01-01 1356998400000000000 Name: A, dtype: int64 ``` should match ``` In [31]: DataFrame(dict(A = Timestamp('20130101'), B = np.arange(5))).groupby('A')['A'].apply(lambda x: x.max()) Out[31]: A 2013-01-01 2013-01-01 00:00:00 dtype: datetime64[ns] ```
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BUG: Bug in groupby dtype conversion with datetimelike (GH5869)
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2014-01-07T20:23:43Z
2014-06-17T08:06:13Z
2014-01-07T20:44:31Z
CONTRIBUTOR
null
closes #5869
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5,871
Possible bug in DataFrame.plot when duplicates in DatetimeIndex
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NONE
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There seems to be a bug when plotting dataframes that has duplicates in a DatetimeIndex. It seems to only be triggered when there is at least one more non-duplicate item in the index as in the first two cases below. These result in a: ``` python ValueError: Shape of passed values is (1, 3), indices imply (1, 2) ``` While the last case seems to be fine. ``` python # Case 1: fails df = pandas.DataFrame( index = pandas.to_datetime([ "2002-07-18 13:49:49", "2002-07-18 13:49:49", "2002-07-18 10:38:58" ]), data = [1, 1, 1] ) df.plot() # Case 2: fails df = pandas.DataFrame( index = pandas.to_datetime([ "2002-07-19 10:51:29", "2002-07-18 13:49:49", "2002-07-18 13:49:49", ]), data = [1, 1, 1] ) df.plot() # Case 3: works df = pandas.DataFrame( index = pandas.to_datetime([ "2002-07-18 13:49:49", "2002-07-18 13:49:49", ]), data = [1, 1] ) df.plot() ``` This is with a recent master checkout: 52a82efe98a52df7a2dbb1208df19622ccdff39c
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5,872
v.13 does not support df.apply(lambda x: (datetime.now() - x['Date']).days, axis=1)
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1
2014-01-07T23:47:59Z
2014-01-08T22:14:07Z
2014-01-07T23:49:11Z
NONE
null
df['diff'] = df.apply(lambda x: (datetime.now() - x['Date']).days, axis=1)
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5,873
BUG: has_duplicates misbehaves when multiindex has a NaN
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5
2014-01-08T06:33:14Z
2015-01-02T15:41:39Z
2015-01-02T15:41:39Z
NONE
null
When (at least) one element in a MultiIndex contains a NaN, has_duplicates starts to behave strangely: ``` python >>> idx = pd.MultiIndex.from_arrays([[101, 102], [3.5, np.nan]]) >>> idx MultiIndex [(101, 3.5), (102, nan)] >>> idx.has_duplicates True >>> idx.get_duplicates() [] ``` I would expect has_duplicates to return False here, because 102 is not the same as 101. I would also expect it to return false for the MultiIndex ``` python MultiIndex [(101, 3.5), (101, nan)] ``` since 3.5 != NaN, but this case is more debatable. This is important because you can't call .unstack() on a series with a MultiIndex for which has_duplicates is True, even if the MultiIndex is of high dimension and the dimensions containing the NaN(s) are not involved in the operation. This is with pandas 0.12.0
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test_json:test_round_trip_exception_ fails on python3 in windows
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4
2014-01-08T06:39:12Z
2014-02-04T05:40:06Z
2014-01-09T17:15:11Z
NONE
null
The API design issue discussed in #5655 again. pd.read_json guesses that a 150KB long string is a filename. ``` python ====================================================================== ERROR: test_round_trip_exception_ (pandas.io.tests.test_json.test_pandas.TestPandasContainer) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\workspace\pandas_tests@2\BITS\64\PYTHONVER\33\pandas\util\testing.py", line 1057, in network_wrapper return t(*args, **kwargs) File "C:\workspace\pandas_tests@2\BITS\64\PYTHONVER\33\pandas\io\tests\test_json\test_pandas.py", line 593, in test_round_trip_exception_ result = pd.read_json(s) File "C:\workspace\pandas_tests@2\BITS\64\PYTHONVER\33\pandas\io\json.py", line 176, in read_json if os.path.exists(filepath_or_buffer): File "c:\envs\33-64\lib\genericpath.py", line 18, in exists os.stat(path) nose.proxy.ValueError: ValueError: path too long for Windows -------------------- >> begin captured stdout << --------------------- Failed: ValueError('path too long for Windows',) --------------------- >> end captured stdout << ---------------------- ```
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DOC: Add %in% operator into compare w r (GH3850)
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1
2014-01-08T09:21:06Z
2014-06-26T12:22:49Z
2014-01-15T16:15:01Z
CONTRIBUTOR
null
Doesn't close #3850 but at least the `%in%` operator is now in the comparison with R docs. I've lumped it with the `match` function since thats the page you see the `%in%` operator in the R docs http://finzi.psych.upenn.edu/R/library/base/html/match.html.
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Series.hist() fails for String Series
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8
2014-01-08T12:56:58Z
2018-07-06T22:02:59Z
2018-07-06T21:39:26Z
CONTRIBUTOR
null
Not sure if that's a pandas or a mathplotlib bug. ``` python import pandas df = pandas.DataFrame({"year":[1,2,3,4],"name": [u"abc", u"bcd", u"cde", u"abc"]}) df["year"].hist() # Works df["name"].hist() TypeError Traceback (most recent call last) <ipython-input-11-79a5ab7bbbd5> in <module>() ----> 1 df["name"].hist() C:\portabel\Python27\lib\site-packages\pandas\tools\plotting.pyc in hist_series(self, by, ax, grid, xlabelsize, xrot, ylabelsize, yrot, figsize, **kwds) 2113 values = self.dropna().values 2114 -> 2115 ax.hist(values, **kwds) 2116 ax.grid(grid) 2117 axes = np.array([ax]) C:\portabel\Python27\lib\site-packages\matplotlib\axes.pyc in hist(self, x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs) 8325 # this will automatically overwrite bins, 8326 # so that each histogram uses the same bins -> 8327 m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs) 8328 m = m.astype(float) # causes problems later if it's an int 8329 if normed and not stacked: C:\portabel\Python27\lib\site-packages\numpy\lib\function_base.pyc in histogram(a, bins, range, normed, weights, density) 174 else: 175 range = (a.min(), a.max()) --> 176 mn, mx = [mi+0.0 for mi in range] 177 if mn == mx: 178 mn -= 0.5 TypeError: coercing to Unicode: need string or buffer, float found ```
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"IndexingError: Unalignable boolean Series key provided" when passing an empty object Series
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6
2014-01-08T13:38:56Z
2014-01-08T17:23:27Z
2014-01-08T17:23:12Z
NONE
null
For some reason, with 0.13, an empty object Series is interpreted as a boolean one, when using to fetch elements in another Series: ``` pandas.Series(['A', 'B'])[pandas.Series([], dtype=object)] ``` The following works though, so the dtype confusion seems to be confined to empty Series: ``` pandas.Series(['A', 'B'])[pandas.Series(['C'], dtype=object)] ```
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pivot function on timezone aware objects does not preserve timezone info in resulting dataframe index
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1
2014-01-08T14:33:35Z
2014-05-12T13:34:58Z
2014-05-12T13:34:58Z
NONE
null
This bug is in 0.12.0 Using example DataFrame like below: ``` col data time 0 1 0 2013-03-22 11:00:00-04:00 1 2 1 2013-03-22 15:00:00-04:00 2 2 2 2013-03-22 11:00:00-04:00 3 1 3 2013-03-22 15:00:00-04:00 ``` After pivoting, the old behavior in 0.10.1 properly preserved the timezone info in the index, resulting in a new DataFrame like such: ``` col 1 2 time 2013-03-22 11:00:00-04:00 0 2 2013-03-22 15:00:00-04:00 3 1 ``` However in 0.12.0 this behavior is lost resulting in an index that does not have the timezone information ``` col 1 2 time 2013-03-22 15:00:00 0 2 2013-03-22 19:00:00 3 1 ``` Below is the code to reproduce this issue: ``` python import pandas print pandas.__version__ import datetime import pandas as pn import pytz est = pytz.timezone('US/Eastern') dt1 = est.localize(datetime.datetime(2013,3,22,11,0,0)) dt2 = est.localize(datetime.datetime(2013,3,22,15,0,0)) df = pn.DataFrame({'time': [dt1, dt2, dt1, dt2], 'col': [1, 2, 2, 1], 'data': range(4)}) pivotDf = df.pivot('time', 'col', 'data') print df print pivotDf print pivotDf.index ``` the output from 0.10.1 is: ``` 0.10.1 col data time 0 1 0 2013-03-22 11:00:00-04:00 1 2 1 2013-03-22 15:00:00-04:00 2 2 2 2013-03-22 11:00:00-04:00 3 1 3 2013-03-22 15:00:00-04:00 col 1 2 time 2013-03-22 11:00:00-04:00 0 2 2013-03-22 15:00:00-04:00 3 1 <class 'pandas.tseries.index.DatetimeIndex'> [2013-03-22 11:00:00, 2013-03-22 15:00:00] Length: 2, Freq: None, Timezone: US/Eastern ``` the output from 0.12.0 is: ``` 0.12.0 col data time 0 1 0 2013-03-22 11:00:00-04:00 1 2 1 2013-03-22 15:00:00-04:00 2 2 2 2013-03-22 11:00:00-04:00 3 1 3 2013-03-22 15:00:00-04:00 col 1 2 time 2013-03-22 15:00:00 0 2 2013-03-22 19:00:00 3 1 <class 'pandas.tseries.index.DatetimeIndex'> [2013-03-22 15:00:00, 2013-03-22 19:00:00] Length: 2, Freq: None, Timezone: None ``` Notice the None in the "Timezone: " infor of the DatetimeIndex.
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ENH: Add regex=True flag to str_contains
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24
2014-01-08T15:17:03Z
2014-06-15T00:35:15Z
2014-01-15T00:26:34Z
CONTRIBUTOR
null
Using regex=False can be faster when full regex searching is not needed. See http://stackoverflow.com/q/20951840/190597 Example use case: ``` import string import itertools as IT import numpy as np from pandas import Series def make_series(letters, strlen, size): return Series( np.fromiter(IT.cycle(letters), count=size*strlen, dtype='|S1') .view('|S{}'.format(strlen))) many = make_series('matchthis'+string.uppercase, strlen=19, size=10000) # 31% matches few = make_series('matchthis'+string.uppercase*42, strlen=19, size=10000) # 1% matches ``` ``` In [115]: %timeit many.str.contains('matchthis') 100 loops, best of 3: 4.93 ms per loop In [116]: %timeit many.str.contains('matchthis', regex=False) 100 loops, best of 3: 2.44 ms per loop In [118]: %timeit few.str.contains('matchthis') 100 loops, best of 3: 4.81 ms per loop In [119]: %timeit few.str.contains('matchthis', regex=False) 100 loops, best of 3: 2.37 ms per loop ```
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BUG: Regresssion in handling of empty Series as indexers to Series (GH5877)
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0
2014-01-08T15:19:02Z
2014-06-16T09:26:31Z
2014-01-08T17:23:12Z
CONTRIBUTOR
null
closes #5877 ``` In [1]: s = Series(['A', 'B']) In [2]: s[Series(['C'], dtype=object)] Out[2]: C NaN dtype: object In [3]: s[Series([], dtype=object)] Out[3]: Series([], dtype: object) ``` This didn't work in 0.12 ``` In [4]: s[Series([], dtype=bool)] Out[4]: Series([], dtype: object) ``` This raises though (in both 0.12 and 0.13) ``` In [5]: s[Series([True], dtype=bool)] IndexingError: Unalignable boolean Series key provided `` ```
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25,248,926
MDU6SXNzdWUyNTI0ODkyNg==
5,881
Allow no returns from expanding_apply
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11
2014-01-08T15:35:29Z
2021-04-11T04:03:28Z
2021-04-11T04:03:27Z
CONTRIBUTOR
null
related #4130 I haven't looked at the code at how easy this is to do, but it looks like things like expanding_apply (moving_apply too I suspect) expects a float returned from the function. It might make sense to have an optional no_return flag (or just be smart about it, though perhaps it makes sense to have a flag as a sanity check for the user.) Use case, I'm using expanding_apply to run some code in a subprocess and write to disk. I'm able to get around the expectation by having my function return 0.0 or something, but it's a bit of a hack.
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5,882
GroupBy apply method is calling passed function multiple times for the same group?
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2
2014-01-08T16:10:04Z
2015-11-05T12:55:52Z
2014-01-08T16:14:09Z
NONE
null
There are two groups below, so I expected the function `f` to be called only twice, but you can see in the output below, it was called 3 times. I am using version 0.13.0rc1-92-gf6fd509 Code ``` import pandas as pd from cStringIO import StringIO # get sample dataframe s = '''Col1 Col2 Col3 1 1 1 1 2 2 3 3 3''' df = pd.read_csv(StringIO(s), sep=' ') # to count how many times f is called i = 0 # given the dataframe above, shouldn't this be called only twice? def f(df): global i i += 1 print '[%d] Calling function' % i print 'Performing complex aggregation' return 'Using result %d' % i print df.groupby('Col1').apply(f) ``` Output ``` [1] Calling function Performing complex aggregation [2] Calling function Performing complex aggregation [3] Calling function Performing complex aggregation Col1 1 Using result 2 3 Using result 3 dtype: object ```
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25,254,610
MDU6SXNzdWUyNTI1NDYxMA==
5,883
ENH: to_html horizontal mode option
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2
2014-01-08T16:54:38Z
2014-01-09T00:57:39Z
2014-01-09T00:57:39Z
CONTRIBUTOR
null
Wouldn't be nice to display dataframe with a few indexes but a lot of columns in horizontal mode to better fit within a html page?
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5,884
newey-west adjustment not working properly in OLS
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11
2014-01-08T18:19:03Z
2014-09-23T14:24:46Z
2014-09-23T14:24:46Z
NONE
null
It looks newey-west adjustment is not working properly in OLS when 'cluster' is set to 'time' or 'entity'. Specifically, pandas.stats.plm.py lines 791-794 don't have any effect. Should that be replaced with: xox = math.newey_west(m, nw_lags, nobs, df, nw_overlap)? Here is some code to reproduce the issue. import numpy from pylab import * from pandas import * T = 100 panel_size = 3 data_dimensions = [T, panel_size] xs_per_y = WidePanel({ 'predictor a' : numpy.random.normal(size=data_dimensions), 'predictor b' : numpy.random.normal(size=data_dimensions) }) # y = B_a + B_b + noise ys = xs_per_y['predictor a'] + xs_per_y['predictor b'] + numpy.random.normal(size=data_dimensions) print ols(y=ys, x=xs_per_y, pool=True, cluster = 'time') # we expect the following t-stats to be smaller, but they are the same as the previous OLS print ols(y=ys, x=xs_per_y, pool=True, cluster = 'time', nw_lags=10)
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5,885
BUG: Don't return the same thing twice.
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1
2014-01-08T19:46:08Z
2014-01-08T21:28:34Z
2014-01-08T20:03:17Z
CONTRIBUTOR
null
Surely this is a typo. I noticed this because I was going add more robust parsing to datetools.parse [1], and I was surprised to find this function there. Two suggestions / questions. 1) Why not just roll this into the parse function like statsmodels instead of having it fall down on things like "1989Q1"? Extra function seems unneeded to me. 2) Why return the "resolution"? The parse function doesn't do this, and I'd expect them to have the same return signature. Optionally, the parse function should also try to infer a frequency. [1] https://github.com/statsmodels/statsmodels/blob/master/statsmodels/tsa/base/datetools.py#L117
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datetools.parse interface
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22
2014-01-08T20:20:38Z
2016-08-29T12:18:28Z
2016-08-28T11:04:22Z
CONTRIBUTOR
null
- [ ] document in docstring / cookbook / timeseries.rst usage of combing integer columns into YYYYMMDD and parsing to datetimes - [ ] clean up imports of `_parse/parse` from dateutils - [ ] `to_period` to create `PeriodIndex` I started to make a PR #5885 to fix what I thought was a typo before realizing that this was intentional. It still doesn't make much sense to me though why I would want this return of datetime, _result, resolution. Maybe the whole approach could use a refactor. Otherwise what am I missing? My typical use case for datetools.parse is something like `dates = map(lambda x : parse(' '.join(x)), zip(df.day, df.month, df.year))` A couple of questions. 1. Is there a better way to do this vectorized to datetime operation? AFAICT pd.to_datetime doesn't actually use the 'advanced' parsing for quarterly and monthly dates. 2. Should this all be unified? Assuming I haven't missed it, should there be, e.g., a function `pd.parse_dates` that is a general parser for both strings and works on array-like input, deprecating datetools.parse, datetools.parse_time_string, and datetools.to_datetime. This function could also have a flag to return Period or TimeStamp objects with frequency information instead of the current return of the parsed object and resolution. Given that I'm having to do things like `dates = [x[0] for x in map(lambda x : parse(' '.join(x)), zip(df.day, df.month, df.year))]` Thoughts?
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5,887
DOC: Add two examples to to_datetime
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2014-06-26T01:33:53Z
2014-01-09T00:59:17Z
CONTRIBUTOR
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API/DOC: status of low_memory kwarg of read_csv/table
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12
2014-01-09T05:55:57Z
2016-11-11T19:28:50Z
2016-05-26T23:56:09Z
CONTRIBUTOR
null
I am getting the following warning: ``` /usr/local/lib/python2.7/dist-packages/pandas-0.13.0rc1_78_g142ca62-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1050: DtypeWarning: Columns (6) have mixed types. Specify dtype option on import or set low_memory=False. data = self._reader.read(nrows) ``` but I can find no documentation for `low_memory`
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Missing wheel files for numpy 1.8.0 on py27/33 on network cache for travis
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3
2014-01-09T07:54:53Z
2016-10-12T23:04:29Z
2014-01-20T03:31:07Z
NONE
null
@cpcloud ?
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5,890
dt+BDay(n) gives the wrong date if n>5, n%5==0 and dt not on offset
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2
2014-01-09T08:00:54Z
2014-01-13T16:18:13Z
2014-01-13T16:18:13Z
CONTRIBUTOR
null
This issue is closely related to bug #2680 and I believe it is also a bug. The problem appears when adding a number of BDay n with n > 5, n % 5 == 0 to a starting date which is not on offset. For instance ``` python dt = Timestamp('20140105') # Sunday print (dt + BDay(10)).strftime('%A') ``` ``` Sunday ``` We except Friday and not Sunday. Likewise, Friday is 'gone' from the week in the following loop: ``` python for i in range(8,12): print i, (Timestamp('20140104') + BDay(i)).strftime('%A') ``` ``` 8 Wednesday 9 Thursday 10 Saturday 11 Monday ``` It does yield the correct results for substraction however: ``` python for i in range(9,13): print i, (Timestamp('20140105') - BDay(i)).strftime('%A') ``` ``` 9 Tuesday 10 Monday 11 Friday 12 Thursday ``` I have patched tseries/offsets.py with a couple of lines and it corrects the problem without apparently breaking any of the existing tests (version 0.13.0-94-g0bab303). I am not super familiar with git but can try to write a PR about this if appropriate.
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5,891
Cannot convert numbers to strings when reading an Excel spreadsheet
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34
2014-01-09T10:03:52Z
2016-03-25T19:44:54Z
2014-02-18T02:27:11Z
NONE
null
I'm reading some excel spreadsheets (xlsx format) into pandas using `read_excel`, which generally works great. The problem I have is that when a column contains numbers, pandas converts these to float64 type, and I would like them to be treated as strings. After reading them in, I can convert the column to str: ``` my_frame.my_col = my_frame.my_col.astype('str') ``` This works as far as assigning the right type to the column, but when I view the values in this column, the strings are formatted in scientific-format e.g. 8.027770e+14, which is not what I want. I like to be able to tell Pandas to read the columns as strings. My current solution involves dropping down to xlrd to read the spreadsheet. See stackoverflow question: http://stackoverflow.com/q/20970483/690890
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BUG: Bug in internal caching, related to (GH5727)
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0
2014-01-09T13:04:20Z
2014-07-12T14:28:53Z
2014-01-09T15:22:22Z
CONTRIBUTOR
null
related #5727
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19
2014-01-09T14:53:03Z
2014-06-15T07:02:50Z
2014-01-09T15:53:24Z
NONE
null
Our CI situation could be better. Travis is great but doesn't do windows or 32 bit, the SPARC buildbot provided by @yarikoptic lives in another location and the windows jenkins box isn't publicly viewable. That means :red_circle: builds have been going unnoticed, sometimes for months until it's time to do a release and we inevitably go spelunking through commits from 2 months ago to find the problem. Each platform features some uncorrelated failures so grabbing as much as we can will improve our overall testing coverage. To address these types of problems, I've started working on a side-project called ScatterCI. You can check out the beta version here: [![screenshot - 01092014 - 04 44 52 pm](https://f.cloud.github.com/assets/1820866/1878516/a2dd1c78-793c-11e3-9aac-40f4d23c4c32.png)](http://scatterci.github.io/ScatterCI-Pandas/). This PR should start getting travis jobs on the status page and the windows jenkins box already submits results from it's nightlies. If you hit problems with the status page, please open an issue up on the [ScatterCI-Pandas](http://scatterci.github.io/ScatterCI-Pandas/issues/new) repo, not here. It's not a Pandas thing just a service useful to the project. @yarikoptic, @neirbowj if you're interested, I can send you an API key and have you and your favorite platform join the CI fun. It should just take a few minutes to setup and a full-blown CI server is not even required, a nightly cron job would work just as well.
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TST: Testing bug in reading json/msgpack from a non-filepath on windows under py3 (GH5874)
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3
2014-01-09T16:38:56Z
2014-06-26T01:49:11Z
2014-01-09T17:15:11Z
CONTRIBUTOR
null
closes #5874
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Clipboard test failure on windows
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2
2014-01-09T16:40:22Z
2014-01-27T09:52:34Z
2014-01-15T19:00:50Z
NONE
null
Intermittent, seen on 2.7 64bits ``` python ====================================================================== ERROR: test_round_trip_frame_string (classname) ---------------------------------------------------------------------- Traceback (most recent call last): File "c:\Python27-AMD64\Lib\unittest\case.py", line 331, in run testMethod() File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\27\pandas\io\tests\test_clipboard.py", line 63, in test_round_trip_frame_string self.check_round_trip_frame(dt,excel=False) File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\27\pandas\io\tests\test_clipboard.py", line 50, in check_round_trip_frame data.to_clipboard(excel=excel, sep=sep) File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\27\pandas\core\generic.py", line 934, in to_clipboard clipboard.to_clipboard(self, excel=excel, sep=sep, **kwargs) File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\27\pandas\io\clipboard.py", line 76, in to_clipboard clipboard_set(objstr) File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\27\pandas\util\clipboard.py", line 72, in winSetClipboard ctypes.cdll.msvcrt.strcpy(ctypes.c_char_p(pchData), bytes(text)) WindowsError: exception: access violation writing 0x0000000000000000 ```
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5,896
Fix bug where use of .ix[tuple(...)]=x fails to correctly check out of b...
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3
2014-01-09T17:45:23Z
2014-06-23T20:21:50Z
2014-01-09T23:26:26Z
CONTRIBUTOR
null
...ounds index for columns. This is a regression as it used to work in pandas 0.12.0. The code incorrectly checks the column index against the len() of the dataframe to see if it is in bounds not against the number of columns. Have added 2 tests with non-square dataframes to test that the fix works.
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5,897
'rot' parameter to DataFrame.boxplot not applied to some axes when multi-panel plot is produced using 'by'
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2
2014-01-10T01:13:38Z
2014-07-19T14:13:11Z
2014-07-01T15:28:58Z
NONE
null
Minimal snippet: ``` python import pandas as pd from numpy.random import randn import matplotlib.pyplot as plt d = pd.DataFrame({'one':randn(5), 'two':randn(5), 'three':randn(5), 'label':['label'] * 5}, columns = ['one','two','three', 'label']) bp= d.boxplot(by='label', rot=45) plt.savefig('xxx.png') ``` Output produced: ![xxx](https://f.cloud.github.com/assets/128034/1884064/fcb7cd40-7993-11e3-8a07-58d7613407e2.png) Issue: The label on the left is rotated but the label on the right is not. Versions: pandas '0.12.0' matplotlib '1.3.1'
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ENH/API: Add count parameter to limit generator in Series, DataFrame, and DataFrame.from_records()
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9
2014-01-10T02:08:01Z
2014-07-10T14:59:30Z
2014-01-18T03:34:46Z
CONTRIBUTOR
null
When reading data from a generator type collection only reads the firsts number (count) of values. First step to solve #2305, known the length of the data to allocate memory. - Add count parameter to Series, DataFrame, and DataFrame.from_records(). - In DataFrame.from_records() deprecate the existing parameter nrows. Count it's more general and only refers to quantity of data units. Also exists on numpy API (fromiter). - Some refactor in DataFrame.from_records(). - Add tests too. - Lack of release docs for now.
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5,899
DOC: docs don't build with new Sphinx version 1.2
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31
2014-01-10T10:31:06Z
2014-01-25T22:11:01Z
2014-01-20T08:21:30Z
MEMBER
null
I always build the docs with Sphinx 1.1.3, but recently (in december) a new Sphinx version was released: 1.2. And it seems that the docs won't build with this new version (also mentioned in #5530 and https://github.com/pydata/pandas/pull/5701#discussion_r8715560). You get the following error: ``` File "C:\Users\vdbosscj\Scipy\pandas-joris\doc\source\_templates\autosummary/class.rst", line 1, in top-level template code {% extends "!autosummary/class.rst" %} File "C:\Anaconda\envs\devel2\lib\site-packages\jinja2\utils.py", line 339, in get return self[key] File "C:\Anaconda\envs\devel2\lib\site-packages\jinja2\utils.py", line 389, in __getitem__ if self._queue[-1] != key: RuntimeError: maximum recursion depth exceeded in cmp ``` This has something to do with the class.rst template for autosummary (which we extend in [doc/source/_templates/autosummary/class.rst](https://github.com/pydata/pandas/blob/master/doc/source/_templates/autosummary/class.rst)) If you remove the template class.rst, the docs will build (you only will get a bunch of warnings because of links to non-existing documents, because not all docstring pages are built (that was the reason for the template)).
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5,900
Add row filtering operator
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2014-01-10T17:07:04Z
2016-01-31T17:58:48Z
2016-01-31T17:58:48Z
NONE
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This would allow chaining operations like: ``` python pd.read_csv('imdb.txt') .sort(columns='year') .filter(lambda x: x['year']>1990) # <---this is missing in Pandas .to_csv('filtered.csv') ``` For current alternatives see: http://stackoverflow.com/questions/11869910/pandas-filter-rows-of-dataframe-with-operator-chaining
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https://api.github.com/repos/pandas-dev/pandas/issues/5900/timeline
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