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10,303
ENH: added rsplit to StringMethods
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3
2015-06-06T18:26:34Z
2015-06-09T00:42:41Z
2015-06-09T00:38:32Z
CONTRIBUTOR
null
as a part of https://github.com/pydata/pandas/issues/9111 cc @sinhrks
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10,304
BUG: Categorical.remove_categories(np.nan) fails when underlying dtype is float
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8
2015-06-06T18:53:16Z
2015-06-10T13:40:04Z
2015-06-09T23:45:58Z
CONTRIBUTOR
null
Fixes GH #10156. This also makes different null values indistinguishable inside of remove_categories, but they're already indistinguishable in most other contexts: ``` .python >>> pd.Categorical([], categories=[np.nan, None]) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "pandas/core/categorical.py", line 289, in __init__ categories = self._validate_categories(categories) File "pandas/core/categorical.py", line 447, in _validate_categories raise ValueError('Categorical categories must be unique') ValueError: Categorical categories must be unique ```
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BUG: PeriodIndex.order doesnt preserve freq
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8
2015-06-06T22:40:59Z
2015-08-08T14:38:52Z
2015-08-08T14:38:47Z
MEMBER
null
Closes #10295 (This is based on #10292). Changed `lib.maybe_indices_to_slice` to detect slice with `step` and also check upper bound of index. However, above change affects to the behavior of `Index.slice_locs`. Internally, the behavoir of `Index.get_slice_bound` seems to assume `lib.maybe_indices_to_slice` returns slice only when its step is "+1". Options are: 1. Change `Index.get_slice_bound` to meet updated `lib.maybe_indices_to_slice` logic. 2. Leave `lib.maybe_indices_to_slice` as it is. Let `DatetimeIndex` to use separate logic, maybe `lib.maybe_indices_to_slice_with_step` To avoid any unexpected result, I think option 2 is preferable?
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BUG: bug in json serialization when frame has mixed types
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1
2015-06-07T01:41:01Z
2015-08-22T11:36:04Z
2015-06-07T23:05:24Z
CONTRIBUTOR
null
closes https://github.com/pydata/pandas/issues/10289 on master: ``` python >>> df 1st 2nd 3rd 4th 5th a 10 1 foo 0.1 0.01 b 20 2 bar 0.2 0.02 c 30 3 baz 0.3 0.03 d 40 4 qux 0.4 0.04 >>> read_json(df.to_json(orient='index'), orient='index', convert_axes=False) 1st 2nd 3rd 4th 5th a 40 4 qux 0.4 0.04 ``` on branch: ``` python >>> read_json(df.to_json(orient='index'), orient='index', convert_axes=False) 1st 2nd 3rd 4th 5th a 10 1 foo 0.1 0.01 b 20 2 bar 0.2 0.02 c 30 3 baz 0.3 0.03 d 40 4 qux 0.4 0.04 ```
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10,307
REL: 0.16.2
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11
2015-06-07T22:25:04Z
2015-06-13T17:48:59Z
2015-06-13T17:48:59Z
CONTRIBUTOR
null
going to release 0.16.2 this friday (11th). so don't add anything else to the milestone unless we discuss.
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85,963,299
MDExOlB1bGxSZXF1ZXN0MzcxMTU1MDk=
10,308
BUG: bug in setitem where type promotion is applied to entire block
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5
2015-06-07T23:34:30Z
2015-06-09T23:30:23Z
2015-06-09T14:43:27Z
CONTRIBUTOR
null
closes https://github.com/pydata/pandas/issues/10280 on master: ``` python >>> df foo bar baz a 0 1 2 b 3 4 5 >>> df.dtypes foo int64 bar int64 baz int64 dtype: object >>> df.loc['a', 'bar'] = 3.14 >>> df.dtypes foo float64 bar float64 baz float64 dtype: object ``` on branch: ``` python >>> df.dtypes foo int64 bar int64 baz int64 dtype: object >>> df.loc['a', 'bar'] = 3.14 >>> df.dtypes foo int64 bar float64 baz int64 dtype: object >>> df foo bar baz a 0 3.14 2 b 3 4.00 5 ```
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10,309
New feature/enhancement request: Merge dataframes where one value is between two others
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2015-06-08T08:18:24Z
2019-01-13T00:59:26Z
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NONE
null
In contrast to SQL, it is not currently possible to merge dataframes with inequality constraints. Could this be added? As an example (which I previously posted on [StackOverflow](http://stackoverflow.com/questions/30627968/merge-pandas-dataframes-where-one-value-is-between-two-others)), I need to merge two pandas dataframes on an identifier and a condition where a date in one dataframe is between two dates in the other dataframe. Dataframe A has a date ("fdate") and an ID ("cusip"): ![](http://i.stack.imgur.com/qQ3UB.png) I need to merge this with this dataframe B: ![](http://i.stack.imgur.com/z6ESB.png) In SQL this would be trivial, but the only way I can see how to do this in pandas is to first merge unconditionally on the identifier, and then filter on the date condition: ``` df = pd.merge(A, B, how='inner', left_on='cusip', right_on='ncusip') df = df[(df['fdate']>=df['namedt']) & (df['fdate']<=df['nameenddt'])] ``` The problem with this approach is that after the merge, but before the filtering step, `df` can become unnecessarily large. Presumably this could be avoided if the filtering were done as part of the merge process.
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10,310
PERF: HDFStore iterator can use slices to represent iterator when where=None
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0
2015-06-08T11:26:04Z
2019-10-21T19:52:14Z
null
CONTRIBUTOR
null
from [SO](http://stackoverflow.com/questions/30587026/when-reading-huge-hdf5-file-with-pandas-read-hdf-why-do-i-still-get-memory) ``` In [2]: df = DataFrame(np.random.randn(1000,2),columns=list('AB')) In [3]: df.to_hdf('test.h5','df',mode='w',format='table',data_columns=True) In [4]: i = pd.read_hdf('test.h5','df',chunksize=10) In [5]: i Out[5]: <pandas.io.pytables.TableIterator at 0x108f397d0> In [6]: i.coordinates Out[6]: Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ... 990, 991, 992, 993, 994, 995, 996, 997, 998, 999], dtype='int64', length=1000) ``` These coordinates could be represented by `slice(0,1000,10)` in this case
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10,311
to_excel : Excel Writer output limit 65,536
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3
2015-06-08T12:17:40Z
2015-06-15T16:44:55Z
2015-06-15T16:44:55Z
NONE
null
Excel supports 1e6 rows with .xlsx files. I'm using to_excel for a dataframe with >65,535 rows and am getting the error, User Warning ... since it exceeds Excel's limit of 65,530 'items' per worksheet. Are there any options that allow > 65K rows with .xlsx output files? BTW: to_csv works correctly.
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UnicodeDecodeError: 'ascii' codec can't decode byte 0xc2 in position 20355: ordinal not in range(128)
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9
2015-06-08T13:25:55Z
2019-10-09T05:51:09Z
2019-10-09T05:51:09Z
CONTRIBUTOR
null
While building a fresh package (out of snapshot of master at bc7d48f, since the last release had way too many failures) for Debian I am running into ``` ====================================================================== ERROR: test_file_like (pandas.io.tests.test_html.TestReadHtml) ---------------------------------------------------------------------- Traceback (most recent call last): File "/tmp/buildd/pandas-0.16.1+git97-gbc7d48f/debian/tmp/usr/lib/python3/dist-packages/pandas/io/tests/test_html.py", line 284, in test_file_like df1 = self.read_html(f, '.*Water.*') File "/tmp/buildd/pandas-0.16.1+git97-gbc7d48f/debian/tmp/usr/lib/python3/dist-packages/pandas/io/tests/test_html.py", line 92, in read_html return read_html(*args, **kwargs) File "/tmp/buildd/pandas-0.16.1+git97-gbc7d48f/debian/tmp/usr/lib/python3/dist-packages/pandas/io/html.py", line 865, in read_html parse_dates, tupleize_cols, thousands, attrs, encoding) File "/tmp/buildd/pandas-0.16.1+git97-gbc7d48f/debian/tmp/usr/lib/python3/dist-packages/pandas/io/html.py", line 728, in _parse raise_with_traceback(retained) File "/tmp/buildd/pandas-0.16.1+git97-gbc7d48f/debian/tmp/usr/lib/python3/dist-packages/pandas/compat/__init__.py", line 708, in raise_with_traceback raise exc.with_traceback(traceback) UnicodeDecodeError: 'ascii' codec can't decode byte 0xc2 in position 20355: ordinal not in range(128) ====================================================================== ERROR: test_string (pandas.io.tests.test_html.TestReadHtml) ---------------------------------------------------------------------- Traceback (most recent call last): File "/tmp/buildd/pandas-0.16.1+git97-gbc7d48f/debian/tmp/usr/lib/python3/dist-packages/pandas/io/tests/test_html.py", line 275, in test_string data = f.read() File "/usr/lib/python3.4/encodings/ascii.py", line 26, in decode return codecs.ascii_decode(input, self.errors)[0] UnicodeDecodeError: 'ascii' codec can't decode byte 0xc2 in position 20355: ordinal not in range(128) ====================================================================== ERROR: test_string_io (pandas.io.tests.test_html.TestReadHtml) ---------------------------------------------------------------------- Traceback (most recent call last): File "/tmp/buildd/pandas-0.16.1+git97-gbc7d48f/debian/tmp/usr/lib/python3/dist-packages/pandas/io/tests/test_html.py", line 264, in test_string_io data1 = StringIO(f.read()) File "/usr/lib/python3.4/encodings/ascii.py", line 26, in decode return codecs.ascii_decode(input, self.errors)[0] UnicodeDecodeError: 'ascii' codec can't decode byte 0xc2 in position 20355: ordinal not in range(128) ``` when building with Python 3.4.3+ . Locale is set to C while running tests
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2015-06-08T14:49:25Z
2015-06-08T14:51:30Z
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2
2015-06-08T17:01:00Z
2015-06-08T18:13:00Z
2015-06-08T18:11:10Z
CONTRIBUTOR
null
Add arg3=3 to pipe example
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86,543,496
MDU6SXNzdWU4NjU0MzQ5Ng==
10,315
BUG: week strftime %W, %U / Week dates
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5
2015-06-09T10:24:29Z
2020-06-28T18:22:41Z
2020-06-28T18:22:41Z
NONE
null
``` pd.DatetimeIndex(['1976-W53']) --> DateParseError: unknown string format ``` I tried to solve this with many libraries (dateutil, arrow, chrono, dt8601) and no one worked excepted this one : http://www.egenix.com/products/python/mxBase/mxDateTime/ ``` import mx mx.DateTime.ISO.ParseWeek("1976-W53").pydatetime() --> datetime.datetime(1976, 12, 27, 0, 0) ``` And this one also : https://aniso8601.readthedocs.org/en/v1.0.0/ ``` import aniso8601 aniso8601.parse_date('1976-W53') --> datetime.date(1976, 12, 27) ``` As the guideline require it : ``` pd.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 2.7.2.final.0 python-bits: 64 OS: Darwin OS-release: 12.2.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: fr_FR.UTF-8 pandas: 0.16.1 nose: 1.3.6 Cython: None numpy: 1.9.2 scipy: 0.15.1 statsmodels: None IPython: 3.1.0 sphinx: 1.3.1 patsy: 0.3.0 dateutil: 2.4.2 pytz: 2015.4 bottleneck: None tables: None numexpr: None matplotlib: 1.3.1 openpyxl: 1.8.6 xlrd: None xlwt: None xlsxwriter: 0.5.7 lxml: 3.4.3 bs4: 4.3.2 html5lib: None httplib2: None apiclient: None sqlalchemy: 0.9.9 pymysql: 0.6.2.None psycopg2: 2.4.5 (dt dec pq3 ext) ```
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DOC: API docs broken with sphinx 1.3.x
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4
2015-06-09T12:04:38Z
2016-06-23T12:42:39Z
2016-06-23T12:41:48Z
MEMBER
null
Just to track this on our side: as raised by @shoyer, there is a bug in the latest sphinx version that causes part of the API docs not to built. Sphinx bug report: https://github.com/sphinx-doc/sphinx/issues/1822, pr: https://github.com/sphinx-doc/sphinx/pull/1828
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df.to_json segfaults with categorical index
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10
2015-06-09T17:25:42Z
2015-06-10T16:51:15Z
2015-06-10T10:30:12Z
NONE
null
DataFrame.to_json is reliably segfaulting python when the DataFrame has an index of type CategoricalIndex. ``` python import pandas as pd idx = pd.Categorical([1,2,3], categories=[1,2,3]) df = pd.DataFrame( { 'count' : pd.Series([3,2,2],index=idx) } ) # this will crash python (2.6.X or 2.7.X on linux 64 or win 64 with pandas 0.16.1) print df.to_json(orient='split') ``` If I call with orient='index', I get a value error instead: ``` python # this throws a ValueError print df.to_json(orient='index') ``` ``` ValueError: Label array sizes do not match corresponding data shape ``` For what it's worth, my work around, which is acceptable in my application, is to convert my index to strings: ``` python df.index = df.index.astype(str) print df.to_json(orient='split') ``` Windows config: ## INSTALLED VERSIONS commit: None python: 2.7.7.final.0 python-bits: 64 OS: Windows OS-release: 7 machine: AMD64 processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel byteorder: little LC_ALL: None LANG: None pandas: 0.16.1 nose: None Cython: 0.20.1 numpy: 1.8.1 scipy: 0.15.1 statsmodels: None IPython: 2.1.0 sphinx: None patsy: None dateutil: 2.2 pytz: 2014.4 bottleneck: None tables: 3.1.1 numexpr: 2.4 matplotlib: 1.3.1 openpyxl: 2.0.3 xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: None httplib2: None apiclient: None sqlalchemy: None pymysql: None psycopg2: None linux config: ## INSTALLED VERSIONS commit: None python: 2.6.8.final.0 python-bits: 64 OS: Linux OS-release: 2.6.18-274.el5 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 pandas: 0.16.1 nose: None Cython: None numpy: 1.9.2 scipy: None statsmodels: None IPython: None sphinx: None patsy: None dateutil: 2.4.2 pytz: 2015.4 bottleneck: None tables: 3.2.0 numexpr: 2.4.3 matplotlib: None openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: None httplib2: None apiclient: None sqlalchemy: None pymysql: None psycopg2: None
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MDU6SXNzdWU4NjcwOTEyMQ==
10,318
BUG: Timestamp construction from numpy.datetime64 creates a naive Timestamp
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4
2015-06-09T19:30:59Z
2015-06-09T21:42:47Z
2015-06-09T19:51:08Z
CONTRIBUTOR
null
``` >>> d = '2013-02-02T02:12:34+0600' >>> ts_from_str = pandas.Timestamp(d) >>> ts_from_dt64 = pandas.Timestamp(numpy.datetime64(d)) >>> ts_from_dt64.tzinfo >>> ts_from_str.tzinfo pytz.FixedOffset(360) ``` NumPy documentation is clear about `datetime64` representing 'a single moment in time' and being 'always stored based on POSIX time'. It seems to me that a `Timestamp` object constructed from `datetime64` should always be tz-aware, so that the equality comparison between `ts_from_dt64` and `ts_from_str` would return `True`, instead of raising an error. This part seems easy and I can create a PR if what I wrote makes sense. Since `datetime64` doesn't seem to store offsets, we probably can't `get ts_from_dt64` and `ts_from_str` to print the same string.
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10,319
rolling_mean returns small negative value on Series with non-negative values
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1
2015-06-09T20:55:29Z
2020-09-15T22:20:49Z
2020-09-15T22:20:49Z
NONE
null
We came upon this issue today while trying to take a square root of a non-negative series. A quick search came up with this old issue: https://github.com/pydata/pandas/issues/2527.The issue was reproduced on 0.15 by tweaking the code from the previous report, which is below: ``` from pandas import date_range, Series s = Series(index=date_range('1999-02-03','1999-04-05')) s['1999-02-03'] = 0.00012456 s['1999-02-04'] = 0.0003 s['1999-04-04'] = -0.0 s['1999-04-05'] = -0.0 In [21]: s.rolling(1).mean()['1999-04-05'] Out[21]: -5.421010862427522e-20 ``` which returns -5.4210108624275222e-20.
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10,320
Groupby with multiple frames
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4
2015-06-09T23:37:08Z
2019-10-26T04:32:58Z
2019-10-26T04:32:58Z
CONTRIBUTOR
null
Perhaps there is some canonical way of doing this that I'm not aware of, but from time to time I find myself wanting to group more than one frame by a key, and apply some function to the result. At the moment, I'm doing this in a somewhat roundabout way: ``` results = [] for key, t1 in table1.groupby(level='id'): try: t2 = table2.xs(key, level='id') except KeyError: t2 = None try: t3 = table3.xs(key, level='id') except KeyError: t3 = None # ... etc r = some_func(t1, t2, t3) results.append(r) results = pd.concat(results) ``` I realize that often the easiest thing to do is just to merge the frames, but there are cases where that doesn't really make sense (e.g. non-compatible multiindexes that share one level). I imagine `pd.merge(t1, t2, on="id")` already does something like this to line up tables - do you guys think there is any value in exposing a standard way of doing this? Perhaps something like: ``` pd.multi_frame_groupby([t1, t2, t3, t4], level='id').apply(some_func) ``` Or, given that the full behaviour of such a groupby object is non trivial, perhaps just a generator for frame tuples? ``` gen = pd.multi_frame_groupby([t1, t2, t3, t4], level='id') pd.concat([some_func(*t) for t in gen]) ```
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BUG: Bug in to_json with certain orients and a CategoricalIndex would segfault, closes #10317
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1
2015-06-10T00:42:11Z
2015-06-10T01:07:41Z
2015-06-10T01:07:41Z
CONTRIBUTOR
null
xref #10317 ``` In [17]: df = DataFrame({ 'A' : pd.Series([3,2,2],index=pd.Categorical([1,2,3],categories=[1,2,3])), 'B' : pd.Categorical(list('aab')) }) In [18]: df Out[18]: A B 1 3 a 2 2 a 3 2 b In [19]: df.index Out[19]: CategoricalIndex([1, 2, 3], categories=[1, 2, 3], ordered=False, dtype='category') In [20]: df.dtypes Out[20]: A int64 B category dtype: object In [31]: def f(orient): print "orient->%s" % orient print df.to_json(orient=orient) ....: In [32]: f('columns') orient->columns {"A":{"1":3,"2":2,"3":2},"B":{"1":"a","2":"a","3":"b"}} In [33]: f('index') orient->index {"1":{"A":3,"B":"a"},"2":{"A":2,"B":"a"},"3":{"A":2,"B":"b"}} In [34]: f('split') orient->split {"columns":["A","B"],"index":[1,2,3],"data":[[3,"a"],[2,"a"],[2,"b"]]} In [35]: f('records') orient->records [{"A":3,"B":"a"},{"A":2,"B":"a"},{"A":2,"B":"b"}] In [36]: f('values') orient->values [[3,"a"],[2,"a"],[2,"b"]] ```
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10,322
Bug in to_json causing segfault with a CategoricalIndex (GH #10317)
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5
2015-06-10T01:04:09Z
2015-09-19T00:38:31Z
2015-06-10T10:28:59Z
CONTRIBUTOR
null
Fixed GH #10317 Stole tests from #10321
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DOC: index's difference method not in API documentation
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1
2015-06-10T06:45:52Z
2015-06-10T06:58:35Z
2015-06-10T06:58:28Z
CONTRIBUTOR
null
http://pandas.pydata.org/pandas-docs/stable/api.html e.g. ``` X_train.columns.difference(X_test.columns) ```
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10,324
mapping with category data does not work
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``` import pandas as pd a = pd.Series([1, 2, 3, 4]) b = pd.Series(["even", "odd", "even", "odd"], dtype="category") c = pd.Series(["even", "odd", "even", "odd"]) ​ print("-- map by category") try: print(a.map(b)) except AttributeError as e: print(e) ​ print("-- map by string") print(a.map(c)) ``` has the following output: > -- map by category > 'Categorical' object has no attribute 'flags' > -- map by string > 0 odd > 1 even > 2 odd > 3 NaN > dtype: object
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Groupby - Apply method doesn't handle arguments correctly
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1
2015-06-10T21:36:24Z
2015-06-11T08:47:37Z
2015-06-10T21:39:14Z
NONE
null
The `apply` method on a groupby object appears to send all arguments to the applied function. As in, if you try to do `df.groupby(level='dt').apply(func, axis=1)`, the `axis=1` argument is passed to `func` rather than being parsed by `apply` as intended. The behavior is correct, however, in the `DataFrame.apply` method.
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87,138,204
MDU6SXNzdWU4NzEzODIwNA==
10,326
pivot_table passes junk to aggfunc when value column does not exist
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5
2015-06-10T23:08:56Z
2020-01-03T23:23:45Z
2020-01-03T23:23:45Z
NONE
null
Expected behavior is that pivot_table would throw an exception explaining the missing column. Instead pivot_table passes DataFrames to aggfunc before checking. Example: ``` python def agg(l): it = iter(l) try: value = next(it) except StopIteration: raise Exception("0 items in iterator") try: next(it) raise Exception("More than 1 item in iterator") except StopIteration: return value foo = pd.DataFrame({"X": [0, 0, 1, 1], "Y": [0, 1, 0, 1], "Z": [10, 20, 30, 40]}) foo.pivot_table('Z', 'X', 'Y', aggfunc=agg) ``` gives, as expected: ``` Y 0 1 X 0 10 20 1 30 40 ``` But the following throws an Exception from agg, rather than an exception from pivot_table about the missing value column: ``` python foo.pivot_table('notpresent', 'X', 'Y', aggfunc=agg) ```
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MDU6SXNzdWU4NzE1NDc3NQ==
10,327
Request for DataFrame.to_tsv() for reading tab delimited text
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16
2015-06-11T00:22:03Z
2019-01-29T20:49:54Z
2015-09-22T08:52:02Z
CONTRIBUTOR
null
I propose a function, which can be called on a DataFrame, named to_tsv or to_table. The function is the equivalent of `to_csv()` with the argument `sep='\t'`. While `to_tsv()` contains the functionality to write tsv files, I find it annoying to always have to specify an additional argument. I prefer tsv files to csv files because tabs more rarely occur and therefore decrease the need for escaping. I also find the plain-text rendering more readable. I worry that the lack of a dedicated `to_tsv()` function encourages the use of csv over tsv. Currently `read_table()` defaults to tab separators, but there is no equivalent function for writing.
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87,240,777
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10,328
BUG/ENH: GH10319 added higher_precision argument to rolling_mean/sum
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6
2015-06-11T07:17:26Z
2015-06-11T23:57:33Z
2015-06-11T23:57:33Z
CONTRIBUTOR
null
This is not specifically for #10319, but should close it for most cases, should users pass in `higher_precision=True`. #10319 is not really a bug in the Cython code, just precision loss when you do finite-precision arithmetic naively. ``` >>> sum([0.00012456, 0.0003, -0.00012456, -0.0003]) -5.421010862427522e-20 ``` This is `2**-64` and quite negligible, although it can be made much bigger with carefully chosen numbers. I think rolling_\* functions that require summation could use some more precision, since doing everything in one pass accumulates rounding error, even if the rolling window is narrow. The only reason why [0.00012456, 0.0003, 0.0, 0.0] currently returns the 'correct' result while [0.00012456, 0.0003, -0.0, -0.0] doesn't is because of explicit negative entry counting. This is only done in rolling_mean but not rolling_sum, and the lack of symmetry doesn't seem desirable. Perhaps we could remove the counting, or add it to rolling_sum as well?
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MDU6SXNzdWU4NzI2MTkyOQ==
10,329
Strange error message when summing datetime64 and datetime.time column
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17
2015-06-11T08:40:14Z
2020-04-03T20:20:04Z
2020-04-03T20:20:04Z
MEMBER
null
You get `TypeError: Argument 'values' has incorrect type (expected numpy.ndarray, got Series)`: ``` In [29]: df = pd.DataFrame({'date':pd.date_range('2012-01-01', periods=3), 'time':[datetime.time(i, i, i) for i in range(3)]}) In [30]: df Out[30]: date time 0 2012-01-01 00:00:00 1 2012-01-02 01:01:01 2 2012-01-03 02:02:02 In [31]: df['date'] + df['time'] --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-31-5101228e303e> in <module>() ----> 1 df['date'] + df['time'] C:\Anaconda\lib\site-packages\pandas\core\ops.pyc in wrapper(left, right, name) 491 return NotImplemented 492 --> 493 time_converted = _TimeOp.maybe_convert_for_time_op(left, right, name) 494 495 if time_converted is None: C:\Anaconda\lib\site-packages\pandas\core\ops.pyc in maybe_convert_for_time_op(c ls, left, right, name) 455 if name.startswith('__r'): 456 name = "__" + name[3:] --> 457 return cls(left, right, name) 458 459 C:\Anaconda\lib\site-packages\pandas\core\ops.pyc in __init__(self, left, right, name) 272 self.right = right 273 lvalues = self._convert_to_array(left, name=name) --> 274 rvalues = self._convert_to_array(right, name=name, other=lvalues ) 275 276 self.is_timedelta_lhs = com.is_timedelta64_dtype(left) C:\Anaconda\lib\site-packages\pandas\core\ops.pyc in _convert_to_array(self, val ues, name, other) 354 elif not (isinstance(values, (np.ndarray, pd.Series)) and 355 com.is_datetime64_dtype(values)): --> 356 values = tslib.array_to_datetime(values) 357 elif inferred_type in ('timedelta', 'timedelta64'): 358 # have a timedelta, convert to to ns here TypeError: Argument 'values' has incorrect type (expected numpy.ndarray, got Series) ```
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87,331,646
MDExOlB1bGxSZXF1ZXN0Mzc0NTg1NjQ=
10,330
DOC: Remove auto_close option form read_hdf
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7
2015-06-11T12:37:05Z
2015-06-15T13:56:20Z
2015-06-12T14:47:53Z
CONTRIBUTOR
null
Remove docstring indicating auto_close can be used in read_hdf. This value is always ignored. Also removes unreachable code. xref #9327
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10,331
Partial string matching for timestamps with multiindex
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13
2015-06-11T16:45:05Z
2019-06-19T14:48:47Z
2016-03-21T12:27:56Z
CONTRIBUTOR
null
I'm trying to get a slice from a multiindex. I find the behavior pretty inconsistent. Here is a simple dataframe: ``` python test = pd.DataFrame({'l': ['A', 'B', 'C', 'C', 'B', 'A'], 'm': [pd.Timestamp('2014-06-11 14:26:27'), pd.Timestamp('2014-06-11 15:26:27'), pd.Timestamp('2014-06-11 16:26:27'), pd.Timestamp('2014-06-12 14:26:27'), pd.Timestamp('2014-06-12 15:26:27'), pd.Timestamp('2014-06-12 16:26:27')]}) ``` With a single index, I can select all the data for a given day as follows: ``` python test_single_index = test.set_index(['m']) #partial string indexing works test_single_index.loc['2014-06-11'] ``` But it doesn't work for a multiindex: ``` python test_multi_index = test.set_index(['m', 'l']) #would expect this to work test_multi_index.loc['2014-06-11'] #or this test_multi_index.loc[('2014-06-11', 'A'),:] #what works idx = pd.IndexSlice test_multi_index.loc[idx['2014-06-11':'2014-06-11',:],:] test_multi_index.loc[idx['2014-06-11','A'],:] ``` So I can make it work if I specify the slice explicitely, but it would be nice if the behavior for the 1D index carried over to Multiindices.
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BUG: Panel.apply (2d) doesn't work when function returns a scalar of NumPy datatype in 0.16.1
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1
2015-06-11T17:17:45Z
2015-06-12T14:28:08Z
2015-06-12T14:28:08Z
NONE
null
Panel.apply (2d) doesn't work when function returns a scalar of NumPy datatype in 0.16.1. test code is as follows: ``` import pandas import numpy panel = pandas.Panel(numpy.random.randn(5, 5, 5)) panel.apply(lambda df: 0, axis=[1, 2]) # OK panel.apply(lambda df: 0.0, axis=[1, 2]) # OK panel.apply(lambda df: numpy.int64(0), axis=[1, 2]) # NG panel.apply(lambda df: numpy.float64(0.0), axis=[1, 2]) # NG ``` result is: ``` yIn [1]: import pandas In [2]: import numpy In [3]: panel = pandas.Panel(numpy.random.randn(5, 5, 5)) In [4]: panel.apply(lambda df: 0, axis=[1, 2]) # OK Out[4]: 0 0 1 0 2 0 3 0 4 0 dtype: int64 In [5]: panel.apply(lambda df: 0.0, axis=[1, 2]) # OK Out[5]: 0 0 1 0 2 0 3 0 4 0 dtype: float64 In [6]: panel.apply(lambda df: numpy.int64(0), axis=[1, 2]) # NG --------------------------------------------------------------------------- PandasError Traceback (most recent call last) <ipython-input-6-fa750da7769d> in <module>() ----> 1 panel.apply(lambda df: numpy.int64(0), axis=[1, 2]) # NG C:\Anaconda3\lib\site-packages\pandas\core\panel.py in apply(self, func, axis, **kwargs) 967 # 2d-slabs 968 if isinstance(axis, (tuple,list)) and len(axis) == 2: --> 969 return self._apply_2d(f, axis=axis) 970 971 axis = self._get_axis_number(axis) C:\Anaconda3\lib\site-packages\pandas\core\panel.py in _apply_2d(self, func, axis) 1066 results.append((e,obj)) 1067 -> 1068 return self._construct_return_type(dict(results)) 1069 1070 def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None, C:\Anaconda3\lib\site-packages\pandas\core\panel.py in _construct_return_type(self, result, axes) 1122 1123 raise PandasError('invalid _construct_return_type [self->%s] ' -> 1124 '[result->%s]' % (self, result)) 1125 1126 def _wrap_result(self, result, axis): PandasError: invalid _construct_return_type [self-><class 'pandas.core.panel.Panel'> Dimensions: 5 (items) x 5 (major_axis) x 5 (minor_axis) Items axis: 0 to 4 Major_axis axis: 0 to 4 Minor_axis axis: 0 to 4] [result->{0: 0, 1: 0, 2: 0, 3: 0, 4: 0}] In [7]: panel.apply(lambda df: numpy.float64(0.0), axis=[1, 2]) # NG --------------------------------------------------------------------------- PandasError Traceback (most recent call last) <ipython-input-7-e7ea1dec29c2> in <module>() ----> 1 panel.apply(lambda df: numpy.float64(0.0), axis=[1, 2]) # NG C:\Anaconda3\lib\site-packages\pandas\core\panel.py in apply(self, func, axis, **kwargs) 967 # 2d-slabs 968 if isinstance(axis, (tuple,list)) and len(axis) == 2: --> 969 return self._apply_2d(f, axis=axis) 970 971 axis = self._get_axis_number(axis) C:\Anaconda3\lib\site-packages\pandas\core\panel.py in _apply_2d(self, func, axis) 1066 results.append((e,obj)) 1067 -> 1068 return self._construct_return_type(dict(results)) 1069 1070 def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None, C:\Anaconda3\lib\site-packages\pandas\core\panel.py in _construct_return_type(self, result, axes) 1122 1123 raise PandasError('invalid _construct_return_type [self->%s] ' -> 1124 '[result->%s]' % (self, result)) 1125 1126 def _wrap_result(self, result, axis): PandasError: invalid _construct_return_type [self-><class 'pandas.core.panel.Panel'> Dimensions: 5 (items) x 5 (major_axis) x 5 (minor_axis) Items axis: 0 to 4 Major_axis axis: 0 to 4 Minor_axis axis: 0 to 4] [result->{0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0}] In [8]: ```
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10,333
SciPy 2015 conference birds-of-a-feather session
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18
2015-06-11T21:32:24Z
2016-04-09T23:04:14Z
2016-04-09T23:04:14Z
MEMBER
null
This issue is for discussing the organization of a [birds-of-a-feather](http://scipy2015.scipy.org/ehome/115969/259291/?&) session for the SciPy 2015 conference. Please speak up if you're interested shaping this discussion (and especially if you'll be at the conference). I'll be at SciPy and would definitely like to make sure this discussion happens. Here are a few possible topics: Editable doc of the proceedings: http://tinyurl.com/futureofpandas - What should pandas 1.0 look like? (https://github.com/pydata/pandas/issues/10000) - Is the pandas DataFrame too complex? Is there room/need for a simpler DataFrame implementation? For more context: http://www.slideshare.net/wesm/dataframes-the-good-bad-and-ugly - do we want _out-of-core_ / parallel support (e.g. how to integrate `dask`)
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10,334
What is the most efficient way to iterate over Pandas's DataFrame row by row?
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8
2015-06-12T01:11:57Z
2021-08-09T09:35:00Z
2015-06-12T01:28:20Z
NONE
spam
I have tried the function `df.iterrows()` but its performance is horrible. Which is not surprising given that `iterrows()` returns a `Series` with full schema and meta data, not just the values (which all that I need). The second method that I have tried is `for row in df.values`, which is significantly faster. However, I have recently realized that `df.values` is not the internal data storage of the DataFrame, because `df.values` converts all `dtypes` to a common `dtype`. For example, one of my columns has dtype `int64` but the dtype of `df.values` is all `float64`. So I suspect that `df.values` actually creates another copy of the internal data. Also, another requirement is that the row iteration must return a list of values that preserve the original `dtype` of the data.
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10,335
BUG: GH10332 where Panel.apply does not handle result with ndim=0
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1
2015-06-12T01:35:25Z
2015-06-12T14:28:13Z
2015-06-12T14:28:08Z
CONTRIBUTOR
null
This is to close #10332
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Dataframe Where Dataframe == False Returns Dataframe with Floats
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6
2015-06-12T03:37:45Z
2021-05-10T21:45:32Z
2021-05-10T21:45:32Z
NONE
null
If I have a Dataframe with True/False values only like this: ``` df_mask = pd.DataFrame({'AAA': [True] * 4, 'BBB': [False]*4, 'CCC': [True, False, True, False]}); print(df_mask) ``` ``` AAA BBB CCC 0 True False True 1 True False False 2 True False True 3 True False False ``` Then try to `print` where the values in the dataframe is equivalent to `False` like so: ``` print(df_mask[df_mask == False]) print(df_mask.where(df_mask == False)) ``` My question is about column `CCC`. Column `BBB` shows False (as I expect) but why is `index 1` and `3` in column `CCC` equal to `0` instead of `False`? ``` AAA BBB CCC 0 NaN False NaN 1 NaN False 0 2 NaN False NaN 3 NaN False 0 AAA BBB CCC 0 NaN False NaN 1 NaN False 0 2 NaN False NaN 3 NaN False 0 ``` Why doesn't it return a dataframe that looks like this? ``` AAA BBB CCC 0 NaN False NaN 1 NaN False False 2 NaN False NaN 3 NaN False False ``` I was asked to post this question here. I originally posted this on [StackOverflow here](http://stackoverflow.com/questions/30701068/python-pandas-boolean-dataframe-where-dataframe-equals-false-returns-0-instead).
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BUG #10228: segfault due to out-of-bounds in binning
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6
2015-06-12T08:13:02Z
2015-06-27T18:29:01Z
2015-06-26T23:24:27Z
CONTRIBUTOR
null
Closes #10228. I also deleted some duplicated code while I was at it. So, I wasn't sure if I should be including a unit test for this. This issue was a segfault, which was happening when you do: ``` s = pd.Series([], index=pd.DatetimeIndex([]), dtype=np.object) s.resample('d', how='count') ``` so I suppose I could add this code into a test, and just verify it doesn't segfault, but that seemed like a bad idea. It would be testing for something that's undefined behavior and therefore be a non-deterministic test.
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DOC: small clean up whatsnew 0.16.2
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DOC: add versionadded to docstrings for 0.16.0 / 0.16.1
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MDU6SXNzdWU4Nzc4MjQ0NQ==
10,340
doc/make.py only works for development build
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3
2015-06-12T16:34:24Z
2018-02-22T02:05:44Z
2018-02-22T02:05:44Z
NONE
null
doc/make.py contains the statement `os.environ['PYTHONPATH'] = '..'`, which means it will only work with an inplace build. I need to be able to contribute to pandas documentation using an installed version of pandas. When I comment out this line, I am able to build the html documentation from source. See this [SO question](http://stackoverflow.com/questions/30807270) for background and details.
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87,824,558
MDExOlB1bGxSZXF1ZXN0Mzc1OTUzODY=
10,341
BUG: Check complib values
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4
2015-06-12T19:17:00Z
2015-06-15T13:56:20Z
2015-06-12T21:49:39Z
CONTRIBUTOR
null
Add check for complib when opening a HDFStore closes #4582 closes #8874
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87,866,799
MDU6SXNzdWU4Nzg2Njc5OQ==
10,342
df.fillna inplace bug
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3
2015-06-12T22:25:23Z
2015-06-12T22:43:33Z
2015-06-12T22:43:18Z
NONE
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This code: ``` a = df() a['f'] = [None, None] a['s'] = [None, None] a['t'] = [None, None] a.loc[:,['f','s']] = a.loc[:,['f','s']].fillna(-1) #works a.loc[:,'f'].fillna(-1, inplace=True) #also works a.loc[:,['f','s']].fillna(-1, inplace=True) #doesn't ``` It's not the biggest error, but still.
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87,910,491
MDU6SXNzdWU4NzkxMDQ5MQ==
10,343
CLN: Series.asof
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0
2015-06-13T02:37:15Z
2019-10-21T20:06:20Z
null
CONTRIBUTOR
null
As discussed in #10266 `Series.asof` is effectively equivalent to `s.dropna().reindex(where, method='ffill')`. The implementation of `Series.asof` uses `Index.asof_locs`, which is used no where else in the code, and is untested. So deprecating `Series.asof` might make sense. Let's add the impls discussed (IOW, implement `.asof` in terms of reindexing internally). Further let's add tests for `.asof_locs` (though this maybe abel to tbe removed)
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10,344
BUG: DataFrame.to_html(index=False) with named Index rendered incorrectly
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2015-06-13T02:50:28Z
2015-08-05T10:29:55Z
2015-08-05T10:29:55Z
MEMBER
null
As below capture, index name is drawn even if `index=False` is specified, and it corrupts the layout. ![2015-06-13 11 47 56](https://cloud.githubusercontent.com/assets/1696302/8142720/1be7dc10-11c2-11e5-894b-8b27c2c62048.png) For copy/paste to Jupyter: ``` import numpy as np import pandas as pd from IPython.display import HTML df = pd.DataFrame(np.random.randn(2, 2), index=pd.Index(['A', 'B'], name='idx')) HTML(df.to_html()) HTML(df.to_html(index=False)) ```
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10,345
DEPR: deprecating series asof GH10343
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15
2015-06-13T03:10:35Z
2015-08-13T21:55:58Z
2015-08-13T10:20:30Z
CONTRIBUTOR
null
closes #10343 and discussion on #10266 - deprecating `Series.asof(where)` in favour of `Series.reindex(where, method='ffill')`.
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10,346
BUG: df.to_html(index=False) renders index.name
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3
2015-06-13T11:14:58Z
2015-08-05T10:30:19Z
2015-08-05T10:29:55Z
MEMBER
null
Closes #10344.
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10,347
limiting reindex with MultiIndex ffill/bfill within levels.
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5
2015-06-13T13:35:44Z
2021-04-18T07:01:42Z
null
CONTRIBUTOR
null
xref #7895 When reindexing on a multiindex with `method='ffill'` or `method='bfill'`, it would be very useful to be able to restrict the fill to certain groups/levels of the index. For example, consider the following: ``` In [1]: dates = pd.date_range(start=pd.Timestamp('20080102'), periods=3, freq='7D') In [2]: names = ['jane', 'john'] In [3]: index = pd.MultiIndex.from_product([names, dates], names=['name', 'date']) In [4]: df = pd.DataFrame(index=index, data={'best_score': [1, 2, 3, None, 5, 6]}) In [5]: df Out[5]: best_score name date jane 2008-01-02 1 2008-01-09 2 2008-01-16 3 john 2008-01-02 NaN 2008-01-09 5 2008-01-16 6 In [7]: new_dates = [pd.Timestamp('20080101'), pd.Timestamp('20080117')] In [8]: new_index = pd.MultiIndex.from_tuples([('jane', new_dates[1]), ('john', new_dates[0]), ('john', new_dates[1])], names=['name', 'date']) In [9]: df.reindex(new_index, method='ffill') Out[9]: best_score name date jane 2008-01-17 3 john 2008-01-01 3 2008-01-17 6 ``` Clearly `john`'s score on `2008-01-01` is not 3, it's NaN. What would be great (ignoring the awful argument name) is something like: ``` df.reindex(new_index, method='ffill', fill_group_level=['name']) best_score name date jane 2008-01-17 3 john 2008-01-01 NaN 2008-01-17 6 ``` This generalizes to indexes with more than two levels. In effect, it amounts to being able to specify a set of boundaries for the ffill/bfill based on changes in level values. I don't think this can be done in a straightforward way with a `groupby(level='name')` because the values of the index in the second level are not the same for every group.
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10,348
DOC: fix docstrings
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0
2015-06-13T13:49:36Z
2015-06-13T13:49:53Z
2015-06-13T13:49:53Z
MEMBER
null
Some errors in newly build docstrings + add highlights in release notes. This is applied on the online docs, but can be merged normally now
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ENH: unit of measurement / physical quantities
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26
2015-06-13T16:24:37Z
2021-08-31T09:20:59Z
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quantities related xref #2494 xref #1071 custom meta-data xref #2485 It would be very convenient if unit support could be integrated into pandas. Idea: pandas checks for the presence of a unit-attribute of columns and - if present - uses it - with 'print' to show the units e.g. below the column names - to calculate 'under the hood' with these units similar to the example below For my example I use the module pint and add an attribute 'unit' to columns (and a 'title'...). Example: ``` python from pandas import DataFrame as DF from pint import UnitRegistry units = UnitRegistry() class ColumnDescription(): '''Column description with additional attributes. The idea is to use this description to be able to add unit and title attributes to a column description in one step. A list of ColumnDescriptions is than used as argument to DataFrame() with unit support. ''' def __init__(self, name, data, title = None, unit = None): ''' Args: name (str): Name of the column.. data (list): List of the column data. title (str): Title of the column. Defaults to None. unit (str): Unit of the column (see documentation of module pint). Defaults to None. ''' self.data = data '''(list): List of the column data.''' self.name = name '''(str): Name of the column, naming convention similar to python variables. Used to access the column with pandas syntax, e.g. df['column'] or df.column. ''' self.title = title '''(str): Title of the column. More human readable than the 'name'. E.g.: Title: 'This is a column title'. name: 'column_title'. ''' self.unit = unit '''Unit of the column (see module pint). Intended to be used in calculations involving different columns. ''' class DataFrame(DF): '''Data Frame with support for ColumnDescriptions (e.g. unit support). 1. See documentation of pandas.DataFrame. 2. When used with ColumnDescriptions supports additional column attributes like title and unit. ''' def __init__(self, data, title = None): ''' Args: data (list or dict): 1. Dict, as in documentation of DataFrame 2. List of the column data (of type ColumnDescription). title (str): Title of the data frame. Defaults to None. ''' if isinstance(data, list): if isinstance(data[0], ColumnDescription): d = {} for column in data: d[column.name] = column.data super(DataFrame, self).__init__(d) for column in data: self[column.name].title = column.title self[column.name].unit = column.unit self.title = title else: super(DataFrame, self).__init__(data) if __name__ == '__main__': data = [ ColumnDescription('length', [1, 10], title = 'Length in meter', unit = 'meter'), ColumnDescription('time', [10, 1], title = 'Time in s', unit = 's') ] d = {'length':[1, 10], 'time': [10, 1]} df = DataFrame(d) print 'standard df' print df df = DataFrame(data) print '\n' + 'new df' print df ####use of dimensions#### # pint works with numpy arrays # df[name] is currently not working with pint, but would be I think # it would be a real enhancement if it would... test = df.as_matrix(['length']) * units(df['length'].unit) / \ (df.as_matrix(['time']) * units(df['time'].unit)) print '\n' + 'unit test' print test print '\n' + 'magnitude' print test.magnitude print '\n' + 'dimensionality' print test.dimensionality ```
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10,350
BUG: frequencies.get_freq_code raises an error against offset with n != 1
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2
2015-06-13T22:54:48Z
2015-06-20T02:09:55Z
2015-06-15T12:57:04Z
MEMBER
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Fixed a `frequencies.get_freq_code` raises `ValueError` against offset instance with n!=1. This is needed for #7832. ``` # OK >>> import pandas.tseries.frequencies as f >>> f.get_freq_code(('D', 3)) (6000, 3) >>> import pandas.tseries.offsets as offsets >>> f.get_freq_code(offsets.Day()) (6000, 1) # NG! >>> f.get_freq_code(offsets.Day(3)) ValueError: Unknown freqstr: 3D ``` Also, added docstrings and tests which make frequencies behavior understandable.
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10,351
Respect numpy fixed length strings
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8
2015-06-14T11:59:30Z
2018-07-03T18:03:00Z
2015-06-17T10:47:18Z
NONE
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pandas converts all strings to 'O' columns. For consistency and writing to files it would be good if pandas respects numpy fixed length strings. ``` import numpy as np import pandas as pd df = pd.DataFrame({'date': np.array(['2015-10-10', '2015-01-01'], dtype='S10')}) print(df.date.dtype) ``` Results in `dtype('O')` and not `dtype('S10')`
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CI: run doc-tests always
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API: Add pipe method to GroupBy objects
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6
2015-06-14T17:32:37Z
2017-10-18T10:30:16Z
2017-10-18T10:30:16Z
NONE
null
Extend the new "pipe" protocol to GroupBy objects to allow for piping of a wider class of functions. Currently, one can only create pipes that chain together objects inheriting from NDFrame. But the concept of piping is general and could be extended to other pandas objects, specifically anything inheriting from GroupBy. The use case is to write pipe that allow one to freely transform back-and-forth between NDFrames and GroupBy objects. Example: ``` df = DataFrame({A: [...], B: [...]}) def f(dfgb): return dfgb['B'].value_counts() def g(srs): return srs * 2 grouped = df.groupby('A') grouped.pipe(f).pipe(g) ``` Note that these transformations are transformations are - GroupBy -> Series - Series -> Series and the chain seamlessly switches from a GroupBy.pipe to a NDFrame.pipe There are a few ways to implement this. A simple way is to break out the core functionality of "pipe" into a pure function and then to call that function in any method implementation of pipe. Another way is to think of piping as a mix-in trait, put it as a method in a base class, and then mix that base class into any class that wants to implement pipe-ability. I have no strong preference between these options, and I'm open to other implementations that may be more inline with Pandas' design goals or the long-term vision of the "pipe" concept. A strawman implementation of the first implementation suggestion can be found here: https://github.com/pydata/pandas/compare/master...ghl3:groupby-pipe CC @TomAugspurger @shoyer
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10,354
BUG: closes bug in apply when function returns categorical
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1
2015-06-15T02:53:41Z
2015-08-22T11:33:42Z
2015-06-15T10:44:29Z
CONTRIBUTOR
null
closes https://github.com/pydata/pandas/issues/9573
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10,355
BUG: STD modifies groupby target column when as_index=False
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11
2015-06-15T08:14:27Z
2020-05-19T12:56:49Z
2020-05-19T12:56:49Z
NONE
null
xref #14547 for other tests In pandas 0.16.2 (and already in 0.16.0), using std() for aggregation after a groupby( 'my_column', as_index=False) modifies 'my_column' by taking its sqrt(). Example: ``` python df = pandas.DataFrame({ 'a' : [1,1,1,2,2,2,3,3,3], 'b' : [1,2,3,4,5,6,7,8,9], }) df.groupby('a',as_index=False).std() Out[5]: a b 0 1.000000 1 1 1.414214 1 2 1.732051 1 ``` The square root values of 'a' are returned instead of 1, 2, 3. ## INSTALLED VERSIONS commit: None python: 2.7.9.final.0 python-bits: 32 OS: Windows OS-release: 7 machine: AMD64 processor: Intel64 Family 6 Model 45 Stepping 7, GenuineIntel byteorder: little LC_ALL: None LANG: fr_CH pandas: 0.16.2 nose: 1.3.4 Cython: 0.22 numpy: 1.9.2 scipy: 0.15.1 statsmodels: None IPython: 3.0.0 sphinx: 1.3.1 patsy: 0.3.0 dateutil: 2.4.0 pytz: 2015.2 bottleneck: None tables: 3.1.1 numexpr: 2.4 matplotlib: 1.4.3 openpyxl: None xlrd: 0.9.3 xlwt: None xlsxwriter: 0.7.1 lxml: None bs4: 4.3.2 html5lib: None httplib2: None apiclient: None sqlalchemy: 0.9.9 pymysql: None psycopg2: None
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10,356
Code sample in pandas/core/categorical.py
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3
2015-06-15T09:46:16Z
2015-08-19T21:39:47Z
2015-08-19T21:39:47Z
NONE
null
The code sample in pandas/core/categorical.py needs a minor correction. In https://github.com/pydata/pandas/blob/master/pandas/core/categorical.py#L186: ``` >>> a = Categorical(['a','b','c','a','b','c'], ['c', 'b', 'a']) >>> a.min() ``` Shouldn't a.is_ordered() come before a.min() ?
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10,357
TST: run doc-tests
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2
2015-06-15T10:32:42Z
2015-06-15T10:41:18Z
2015-06-15T10:41:18Z
CONTRIBUTOR
null
need to add the option `--with-doctest` in `ci/script.py` to run doc tests. Lots of these fail at them moment though. We could run add these incrementally as the doc tests are fixed though, eg add lines line `nosetests --with-doctest pandas/core` after the main tester
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MDExOlB1bGxSZXF1ZXN0Mzc2ODUzOTE=
10,358
Fixing the code sample in pandas/core/categorical.py
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2015-06-15T12:19:49Z
2015-08-19T21:40:11Z
2015-08-19T21:40:10Z
NONE
null
Fix for #10356
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10,359
DOC: consistent imports (GH9886) part III
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0
2015-06-15T14:58:10Z
2015-06-23T12:43:04Z
2015-06-23T12:42:59Z
MEMBER
null
Further work on #9886
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BUG: _NDFrameIndexer._setitem_with_indexer() doesn't handle MultiIndex and mixed types properly
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6
2015-06-15T16:59:25Z
2019-10-23T04:03:32Z
2019-10-23T04:03:31Z
CONTRIBUTOR
null
Compare the behavior of the homogeneously typed `panel1` with that of the heterogeneously types `panel2`: ``` In [1]: import pandas as pd In [29]: panel1 = pd.Panel([[[0., 0.]], [[0., 0.]]], items=['A', 'B'], minor_axis=['x', 'y']) In [30]: panel2 = pd.Panel([[[0., 0.]], [['o', 'o']]], items=['A', 'B'], minor_axis=['x', 'y']) In [33]: panel1['C'] = 0. In [34]: panel2['C'] = 0. In [35]: panel1.dtypes Out[35]: A float64 B float64 C float64 dtype: object In [36]: panel2.dtypes Out[36]: A object B object C float64 dtype: object In [37]: panel1.loc['C', 0, :] = [5, 6] In [38]: panel1['C'] Out[38]: x y 0 5 6 In [39]: panel2.loc['C', 0, :] = [5, 6] --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-39-57fe49e884e4> in <module>() ----> 1 panel2.loc['C', 0, :] = [5, 6] C:\Python34\lib\site-packages\pandas\core\indexing.py in __setitem__(self, key, value) 116 def __setitem__(self, key, value): 117 indexer = self._get_setitem_indexer(key) --> 118 self._setitem_with_indexer(indexer, value) 119 120 def _has_valid_type(self, k, axis): C:\Python34\lib\site-packages\pandas\core\indexing.py in _setitem_with_indexer(self, indexer, value) 460 461 if len(labels) != len(value): --> 462 raise ValueError('Must have equal len keys and value ' 463 'when setting with an iterable') 464 ValueError: Must have equal len keys and value when setting with an iterable In [41]: pd.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.4.3.final.0 python-bits: 64 OS: Windows OS-release: 7 machine: AMD64 processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel byteorder: little LC_ALL: None LANG: None pandas: 0.16.1 nose: 1.3.6 Cython: 0.22 numpy: 1.9.1 scipy: None statsmodels: None IPython: 3.1.0 sphinx: None patsy: 0.3.0 dateutil: 2.4.2 pytz: 2015.4 bottleneck: 0.8.0 tables: 3.1.1 numexpr: 2.4 matplotlib: 1.4.3 openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: None httplib2: None apiclient: None sqlalchemy: 1.0.5 pymysql: None psycopg2: None ``` Pandas 0.16.2 exhibits the same error.
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ERROR: test_creating_and_reading_multiple_sheets (pandas.io.tests.test_excel.ExcelReaderTests)
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2
2015-06-15T20:01:00Z
2016-11-11T11:16:23Z
2016-11-11T11:16:20Z
CONTRIBUTOR
null
``` ====================================================================== ERROR: test_creating_and_reading_multiple_sheets (pandas.io.tests.test_excel.ExcelReaderTests) ---------------------------------------------------------------------- Traceback (most recent call last): File "/var/tmp/portage/dev-python/pandas-0.16.2/work/pandas-0.16.2-python3_3/lib/pandas/io/tests/test_excel.py", line 474, in test_creating_and_reading_multiple_sheets for sheetname, df in dfs.iteritems(): AttributeError: 'dict' object has no attribute 'iteritems' ---------------------------------------------------------------------- ```
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MDU6SXNzdWU4ODUzMTE5OA==
10,362
Improve Documentation for pandas.Series.interpolate as to how index is used
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0
2015-06-15T20:27:20Z
2015-08-07T10:54:07Z
2015-08-07T10:54:07Z
CONTRIBUTOR
null
From the docs: > ‘linear’: ignore the index and treat the values as equally spaced. default > ‘index’, ‘values’: use the actual numerical values of the index but the remaining types, for example ‘quadratic’, ‘cubic’, ‘barycentric’, `krogh`, don't specify whether they assume equal spacing or use the index values. I believe that they DO use the index values.
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88,536,091
MDU6SXNzdWU4ODUzNjA5MQ==
10,363
Inconsistent Series/scalar comparison behavior based upon the scalar's type
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4
2015-06-15T20:46:32Z
2015-06-15T21:08:49Z
2015-06-15T21:01:34Z
NONE
null
It's pretty unexpected that the behavior of `x <= pd.Series(...)` depends on whether `x` is a python float or a `np.float64`: ``` python >>> import pandas as pd >>> import numpy as np >>> 5 <= pd.Series(range(10)) 0 False 1 False 2 False 3 False 4 False 5 True 6 True 7 True 8 True 9 True dtype: bool >>> np.float64(5) <= pd.Series(range(10)) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python2.7/dist-packages/pandas/core/ops.py", line 588, in wrapper if len(self) != len(other): TypeError: len() of unsized object >>> pd.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 2.7.3.final.0 python-bits: 64 OS: Linux OS-release: 3.2.0-54-virtual machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 pandas: 0.16.2 nose: 1.3.3 Cython: None numpy: 1.9.2 scipy: 0.14.0 statsmodels: 0.5.0 IPython: 0.12.1 sphinx: None patsy: 0.3.0 dateutil: 2.2 pytz: 2014.4 bottleneck: None tables: None numexpr: None matplotlib: 1.3.1 openpyxl: 1.8.6 xlrd: 0.9.3 xlwt: None xlsxwriter: None lxml: 3.3.2 bs4: None html5lib: None httplib2: 0.7.2 apiclient: None sqlalchemy: None pymysql: None psycopg2: 2.5.2 (dt dec pq3 ext) >>> ```
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10,364
BUG/DOC: DataFrame.values return type when uint64 is mixed with signed int types
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8
2015-06-16T12:44:52Z
2017-02-18T17:33:32Z
2017-02-18T12:03:24Z
CONTRIBUTOR
null
`DataFrame.values`' doc mentions type promotion but leaves out that when `uint64` is mixed with signed `int` types, the return type is `int64`, which can be surprising. Perhaps consider returning `dtype(object)` in `internals._interleaved_dtype` when `uint64` and signed `int`s are present?
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88,754,952
MDU6SXNzdWU4ODc1NDk1Mg==
10,365
Improve documentation for piecewise_polynomial interpolation
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3
2015-06-16T15:09:18Z
2021-03-09T17:00:02Z
2021-03-09T17:00:01Z
CONTRIBUTOR
null
Using piecewise_polynomial results in `ZeroDivisionError`. I assume there is something else that needs to be passed (not `order`) to make this work: ``` python test = pd.DataFrame({'a':range(1000)}) test.ix[3] = np.nan test.interpolate(method='piecewise_polynomial') ``` ``` py-tb ZeroDivisionError Traceback (most recent call last) <ipython-input-51-ae8bbf1d5e8d> in <module>() 1 test = pd.DataFrame({'a':range(1000)}) 2 test.ix[3] = np.nan ----> 3 test.interpolate(method='piecewise_polynomial') /Users/alex/.virtualenvs/work/lib/python2.7/site-packages/pandas/core/generic.pyc in interpolate(self, method, axis, limit, inplace, downcast, **kwargs) 2891 inplace=inplace, 2892 downcast=downcast, -> 2893 **kwargs) 2894 if inplace: 2895 if axis == 1: /Users/alex/.virtualenvs/work/lib/python2.7/site-packages/pandas/core/internals.pyc in interpolate(self, **kwargs) 2490 2491 def interpolate(self, **kwargs): -> 2492 return self.apply('interpolate', **kwargs) 2493 2494 def shift(self, **kwargs): /Users/alex/.virtualenvs/work/lib/python2.7/site-packages/pandas/core/internals.pyc in apply(self, f, axes, filter, do_integrity_check, **kwargs) 2457 copy=align_copy) 2458 -> 2459 applied = getattr(b, f)(**kwargs) 2460 2461 if isinstance(applied, list): /Users/alex/.virtualenvs/work/lib/python2.7/site-packages/pandas/core/internals.pyc in interpolate(self, method, axis, index, values, inplace, limit, fill_value, coerce, downcast, **kwargs) 770 inplace=inplace, 771 downcast=downcast, --> 772 **kwargs) 773 774 raise ValueError("invalid method '{0}' to interpolate.".format(method)) /Users/alex/.virtualenvs/work/lib/python2.7/site-packages/pandas/core/internals.pyc in _interpolate(self, method, index, values, fill_value, axis, limit, inplace, downcast, **kwargs) 836 837 # interp each column independently --> 838 interp_values = np.apply_along_axis(func, axis, data) 839 840 blocks = [make_block(interp_values, /Users/alex/.virtualenvs/work/lib/python2.7/site-packages/numpy/lib/shape_base.pyc in apply_along_axis(func1d, axis, arr, *args, **kwargs) 89 outshape = asarray(arr.shape).take(indlist) 90 i.put(indlist, ind) ---> 91 res = func1d(arr[tuple(i.tolist())], *args, **kwargs) 92 # if res is a number, then we have a smaller output array 93 if isscalar(res): /Users/alex/.virtualenvs/work/lib/python2.7/site-packages/pandas/core/internals.pyc in func(x) 833 return com.interpolate_1d(index, x, method=method, limit=limit, 834 fill_value=fill_value, --> 835 bounds_error=False, **kwargs) 836 837 # interp each column independently /Users/alex/.virtualenvs/work/lib/python2.7/site-packages/pandas/core/common.pyc in interpolate_1d(xvalues, yvalues, method, limit, fill_value, bounds_error, order) 1687 result[firstIndex:][invalid] = _interpolate_scipy_wrapper( 1688 valid_x, valid_y, new_x, method=method, fill_value=fill_value, -> 1689 bounds_error=bounds_error, order=order) 1690 if limit: 1691 result[violate_limit] = np.nan /Users/alex/.virtualenvs/work/lib/python2.7/site-packages/pandas/core/common.pyc in _interpolate_scipy_wrapper(x, y, new_x, method, fill_value, bounds_error, order) 1747 new_x = new_x.copy() 1748 method = alt_methods[method] -> 1749 new_y = method(x, y, new_x) 1750 return new_y 1751 /Users/alex/.virtualenvs/work/lib/python2.7/site-packages/scipy/interpolate/polyint.pyc in piecewise_polynomial_interpolate(xi, yi, x, orders, der, axis) 925 """ 926 --> 927 P = PiecewisePolynomial(xi, yi, orders, axis=axis) 928 if der == 0: 929 return P(x) /Users/alex/.virtualenvs/work/lib/python2.7/site-packages/scipy/interpolate/polyint.pyc in __init__(self, xi, yi, orders, direction, axis) 727 728 yi0 = np.asarray(yi[slice0]) --> 729 self._set_yi(yi0) 730 731 self.xi = [xi[0]] /Users/alex/.virtualenvs/work/lib/python2.7/site-packages/scipy/interpolate/polyint.pyc in _set_yi(self, yi, xi, axis) 129 "interpolation axis.") 130 --> 131 self._y_axis = (axis % yi.ndim) 132 self._y_extra_shape = yi.shape[:self._y_axis]+yi.shape[self._y_axis+1:] 133 self.dtype = None ZeroDivisionError: integer division or modulo by zero ```
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10,366
Irregular errors when reading certain categorical strings from hdf
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8
2015-06-16T16:33:23Z
2015-08-28T02:29:03Z
2015-08-28T02:29:03Z
CONTRIBUTOR
null
It seems that there is something bad happening when we use certain strings with special characters AND the empty string with categoricals: ``` python # -*- coding: latin-1 -*- import pandas import os examples = [ pandas.Series(['EÉ, 17', '', 'a', 'b', 'c'], dtype='category'), pandas.Series(['EÉ, 17', 'a', 'b', 'c'], dtype='category'), pandas.Series(['', 'a', 'b', 'c'], dtype='category'), pandas.Series(['EE, 17', '', 'a', 'b', 'c'], dtype='category'), pandas.Series(['øü', 'a', 'b', 'c'], dtype='category'), pandas.Series(['Aøü', '', 'a', 'b', 'c'], dtype='category'), pandas.Series(['EÉ, 17', 'øü', 'a', 'b', 'c'], dtype='category') ] def test_hdf(s): f = 'testhdf.h5' if os.path.exists(f): os.remove(f) s.to_hdf(f, 'data', format='table') return pandas.read_hdf(f, 'data') for i, s in enumerate(examples): flag = True e = '' try: test_hdf(s) except Exception as ex: e = ex flag = False print('%d: %s\t%s\t%s' % (i, 'pass' if flag else 'fail', s.tolist(), e)) ``` Results in: ``` 0: fail ['EÉ, 17', '', 'a', 'b', 'c'] Categorical categories must be unique 1: pass ['EÉ, 17', 'a', 'b', 'c'] 2: pass ['', 'a', 'b', 'c'] 3: pass ['EE, 17', '', 'a', 'b', 'c'] 4: pass ['øü', 'a', 'b', 'c'] 5: fail ['Aøü', '', 'a', 'b', 'c'] Categorical categories must be unique 6: pass ['EÉ, 17', 'øü', 'a', 'b', 'c'] ``` Not sure if I am using this incorrectly or if this is actually a corner case.
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10,367
BUG: drop_duplicates drops name(s).
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4
2015-06-16T21:58:38Z
2015-06-23T14:12:49Z
2015-06-23T14:11:23Z
MEMBER
null
Closes #10115. Closes #10116. Based on @seth-p and @jreback 's fix, added tets for datetime-likes.
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10,368
Sparsification of Graphs
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2
2015-06-16T23:21:16Z
2015-06-16T23:34:45Z
2015-06-16T23:31:41Z
CONTRIBUTOR
null
It might be nice to get some functions in NetworkX for sparsifying graphs. cut sparsification and spectral sparsification are two options. For example, http://arxiv.org/abs/0808.4134 Spectral Sparsification of Graphs Daniel A. Spielman, Shang-Hua Teng We introduce a new notion of graph sparsificaiton based on spectral similarity of graph Laplacians: spectral sparsification requires that the Laplacian quadratic form of the sparsifier approximate that of the original. This is equivalent to saying that the Laplacian of the sparsifier is a good preconditioner for the Laplacian of the original. We prove that every graph has a spectral sparsifier of nearly linear size. Moreover, we present an algorithm that produces spectral sparsifiers in time $\softO{m}$, where m is the number of edges in the original graph. This construction is a key component of a nearly-linear time algorithm for solving linear equations in diagonally-dominant matrcies. Our sparsification algorithm makes use of a nearly-linear time algorithm for graph partitioning that satisfies a strong guarantee: if the partition it outputs is very unbalanced, then the larger part is contained in a subgraph of high conductance.
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pandas.read_msgpack(None) returns [78, 111, 110, 101]
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3
2015-06-17T00:23:51Z
2015-07-03T11:58:52Z
2015-07-03T11:58:52Z
NONE
null
It should return an error, as that's what it most likely is. If someone is really sending empty msgpacks out there to transmit [78, 111, 110, 101], they could add a special case.
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CI: use versioneer to have PEP440 versions
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23
2015-06-17T01:11:24Z
2015-07-16T11:44:37Z
2015-07-16T00:08:25Z
CONTRIBUTOR
null
- use versioneer, for PEP440 version strings, closes #9518 - use binstar build - OSX stack testing, closes #7127 This removes all of the original manual-ish logic to create/manage the versioning in `setup.py`, and replaces with `versioneer.py` (bundled inline) to handle correct versioning for release, development, and conda builds. This actually was tricky simply because of the way the tags are setup, they are like: `v0.16.2` which is pretty non-standard, e.g. most would simply have `0.16.0`. This messes with comparision using `LooseVersion` (of course you _can_ replace this tag as versioneer does, its called the `tag_prefix`, but his makes other things require a slightly different method). So bottom line we get PEP compliant versions like this: ``` In [1]: pd.__version__ Out[1]: '0.16.2+10.gc01927b' ``` IOW, 10 commits past the last tag and the git hash. Now also able to build on `anaconda.org` for all platforms (`linux-32,linux-64,OSX,win-32,win-64`). This part is not 100% done, but quite close. The intention is to have testing and artifact building (well at least for say 2.7/3.4) for linux,osx,win that can be directly `conda install pandas -c pandas` to get the dev version.
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10,371
Add note in docs for interpolate that Only `method=linear` interpolation is supported on MultiIndexes.
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2
2015-06-17T03:41:16Z
2015-07-15T23:36:26Z
2015-07-15T23:36:26Z
CONTRIBUTOR
null
Trying to interpolate a DataFrame with MultiIndex: ``` lib/python2.7/site-packages/pandas/core/generic.pyc in interpolate(self, method, axis, limit, inplace, downcast, **kwargs) 2868 2869 if isinstance(self.index, MultiIndex) and method != 'linear': -> 2870 raise ValueError("Only `method=linear` interpolation is supported " 2871 "on MultiIndexes.") 2872 ValueError: Only `method=linear` interpolation is supported on MultiIndexes. ``` add a note [in the docs](http://pandas.pydata.org/pandas-docs/dev/generated/pandas.Series.interpolate.html).
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10,372
ERR: GH9513 NaT methods now raise ValueError
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19
2015-06-17T06:16:37Z
2015-07-17T16:40:41Z
2015-07-17T16:40:41Z
CONTRIBUTOR
null
This is to close #9513. `NaT` methods now raise `ValueError` or return `np.nan`.
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10,373
TST: Make understandable assertion messages
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5
2015-06-17T12:45:49Z
2015-08-10T16:27:46Z
2015-08-10T16:27:46Z
MEMBER
null
I think making assertion messages more understandable helps users who is starting contribution / using `pandas` as their dependencies. I'm considering examples like [power assert](https://github.com/power-assert-js/power-assert), but more focuses what's differences are. If it worth to be discussed, I'll prepare more examples to fix the specifications. ``` df1 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]}, index=['a', 'b']) df2 = pd.DataFrame({'A': [1, 2], 'B': [3, 5]}, index=['a', 'c']) assert_frame_equal(df1, df2) # AssertionError: DataFrames are not equal # [values]: [[1 3] [2 4]] != [[1 3] [2 5]] # [index values] ['a', 'b'] != ['a', 'c'] # [columns]: equal idx1 = pd.Index([1, 2], name='x') idx2 = pd.Index([1, 2], name='y') assert_index_equal(idx1, idx2) # AssertionError: Index are not equal # [values]: equal # [names]: 'x' != 'y' ``` First target should be: - `assert_frame_equal` - `assert_series_equal` - `assert_index_equal`
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10,374
ENH: Add `categorize` method to Categoricals
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4
2015-06-17T13:19:54Z
2018-12-14T18:54:44Z
2018-12-14T18:54:43Z
CONTRIBUTOR
null
I may be missing an existing method here. Didn't see anything though... Given a Categorical ``` python In [10]: s = pd.Series(['A', 'B', 'C', 'A'], dtype='category') In [11]: s Out[11]: 0 A 1 B 2 C 3 A dtype: category Categories (3, object): [A, B, C] ``` And some codes, `new_codes = pd.Series([0, 1, 0, 2], index=[4, 5, 6, 7])`, I'd like to easily categorize the new codes. I think this is essentially ``` python In [22]: catmap = dict(enumerate(s.cat.categories)) In [23]: new_codes.map(catmap).astype('category') Out[23]: 4 A 5 B 6 A 7 C dtype: object ``` That's not quite the same (what if `new_codes` doesn't have every code from `s`, then it's "missing"? need to make sure the categories / codes are identical), but the basic idea is there. --- My use-case here is going from Categorical -> codes -> scikit-learn classifier -> prediction (codes). It'd be nice if the column could easily transform that result back. Naming wise, I'd say `categorize` or `decode`. Should we also add the symmetrical `encode` method for `category` to `code`? If `new_codes` contains a previously unseen `code` I'd say raise, or maybe insert nan (perhaps an option here?)
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PERF: groupby slower than pure numpy (for many aggregation functions)
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2015-06-17T13:28:48Z
2015-06-17T19:49:46Z
2015-06-17T17:05:48Z
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@ml31415 and I have just created/updated an [aggregation package](https://github.com/ml31415/accumarray) which has multiple equivalent implementations: pure python, numpy, pandas, and scipy.weave. As shown on the readme, pandas is slower than a careful numpy implementation for most aggregation functions, and slower than scipy.weave by a fairly wide margin in all cases. The worst functions appear to be `any` and `all`. Note that the times shown include construction of the dataframe, although whatever overhead this incurs cannot be more than the minimum time shown (11ms for `groupby.first()`). Surely pandas should be able to more or less match at least the numpy implementation, and maybe even the weave version (since it looks like pandas is also getting at low level C using cython)? Note that although pandas may end up faster when performing multiple aggregations of the same grouping (e.g. first, last and max), the other implementations also have this potential (admittedly this optimization is yet to be implemented though).
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10,376
ENH: Enable ExcelWriter to construct in-memory sheets
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9
2015-06-17T14:37:38Z
2015-06-20T16:20:16Z
2015-06-20T16:20:11Z
CONTRIBUTOR
null
Add support for StringIO/BytesIO to ExcelWriter Add vbench support for writing excel files Add support for serializing lists/dicts to strings Fix bug when reading blank excel sheets closes #8188 closes #7074 closes #6403 closes #7171
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89,054,745
MDU6SXNzdWU4OTA1NDc0NQ==
10,377
str conversion to and comparison with datetime no longer working
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3
2015-06-17T15:47:57Z
2015-06-17T23:26:21Z
2015-06-17T15:56:38Z
NONE
null
It appears that appending a dataframe with a column containing string representations of dates, to a dataframe containing datetime objects now results in a column of mixed types, previously the strings were converted to datetime. In addition filter operations using strings now no longer match the equivalent datetime objects. This was working in 0.13.1, but no longer works in 0.16.1. # Example ``` import pandas as pd import datetime import pprint data1 = [{'a': 1, 'b': datetime.datetime(2015,06,10)}, {'a': 2, 'b': datetime.datetime(2015,06,11)}] df1 = pd.DataFrame(data1) data2 = [{'a': 3, 'b': "2015-06-10"}, {'a': 4, 'b': "2015-06-11"}] df2 = pd.DataFrame(data2) df = df1.append(df2) pprint.pprint(df) pprint.pprint(df['b']) print("filtered table") df = df[df['b'] == '2015-06-11'] pprint.pprint(df) pprint.pprint(df['b']) Output using 0.13.1 =============== a b 0 1 2015-06-10 1 2 2015-06-11 0 3 2015-06-10 1 4 2015-06-11 [4 rows x 2 columns] 0 2015-06-10 1 2015-06-11 0 2015-06-10 1 2015-06-11 Name: b, dtype: datetime64[ns] filtered table a b 1 2 2015-06-11 1 4 2015-06-11 [2 rows x 2 columns] 1 2015-06-11 1 2015-06-11 Name: b, dtype: datetime64[ns] Output using 0.16.1 =============== a b 0 1 2015-06-10 00:00:00 1 2 2015-06-11 00:00:00 0 3 2015-06-10 1 4 2015-06-11 0 2015-06-10 00:00:00 1 2015-06-11 00:00:00 0 2015-06-10 1 2015-06-11 Name: b, dtype: object filtered table a b 1 4 2015-06-11 1 2015-06-11 Name: b, dtype: object ```
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89,058,144
MDU6SXNzdWU4OTA1ODE0NA==
10,378
Pass through the kwargs to the various interpolators
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0
2015-06-17T16:02:36Z
2015-06-26T18:08:36Z
2015-06-26T18:08:21Z
CONTRIBUTOR
null
Many of the interpolators have additional arguments so why not pass the `kwargs` collected by [`interpolate`](http://pandas.pydata.org/pandas-docs/dev/generated/pandas.Series.interpolate.html) function to the underlying interpolators? For example the [spline](https://github.com/pydata/pandas/blob/821542fb1dfd55b0e0d25e55eb4fee6555d01cbd/pandas/core/common.py#L1737) function can also take a smoothing factor `s`.
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89,078,879
MDExOlB1bGxSZXF1ZXN0Mzc5MjY5MjY=
10,379
Check for size=0 before setting item
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6
2015-06-17T17:38:44Z
2015-06-18T15:43:28Z
2015-06-18T15:36:21Z
CONTRIBUTOR
null
This is a second try at fixing #10193; the first try is in #10194. There is some useful discussion in that PR, so I didn't want to clobber that- not sure of the etiquette of multiple PRs for the same bug?? The discussion around setting values in views on #10194 is separate from the bug itself- I find that setting an item on a newly-constructed empty series with a frequency (thus, no views are involved) raises the same error. I couldn't cherry pick from my old branch easily as some commits contained more code than is necessary for this set of changes, so I have done a new one.
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10,380
Spline Interpolation Should Use InterpolatedUnivariateSpline
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2015-06-17T17:56:47Z
2021-04-18T07:02:26Z
null
CONTRIBUTOR
null
The spline Interpolation should probably use `InterpolatedUnivariateSpline` rather than [`UnivariateSpline`](https://github.com/pydata/pandas/blob/821542fb1dfd55b0e0d25e55eb4fee6555d01cbd/pandas/core/common.py#L1737) as it does now. For some discussion: https://github.com/scipy/scipy/issues/4975#issuecomment-112867854 Related: #10378
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89,088,349
MDU6SXNzdWU4OTA4ODM0OQ==
10,381
BUG: DataFrame.to_hdf doesn't pass along min_itemsize for index
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4
2015-06-17T18:17:28Z
2016-12-11T21:54:59Z
2016-12-11T21:54:59Z
CONTRIBUTOR
null
Unless I'm seeing something wrong ``` python In [21]: df = DataFrame(dict(A = 'foo', B = 'bar'),index=range(5)).set_index("A") In [22]: df.to_hdf('store.h5', 'test', format='table', min_itemsize={'index': 10}) In [23]: store = pd.HDFStore('store.h5') In [24]: store.get_storer('test').table Out[24]: /test/table (Table(5,)) '' description := { "index": StringCol(itemsize=3, shape=(), dflt=b'', pos=0), "values_block_0": StringCol(itemsize=3, shape=(1,), dflt=b'', pos=1)} # <---- I think this should be 10 byteorder := 'irrelevant' chunkshape := (10922,) autoindex := True colindexes := { "index": Index(6, medium, shuffle, zlib(1)).is_csi=False} ``` and FYI this raises (not sure if it should work) ``` python In [25]: df.index.name = 'theindex' In [26]: df.to_hdf('store.h5', 'test2', format='table', min_itemsize={'theindex': 10}) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) ``` Just a report right now... no time.
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MDU6SXNzdWU4OTExODE3OQ==
10,382
Incorrect usage of float16 in algos.pyx
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3
2015-06-17T20:29:14Z
2018-01-12T11:08:28Z
2018-01-12T11:08:27Z
NONE
null
Hi, everyone According to [documentation](http://docs.scipy.org/doc/numpy-dev/reference/c-api.coremath.html#half-precision-functions), `np.float16` should **not** be used in C code as it is right now in pandas/algos.pyx:38 (assigning `np.NINF` to a `np.float16` container in "cython-eventually-converted-to-C" code As a matter of fact, when this value/variable is exposed, it is exposed as `0.0`, and not `-inf`, either in C/C++ while using gdb or even with python. Since `MINfloat6` it is not being used in all pandas code, I recommend eliminating this line and/or commentating the reason. I tried by using `np.float16(np.NINF)` but the problem is that a correct C `-inf` value is **still** being C-assigned to a please-don't-use-C-container. Why is it that this is important to me/I got to find this error? I'm dealing with 3rd party code so I cannot get a configuration that allows me to do this assignment without raising SIGFPE. No pull request? The two possible solutions to me is either using cython-kept-as-python code with just this variable (which would impact performance) or simply not using it at all. I think I don't have the authority for either.
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10,383
Allow passing other arguments to interpolation functions
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9
2015-06-18T05:45:53Z
2015-06-26T18:08:31Z
2015-06-26T18:08:21Z
CONTRIBUTOR
null
Closes #10378
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10,384
ENH: column label filtering via regexes to work for numeric names
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14
2015-06-18T06:59:19Z
2015-07-07T04:17:04Z
2015-07-06T12:01:38Z
CONTRIBUTOR
null
Simple fix to allow regex filtering to work for numeric column labels, e.g. df.filter(regex="[12][34]") closes #10506
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BUG: specifying multiple columns as primary keys broken in io.sql.get_schema
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0
2015-06-18T11:25:27Z
2015-07-03T13:33:49Z
2015-07-03T13:33:49Z
MEMBER
null
See http://stackoverflow.com/questions/30911299/get-schema-multiple-primary-keys I think this still works for the fallback sqlite version, but not for the new sqlalchemy implementation. in `PrimaryKeyConstraint`, the columns should be feeded as separate positional arguments instead of a list (https://github.com/pydata/pandas/blob/master/pandas/io/sql.py#L837). So just needs an unacking `*`.
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89,273,917
MDExOlB1bGxSZXF1ZXN0Mzc5OTg3NDU=
10,386
BUG: fix multiple columns as primary key in io.sql.get_schema (GH10385)
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2
2015-06-18T12:00:03Z
2015-07-03T13:33:49Z
2015-07-03T13:33:49Z
MEMBER
null
Closes #10385
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10,387
BUG: DataFrame.plot raises ValueError when color name is specified by multiple characters
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10
2015-06-18T13:42:29Z
2015-07-01T13:47:12Z
2015-07-01T13:21:04Z
MEMBER
null
Derived from #9894. Passing color name with multiple characters results in `ValueError`. Below is the bahavior of current master. ``` # OK df = pd.DataFrame(np.random.randn(3, 3)) df[0].plot(color='green') # single green line # OK df.plot(color=['green']) # triple green lines # NG df.plot(color='green') # ValueError: to_rgba: Invalid rgba arg "e" # -> This should be triple green lines ``` If passed str can be parsed as both single color and color cycle, following error will be raised. - _"'green' can be parsed as both single color and color cycle. Specify each color using a list like ['green'] or ['g', 'r', 'e', 'e', 'n']"_ Currently, there is no color name which can meet above condition (thus cannot tested). - http://matplotlib.org/examples/color/named_colors.html
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10,388
NaT in MultiIndex throws ValueError when reset_index() is called.
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2
2015-06-18T14:02:14Z
2015-06-20T11:09:44Z
2015-06-20T11:09:44Z
NONE
null
Please see this test case: ``` x = pp.DataFrame({ 'other': ['a', np.nan], 'date': ['2015-03-22', np.nan], 'amount': [2, 3] }) x.date = pp.to_datetime(x.date) ``` | index | amount | date | other | | --- | --- | --- | --- | | 0 | 2 | 2015-03-22 | a | | 1 | 3 | NaT | NaN | The reset_index here then fails with the error below: ``` x.set_index(['other', 'date']).reset_index() ValueError: Converting an integer to a NumPy datetime requires a specified unit ``` pp.version.version '0.16.1' pp.np.version.version '1.7.1'
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10,389
BUG: closes bug in reset_index when index contains NaT
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1
2015-06-19T00:29:40Z
2015-06-20T12:17:35Z
2015-06-20T11:09:45Z
CONTRIBUTOR
null
closes https://github.com/pydata/pandas/issues/10388
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89,445,710
MDU6SXNzdWU4OTQ0NTcxMA==
10,390
BUG: DataFrame with tz-aware data and max(axis=1) returns NaN
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2
2015-06-19T02:08:00Z
2019-01-16T01:36:21Z
2019-01-16T01:36:21Z
NONE
null
I have a dataframe looks like this, and its column 2 is missing: ![image](https://cloud.githubusercontent.com/assets/6269369/8245984/6ff1798c-1669-11e5-88b1-2c27a6b5f2fb.png) When I try to select the max date in each row, I got all NaN in return: ![img2](https://cloud.githubusercontent.com/assets/6269369/8246090/eea64bbc-166a-11e5-9fb3-668a37a2cf3a.png) However, If the dataframe's type is float64, the selection work as expected.
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10,391
groupby fails for pyhon 3 unsortable objects with sort=True
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1
2015-06-19T14:41:08Z
2015-06-20T11:17:01Z
2015-06-20T11:16:53Z
NONE
null
The default groupby behaviour will try to sort every object. But python 3 does not define an ordering on everything and so the method will fail with the default parameters. In python 2 the order of objects is random (the id). ``` python import pandas as pd class A: pass df = pd.DataFrame([[A(), i, i**2] for i in range(10)]) for x in df.groupby(0,1): print(x) ```
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89,633,876
MDU6SXNzdWU4OTYzMzg3Ng==
10,392
BUG: select_column does not preserve the column name
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0
2015-06-19T18:49:33Z
2015-06-23T11:00:14Z
2015-06-23T11:00:14Z
CONTRIBUTOR
null
``` In [4]: df = DataFrame({'A' : np.random.randn(10), 'B' : 'foo'}) In [5]: df.to_hdf('test.h5','df',mode='w',format='table',data_columns=True) # this should have a name of B In [7]: with pd.HDFStore('test.h5') as store: print store.select_column('df','B') ...: 0 foo 1 foo 2 foo 3 foo 4 foo 5 foo 6 foo 7 foo 8 foo 9 foo dtype: object ```
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10,393
Add nlargest/nsmallest for DataFrame
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15
2015-06-19T19:11:50Z
2015-08-04T22:30:40Z
2015-08-04T22:30:32Z
MEMBER
null
Closes #7359
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89,668,758
MDU6SXNzdWU4OTY2ODc1OA==
10,394
moderate to large groupby computations hang in multithreading
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1
2015-06-19T21:53:47Z
2015-06-19T22:57:26Z
2015-06-19T22:57:02Z
NONE
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Since I am running calculations on very large sets of data, I have broken the data into moderate-sized chunks (using MutliIndex) and run the calculation in parallel. I first tried doing this using threading, but ended up using multiprocessing because some calculations 'hung' when I used threading. Here is my system information: ## INSTALLED VERSIONS commit: None python: 2.7.8.final.0 python-bits: 64 OS: Windows OS-release: 7 machine: AMD64 processor: Intel64 Family 6 Model 26 Stepping 4, GenuineIntel byteorder: little LC_ALL: None LANG: None pandas: 0.15.2 nose: 1.3.6 Cython: 0.21 numpy: 1.9.2 scipy: 0.15.1 statsmodels: 0.5.0 IPython: 2.2.0 sphinx: 1.2.3 patsy: 0.3.0 dateutil: 2.4.0 pytz: 2014.10 bottleneck: None tables: 3.1.1 numexpr: 2.3.1 matplotlib: 1.4.0 openpyxl: 1.8.5 xlrd: 0.9.3 xlwt: 0.7.5 xlsxwriter: 0.5.7 lxml: 3.4.0 bs4: 4.3.2 html5lib: None httplib2: None apiclient: None rpy2: None sqlalchemy: 0.9.7 pymysql: None psycopg2: 2.5.3 (dt dec pq3 ext) ## EXAMPLE I have isolated the issue into the following example: (this code block is the setup, I have further explanations with more code snippets following) ``` python import numpy import pandas import threading def test_fn_lock(name,data,lock,output=True): gp = data.groupby(level=1,sort=False) m = gp.mean() if output: print name + ' - before lock' lock.acquire() data.ge(m) lock.release() if output: print name + ' - done' def test_fn_no_lock(name,data,output=True): gp = data.groupby(level=1,sort=False) m = gp.mean() if output: print name + ' - before lock (no lock)' data.ge(m) if output: print name + ' - done' array = ['bar', 'baz', 'foo', 'qux', 'rad','lol','other','again','diff'] dates = pandas.date_range(start='2014-01-01',end='2015-01-01') tuples = [] for a in array: for d in dates: tuples.append((a,d)) index = pandas.MultiIndex.from_tuples(tuples, names=['first', 'second']) cols = [] for i in xrange(100): cols.append("col%s" % i) df = pandas.DataFrame(numpy.random.random((3294,100)),columns=cols,index=index) cols = [] for i in xrange(100): cols.append("col%s" % (i+200)) df2 = pandas.DataFrame(numpy.random.random((3294,100)),columns=cols,index=index) lock = threading.Lock() ``` The grouped function `ge` in `test_fn_lock` and `test_fn_no_lock` is not the only function where an issue occurs, I have also found problems with `sub`, and I assume the issue exists for `mul`, `div`, `mod`, `add`, etc. To demonstrate that `test_fn_lock` and `test_fn_no_lock` have no meaningful difference in performance, I ran each of the functions for each of the DataFrames in the console: ``` python %timeit test_fn_lock('test', df, lock, False) %timeit test_fn_lock('test', df2, lock, False) %timeit test_fn_no_lock('test', df, False) %timeit test_fn_no_lock('test', df,2 False) ``` All of them took approximately the same amount of time. Now to demonstrate the problem: If I run ``` python t = threading.Thread(target=test_fn_lock, args=('thread1', df, lock)) t2 = threading.Thread(target=test_fn_lock, args=('thread2', df2, lock)) t.start() t2.start() ``` both threads finish without issue. But if I run ``` python t = threading.Thread(target=test_fn_no_lock, args=('thread1', df)) t2 = threading.Thread(target=test_fn_no_lock, args=('thread2', df2)) t.start() t2.start() ``` then neither thread ever finishes. While I could put locks around all my calculations, doing so would defeat the purpose of running them in parallel. I think that the above situation needs to at least produce an exception so that the threads stop processing.
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MDU6SXNzdWU4OTY4OTk2Nw==
10,395
bug: interpolate() row-by-row doesn't work when setting inplace=True
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1
2015-06-20T00:20:55Z
2015-06-23T14:00:40Z
2015-06-23T14:00:40Z
NONE
null
``` python # pandas version 0.16.2 import numpy as np import pandas as pd np.random.seed(12345) # simulate some data df = pd.DataFrame(np.random.randn(250, 10), columns=np.arange(1, 11), index=pd.date_range('2015-01-01', periods=250, freq='B')) # set 20% data to NaN sel = np.random.choice([True, False], size=(250, 10), p=[0.2, 0.8]) df.values[sel] = np.nan # row-wise interpolation doesn't work when setting inplace=True df1 = df.copy() df1.interpolate(method='linear', axis=1, inplace=True) # inplace doesn't work import pandas.util.testing as pdt pdt.assert_frame_equal(df, df1) # dropping inplace=True, it works df2 = df.copy() df2 = df2.interpolate(method='linear', axis=1) pdt.assert_frame_equal(df, df2) ```
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MDExOlB1bGxSZXF1ZXN0MzgxNjU0ODc=
10,396
PERF: parse and timedelta ops improvements, #6755
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1
2015-06-20T09:27:26Z
2015-06-22T09:16:22Z
2015-06-22T08:34:19Z
CONTRIBUTOR
null
closes #6755 ``` ------------------------------------------------------------------------------- Test name | head[ms] | base[ms] | ratio | ------------------------------------------------------------------------------- dtype_infer_timedelta64_1 | 7.0527 | 119.7317 | 0.0589 | timedelta_convert_string | 13.3094 | 108.9200 | 0.1222 | timedelta_convert_string_seconds | 18.2626 | 85.0617 | 0.2147 | dtype_infer_timedelta64_2 | 9.4047 | 9.4063 | 0.9998 | timedelta_convert_int | 0.1300 | 0.1260 | 1.0315 | ------------------------------------------------------------------------------- Test name | head[ms] | base[ms] | ratio | ------------------------------------------------------------------------------- Ratio < 1.0 means the target commit is faster then the baseline. Seed used: 1234 Target [da92dc0] : PERF: timedelta and datetime64 ops improvements Base [d8a2f30] : Check for size=0 before setting item Fixes #10193 ```
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BLD: remove support for 3.2, #9118
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3
2015-06-20T14:58:44Z
2015-06-22T09:03:08Z
2015-06-22T08:46:38Z
CONTRIBUTOR
null
closes #9118
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10,398
Sorted dataframe resets after indexing
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2
2015-06-20T16:44:50Z
2016-07-18T11:19:11Z
2016-07-18T11:19:11Z
NONE
null
I found a in issue when sorting followed by indexing in pandas when reset_index() is not used. I'm not sure why but the ordering of the rows reverts back after indexing and hence results in dfList containing dataframes that are ordered differently. reset_index() works and helped to overcome the problem. This is really subtle and rather difficult to troubleshoot. I hope this would be useful for others. currentReadFile = pd.read_table(currentFile) matrix = currentReadFile.sort(['Ref','Var','Before','After']) matrix_out = matrix.ix[:,4:] dfList.append(matrix_out)
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https://api.github.com/repos/pandas-dev/pandas/issues/10399
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https://github.com/pandas-dev/pandas/issues/10399
89,853,497
MDU6SXNzdWU4OTg1MzQ5Nw==
10,399
potential dataframe apply bug
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2
2015-06-21T02:47:33Z
2015-06-22T09:13:45Z
2015-06-22T08:16:30Z
CONTRIBUTOR
null
I seem to have a weird problem with DataFrame.apply(.., axis=1) not matching the axes/columns correctly, ONLY if there's a time column in the dataframe? See details here: http://nbviewer.ipython.org/gist/anonymous/a0bcaa95a48327fadc63 but basically I try to apply this: ``` python def combine_binning_data(row): return tuple(int(row[col]) for col in [0,1,2]) ``` to the dataframe.And if there's another axis with time I get this error: ``` python ValueError: Shape of passed values is (5, 3), indices imply (5, 5) ``` Version: '0.16.2-15-g2fea54a'
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https://api.github.com/repos/pandas-dev/pandas/issues/10400
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https://github.com/pandas-dev/pandas/pull/10400
89,871,354
MDExOlB1bGxSZXF1ZXN0MzgxNzk4OTQ=
10,400
BUG: GH10395 bug in DataFrame.interpolate with axis=1 and inplace=True
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2015-06-21T06:24:00Z
2015-06-23T14:00:48Z
2015-06-23T14:00:40Z
CONTRIBUTOR
null
To close #10395.
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10,401
BUG: GH10392 bug where Table.select_column does not preserve column name
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2015-06-21T15:43:36Z
2015-06-23T11:00:19Z
2015-06-23T11:00:14Z
CONTRIBUTOR
null
To close #10392
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89,946,116
MDU6SXNzdWU4OTk0NjExNg==
10,402
density plot on zero series throws exception
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4
2015-06-21T18:52:40Z
2015-07-14T11:53:12Z
2015-06-22T01:56:04Z
NONE
null
``` python pd.Series(np.zeros(4)).to_frame().plot(kind='kde') ``` Executing the above line throws the below exception. ``` LinAlgError Traceback (most recent call last) <ipython-input-118-eac1a90078a2> in <module>() ----> 1 pd.Series(np.zeros(4)).to_frame().plot(kind='kde') ``` pandas version 0.16.2
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