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boxplot fontsize argument only works on x axis (and not on y axis)
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2
2017-01-11T12:40:22Z
2017-01-20T17:12:58Z
2017-01-20T17:12:58Z
CONTRIBUTOR
null
#### Code Sample ```python import pandas as pd df = pd.DataFrame({"a": [0, 0, 0, 1, 1, 1], "b": [1, 2, 3, 4, 5, 6]}) df.boxplot("b", by="a", fontsize=20) ``` #### Problem description The fontsize is only applied to the x axis and not to the y axis tick labels (see figure). ![boxplot](https://cloud.githubusercontent.com/assets/4377312/21848879/4522967a-d803-11e6-908a-becdc1739e40.png) #### Expected Output IMO this argument should change the font size of both axes tick labels. Side note: I've tried to use the new plot API, but I couldn't get the output to match the one shown above using `df.plot.box` (I couldn't figure out how to set the variable to plot in the first place - probably this only works on Series and not on DataFrames). #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit: None python: 3.5.2.final.0 python-bits: 64 OS: Darwin OS-release: 15.6.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: en_US.UTF-8 LANG: en_US.UTF-8 pandas: 0.18.1 nose: 1.3.7 pip: 8.1.2 setuptools: 27.2.0 Cython: 0.24.1 numpy: 1.11.1 scipy: 0.18.1 statsmodels: 0.6.1 xarray: None IPython: 5.1.0 sphinx: 1.4.6 patsy: 0.4.1 dateutil: 2.5.3 pytz: 2016.6.1 blosc: None bottleneck: 1.1.0 tables: 3.2.3.1 numexpr: 2.6.1 matplotlib: 1.5.3 openpyxl: 2.3.2 xlrd: 1.0.0 xlwt: 1.1.2 xlsxwriter: 0.9.3 lxml: 3.6.4 bs4: 4.5.1 html5lib: None httplib2: None apiclient: None sqlalchemy: 1.0.13 pymysql: None psycopg2: None jinja2: 2.8 boto: 2.42.0 pandas_datareader: None </details>
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BUG: groupby.cummin/max changing passed values to nan uncesessarily
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3
2017-01-11T14:50:40Z
2017-01-25T19:05:23Z
2017-01-25T19:05:22Z
CONTRIBUTOR
null
after #15053 is failing. (puzzled why its not actually failing on master though). This is local in osx (3.5), so same as the travis test (though that used numpy 1.10.4), its possible that is the cause. ``` (pandas) bash-3.2$ nosetests pandas/tests/groupby/test_groupby.py -s -m cummin_cummax F ====================================================================== FAIL: test_cummin_cummax (test_groupby.TestGroupBy) ---------------------------------------------------------------------- Traceback (most recent call last): File "/Users/jreback/pandas/pandas/tests/groupby/test_groupby.py", line 5798, in test_cummin_cummax tm.assert_frame_equal(result, expected) File "/Users/jreback/pandas/pandas/util/testing.py", line 1313, in assert_frame_equal obj='DataFrame.iloc[:, {0}]'.format(i)) File "/Users/jreback/pandas/pandas/util/testing.py", line 1154, in assert_series_equal assert_attr_equal('dtype', left, right) File "/Users/jreback/pandas/pandas/util/testing.py", line 878, in assert_attr_equal left_attr, right_attr) File "/Users/jreback/pandas/pandas/util/testing.py", line 1018, in raise_assert_detail raise AssertionError(msg) AssertionError: Attributes are different Attribute "dtype" are different [left]: float64 [right]: int64 ---------------------------------------------------------------------- Ran 1 test in 0.046s FAILED (failures=1) ``` because the inf values are == iNaT, and we convert these on exit from the function as we are using these to mark NaT in all int64, not just for datetimelike. https://github.com/jreback/pandas/commit/2af580638483bce8ca35d32239d28997bdc230c2 fixes, but is kludgey. I pre-check for iNaT an if so, convert to float. A better soln is to pass in the ``is_datetimelike`` flag so that the check could be done internally in cython. IOW, if is_datetimelike==True, then go ahead and treat iNaT as null, otherwise ignore it. Ideally we would actually be able to return a correctly dtyped array in the first place, but the templating code won't allow this, e.g. we have a 1-1 mapping between in-out and they are done a-priori. part of this fix could also remove the need for passing in ``accum`` as its wholly internal to the group by cython functions (as part of this could remove the fused typed, ``numeric`` and replace with templated code).
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BUG: Fix for GH #14848 for groupby().describe() with tuples as the Index
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8
2017-01-11T15:17:59Z
2017-01-13T17:38:20Z
2017-01-13T17:38:20Z
CONTRIBUTOR
null
- [x] closes #14848 - [x ] tests added / passed - [x ] passes ``git diff upstream/master | flake8 --diff`` - [x ] whatsnew entry This isn't the most elegant fix for the bug, but all the tests pass. There is probably a bigger issue to deal with here, which is to support tuples as an `Index` (as opposed to a `MultiIndex`) throughout, but at least this fix clears the bug.
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ENH: Dropping outliers
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20
2017-01-11T17:00:56Z
2020-06-07T10:13:10Z
2020-06-07T10:13:10Z
CONTRIBUTOR
null
Create a new function to remove outliers. http://stackoverflow.com/questions/23199796/detect-and-exclude-outliers-in-pandas-dataframe I find myself using the code from SO quite often to remove outliers in a particular column when preprocessing data and it seems this is a common issue. It would be nice to have a function that operates on a Series to do this automatically. #### Code Sample, a copy-pastable example if possible ```python df = pd.DataFrame(np.random.randn(100, 3)) # from SO answer by tanemaki from scipy import stats df[(np.abs(stats.zscore(df)) < 3).all(axis=1)] # instead, would prefer df.drop_outliers(3) ``` Alternatively, instead of a new function, we could modify .clip(), though I think a new function makes more sense. The implementation could be similar to the SO implementation. If there is agreement that this would be useful and the implementation makes sense, happy to do the PR.
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MDU6SXNzdWUyMDAxNzMwNDg=
15,112
Aggregate with `pd.Series.nunique` in datetime column has weird result
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1
2017-01-11T18:36:54Z
2017-01-11T18:43:30Z
2017-01-11T18:43:11Z
NONE
null
I have `DataFrame`: import pandas as pd import datetime as datetime a = pd.DataFrame({'id': [1, 2, 3, 2], 'my_date': [datetime.datetime(2017, 1, i) for i in range(1, 4)] + [datetime.datetime(2017, 1, 1)], 'num': [2, 3, 1, 4] }) print (a.dtypes) id int64 my_date datetime64[ns] num int64 dtype: object I try aggreagate by `pd.Series.nunique`, but get wrong output for datetimes: grouped_a = a.groupby('id').agg({'my_date': pd.Series.nunique, 'num': pd.Series.nunique}).reset_index() grouped_a.columns = ['id', 'num_unique_num', 'num_unique_my_date'] print (grouped_a) id num_unique_num num_unique_my_date 0 1 1 1970-01-01 00:00:00.000000001 1 2 2 1970-01-01 00:00:00.000000002 2 3 1 1970-01-01 00:00:00.000000001 My solution is use `nunique` which works nice. grouped_a = a.groupby('id').agg({'my_date': 'nunique', 'num': 'nunique'}).reset_index() grouped_a.columns = ['id', 'num_unique_num', 'num_unique_my_date'] print (grouped_a) id num_unique_num num_unique_my_date 0 1 1 1 1 2 2 2 2 3 1 1 But why does not work first solution? Bug? [SO question](http://stackoverflow.com/questions/41597179/pandas-counting-unique-datetime-values-in-group-by-gives-weird-values). print (pd.show_versions()) INSTALLED VERSIONS ------------------ commit: None python: 3.5.1.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: sk_SK LOCALE: None.None pandas: 0.19.2+0.g825876c.dirty nose: 1.3.7 pip: 8.1.1 setuptools: 20.3 Cython: 0.23.4 numpy: 1.11.0 scipy: 0.17.0 statsmodels: 0.6.1 xarray: None IPython: 4.1.2 sphinx: 1.3.1 patsy: 0.4.0 dateutil: 2.5.1 pytz: 2016.2 blosc: None bottleneck: 1.0.0 tables: 3.2.2 numexpr: 2.5.1 matplotlib: 1.5.1 openpyxl: 2.3.2 xlrd: 0.9.4 xlwt: 1.0.0 xlsxwriter: 0.8.4 lxml: 3.6.0 bs4: 4.4.1 html5lib: 0.999 httplib2: None apiclient: None sqlalchemy: 1.0.12 pymysql: None psycopg2: None jinja2: 2.8 boto: 2.39.0 pandas_datareader: 0.2.1 None
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15,113
DEPR: deprecate .ix in favor of .loc/.iloc
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15
2017-01-12T01:02:27Z
2017-01-18T16:12:01Z
2017-01-18T16:12:01Z
CONTRIBUTOR
null
closes #14218 closes #15116 This shows a pretty big deprecation message, though its instructive. ``` In [1]: df = pd.DataFrame({'A': [1, 2, 3], ...: 'B': [4, 5, 6]}, ...: index=list('abc')) ...: df ...: Out[1]: A B a 1 4 b 2 5 c 3 6 In [2]: df.ix[[0, 2], 'A'] /Users/jreback/miniconda3/envs/pandas/bin/ipython:1: FutureWarning: .ix is deprecated. Please use .loc for label based indexing or .iloc for positional indexing You an do multi-axes indexing like the following >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, index=list('abc')) >>> df A B a 1 4 b 2 5 c 3 6 From prior versions >>> df.ix[[0, 2], 'A'] a 1 c 3 Name: A, dtype: int64 Using .loc >>> df.loc[df.index[[0, 2]], 'A'] a 1 c 3 Name: A, dtype: int64 Using .iloc >>> df.iloc[[0, 2]], df.columns.get_loc('A')] a 1 c 3 Name: A, dtype: int64 ```
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15,114
TST: Add another test for uneven CSV rows
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11
2017-01-12T05:30:30Z
2017-01-12T22:38:24Z
2017-01-12T22:33:49Z
MEMBER
null
Title is self-explanatory. xref #15066. Closes #9549.
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15,115
BUG: Comparisons result in different dtypes for empty DataFrames #15077
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5
2017-01-12T09:50:47Z
2017-01-21T15:32:29Z
2017-01-21T15:32:28Z
CONTRIBUTOR
null
- [x] closes #15077 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff``
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15,116
Reindex versus ix gotchas documentation text does not match example code
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2
2017-01-12T14:20:02Z
2017-01-18T16:12:01Z
2017-01-18T16:12:00Z
NONE
null
In the gotchas documentation, in the section "Reindex versus ix gotchas", there is the following example: ```python In [28]: s = pd.Series([1, 2, 3], index=['a', 0, 1]) In [29]: s Out[29]: a 1 0 2 1 3 dtype: int64 In [30]: s.ix[[0, 1]] Out[30]: 0 2 1 3 dtype: int64 In [31]: s.reindex([0, 1]) Out[31]: 0 2 1 3 dtype: int64 ``` The example shows both `ix` and `reindex` behaving the same. The subsequent text, however, states that they are expected to give different results: > Because the index in this case does not contain solely integers, `ix` falls back on integer indexing. By contrast, `reindex` only looks for the values passed in the index, thus finding the integers `0` and `1`. I am not sure if the indexing behaviour is correct and the documentation is wrong, or vice versa. It appears that in this case `ix` does not fall back on integer indexing while the text states that it does.
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200,388,019
MDExOlB1bGxSZXF1ZXN0MTAxMjc0Mjk0
15,117
BUG: Fix to_json lines with escaped characters
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8
2017-01-12T14:56:47Z
2017-01-13T20:25:17Z
2017-01-13T20:10:08Z
CONTRIBUTOR
null
Updates existing to_json methodology by adding is_escaping variable, which ensures escaped chars are handled correctly. Bug description: A simple check of whether the prior char is a backslash is insufficient because the backslash may itself be escaped. A test is also included (previously included in #14693). xref #14693 xref #15096 - [x] closes #15096 (#14693 was previously closed by author) - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` (used ``pep8radius master --diff`` instead) - [x] whatsnew entry (does not seem necessary, but let me know if I should add)
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15,118
API: Return type of DataFrame.asof
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2
2017-01-12T15:32:34Z
2017-01-26T12:37:53Z
2017-01-26T12:37:53Z
CONTRIBUTOR
null
#### Code Sample, a copy-pastable example if possible ```python # Your code here In [135]: df = pd.DataFrame({'a': range(10), 'b': range(10)}, index=pd.date_range('2014-01-01', periods=10)) In [136]: df.asof('2014-01-15') Out[136]: a 9 b 9 Name: 2014-01-10 00:00:00, dtype: int32 In [137]: df.asof('2013-01-15') Out[137]: nan ``` #### Problem description I got tripped up by `[137]` returning a scalar - it makes sense for a `Series`, but not sure it does with a `DataFrame` lookup? So maybe it should instead return an empty Series, e.g. ``` In [138]: df.asof('2013-01-15') Out[138]: a NaN b NaN dtype: float64 ``` #### Output of ``pd.show_versions()`` <details> # Paste the output here pd.show_versions() here INSTALLED VERSIONS ------------------ commit: None python: 3.5.2.final.0 python-bits: 64 OS: Windows OS-release: 7 machine: AMD64 processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel byteorder: little LC_ALL: None LANG: None LOCALE: None.None pandas: 0.19.0 nose: 1.3.7 pip: 8.1.2 setuptools: 23.0.0 Cython: 0.24.1 numpy: 1.11.2 scipy: 0.18.1 statsmodels: 0.6.1 xarray: 0.8.2 IPython: 5.1.0 sphinx: 1.3.1 patsy: 0.4.1 dateutil: 2.5.3 pytz: 2016.4 blosc: None bottleneck: 1.1.0 tables: 3.2.2 numexpr: 2.6.1 matplotlib: 1.5.3 openpyxl: 2.3.2 xlrd: 1.0.0 xlwt: 1.1.2 xlsxwriter: 0.9.2 lxml: 3.6.0 bs4: 4.4.1 html5lib: 0.999 httplib2: 0.9.2 apiclient: 1.5.3 sqlalchemy: 1.0.13 pymysql: None psycopg2: None jinja2: 2.8 boto: 2.40.0 pandas_datareader: 0.2.1 </details>
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200,417,379
MDU6SXNzdWUyMDA0MTczNzk=
15,119
Inconsistent parsing for timestamp with timezone with read_sql_query
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6
2017-01-12T16:40:11Z
2018-12-31T14:53:40Z
2018-12-31T14:53:40Z
CONTRIBUTOR
null
#### Code Sample, a copy-pastable example if possible I'm creating a test table in postgresql as follows: ```sql CREATE TABLE test(date timestamptz); INSERT INTO test VALUES('2014-10-16 07:50:49-04'),('2014-11-06 07:42:06-05') ``` Notice that the dates are across the dst date, so that they have different offsets to utc. ```python df1 = pd.read_sql_query("SELECT date FROM test", engine, index_col='date') df2 = pd.read_sql_query("SELECT date FROM test WHERE date<='2014-10-17'", engine, index_col='date') df2bis = pd.read_sql_query("SELECT date FROM test WHERE date<='2014-10-17'", engine, index_col='date', parse_dates=['date']) df3 = pd.read_sql_query("SELECT date FROM test", engine, index_col='date', parse_dates=['date']) ``` #### Problem description ``` In [159]: df1.index Out[159]: Index([2014-10-16 07:50:49-04:00, 2014-11-06 07:42:06-05:00], dtype='object', name='date') In [160]: df2.index Out[160]: DatetimeIndex(['2014-10-16 11:50:49+00:00'], dtype='datetime64[ns, UTC]', name='date', freq=None) In [162]: df2bis.index Out[162]: DatetimeIndex(['2014-10-16 11:50:49+00:00'], dtype='datetime64[ns, UTC]', name='date', freq=None) In [163]: df3.index Out[163]: DatetimeIndex(['2014-10-16 11:50:49', '2014-11-06 12:42:06'], dtype='datetime64[ns]', name='date', freq=None) ``` #### Expected Output The current behavior if I understand correctly is that if there is only one timezone offset: timestamps are parsed automatically to timezone aware timestamps. If dates are across different offsets, they are not parsed automatically. We can force parsing using parse_dates, but then they are not timezone aware. I can make the conversion using tz_localize, but it's not very consistent. I would expect to have everything parsed automatically to timezone aware timestamps. This is related to issue #7364. If there is a consensus on what should be done, I can try to make the required changes to make it more regular. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit: None python: 3.6.0.final.0 python-bits: 64 OS: Linux OS-release: 4.8.13-1-ARCH machine: x86_64 processor: byteorder: little LC_ALL: None LANG: en_US.utf8 LOCALE: en_US.UTF-8 pandas: 0.19.2 nose: 1.3.7 pip: 9.0.1 setuptools: 33.0.0 Cython: 0.25.2 numpy: 1.11.2 scipy: 0.18.1 statsmodels: 0.6.1 xarray: None IPython: 5.1.0 sphinx: 1.5.1 patsy: 0.4.1 dateutil: 2.6.0 pytz: 2016.10 blosc: None bottleneck: None tables: 3.3.0 numexpr: 2.6.1 matplotlib: 2.0.0rc2 openpyxl: 2.4.1 xlrd: 1.0.0 xlwt: None xlsxwriter: None lxml: 3.7.2 bs4: 4.5.3 html5lib: None httplib2: 0.9.2 apiclient: 1.5.5 sqlalchemy: 1.1.4 pymysql: 0.7.9.None psycopg2: 2.6.2 (dt dec pq3 ext lo64) jinja2: 2.9.4 boto: None pandas_datareader: None </details>
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15,120
BUG: indexing changes to .loc for compat to .ix for several situations
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1
2017-01-12T20:31:40Z
2017-01-12T22:43:12Z
2017-01-12T22:43:12Z
CONTRIBUTOR
null
handle iterator handle NamedTuple .loc retuns scalar selection dtypes correctly, closes #11617 xref #15113
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15,121
Fix typo in docstring
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1
2017-01-12T21:53:39Z
2017-01-12T22:11:11Z
2017-01-12T22:10:59Z
CONTRIBUTOR
null
it -> int
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15,122
API/ENH: read_csv handling of bad lines (too many/few fields)
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6
2017-01-12T22:56:08Z
2021-05-28T16:58:21Z
2021-05-28T16:58:21Z
MEMBER
null
Currently `read_csv` has some ways to deal with "bad lines" (bad in the sense of too many or too few fields compared to the determined number of columns): - by default, it will error for too many fields, and fill with NaNs for too few fields - with `error_bad_lines=false` rows with too many fields will be dropped instead of raising an error (and in that case, `warn_bad_lines` controls to get a warning or not) - with `usecols` you can select certain columns, and in this way deal with rows with too many fields. Some possibilities are missing in this scheme: - "process" bad lines with too many fields, i.e. drop the excessive fields instead of either raising an error or dropping the full row (discussed in #9549) - getting a warning or error with too few fields instead of automatically filling with NaNs (asked for in #9729), or dropping those rows Apart from that, https://github.com/pandas-dev/pandas/issues/5686 makes the request to be able to specify a custom function to process a bad line, to have even more control. In https://github.com/pandas-dev/pandas/issues/9549#issuecomment-76498787 (and surrounding comments) there was some discussion about how to integrate this, and some idea from there from @jreback and @selasley: Provide more fine grained control in a new keyword (and deprecate `error_bad_lines`): ``` bad_lines='error'|'warn'|'skip'|'process' ``` or leave out `'warn'` and keep `warn_bad_lines` to be able to combine a warning with both 'skip' and 'process'. We should further think about whether we can integrate this with the case of too few fields and not only too many. I think it would be nice to have some better control here, but we should think a bit about the best API for this.
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Why does it take so much time to retrieve one row?
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2
2017-01-13T12:49:34Z
2017-01-13T13:40:33Z
2017-01-13T13:40:17Z
NONE
null
```python # data.csv contains 10000*10 records which is depicted as follows: # 1,1,1,1,1,1,1,1,1,1 # 2,2,2,2,2,2,2,2,2,2 # ... # 10000,10000,10000,10000,10000,10000,10000,10000,10000,10000 import pandas as pd import numpy as np import time data = pd.read_csv('data.csv') data_matrix = data.as_matrix() s = time.time() for _ in range(10000): v = data.iloc[_] e = time.time() print('Performance with DataFrame:'+str(e-s)) s = time.time() for _ in range(10000): v = data_matrix[_] e = time.time() print('Performance with Array: '+str(e-s)) #result #performance with DataFrame:3.964857816696167 #performance with array: 0.015623092651367188 ``` #### Problem description As is shown in the coding above,locating an element while given the index takes much much much longer in the DataFrame than in a raw array. In a common sense,it hardly takes any time to locate an element in an raw array or list as the index of that element is given and there is no reason to use a sequential search. However,comparing to the array,the performance is quite lower while retrieving a specific row in a DataFrame.The row-index is already given,the DataFrame should be able to locate the row directly,could it be said that the DataFrame actually start a sequential search? Any alternative methods that works like locating an element in an array? #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS commit: None python: 3.4.4.final.0 python-bits: 64 OS: Windows OS-release: 10 machine: AMD64 processor: Intel64 Family 6 Model 37 Stepping 2, GenuineIntel byteorder: little LC_ALL: None LANG: None LOCALE: None.None pandas: 0.19.1 nose: None pip: 9.0.1 setuptools: 32.0.0 Cython: None numpy: 1.10.2 scipy: 0.16.1 statsmodels: None xarray: None IPython: None sphinx: None patsy: None dateutil: 2.6.0 pytz: 2016.10 blosc: None bottleneck: None tables: None numexpr: None matplotlib: 1.5.0 openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: None httplib2: None apiclient: None sqlalchemy: None pymysql: None psycopg2: None jinja2: None boto: None pandas_datareader: None # </details>
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15,124
BUG: pandas.Series.dt.round inconsistent behaviour on NAT's with different arguments
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1
2017-01-13T15:09:45Z
2017-01-14T18:24:39Z
2017-01-14T18:24:27Z
CONTRIBUTOR
null
- [ ] closes #14940 - [ ] tests added / passed - [ ] passes ``git diff upstream/master | flake8 --diff`` - [ ] whatsnew entry
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Series constructor ignores copy=True when dtype agrees
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2
2017-01-13T16:40:13Z
2017-01-15T16:11:15Z
2017-01-15T16:11:15Z
CONTRIBUTOR
null
#### Code Sample ```python >>> %paste import pandas as pd x = pd.Series([1.0]) y = pd.Series(x, copy=True) z = pd.Series(x, copy=True, dtype=float) x[0] = 2.0 print(x[0]) print(y[0]) print(z[0]) ## -- End pasted text -- 2.0 1.0 2.0 ``` #### Problem description Constructing a Series with copy=True should return a copy of the original, but when the dtype argument agrees with the source dtype, no copy is performed. #### Expected Output ```python 2.0 1.0 1.0 ``` #### Output of ``pd.show_versions()`` <details> commit: None python: 3.5.2.final.0 python-bits: 64 OS: Linux OS-release: 4.1.13-100.fc21.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: C LANG: C LOCALE: None.None pandas: 0.19.2 nose: 1.3.7 pip: 9.0.1 setuptools: 27.2.0 Cython: 0.25.1 numpy: 1.11.2 scipy: 0.18.1 statsmodels: 0.6.1 xarray: None IPython: 4.2.0 sphinx: 1.4.8 patsy: 0.4.1 dateutil: 2.5.3 pytz: 2016.7 blosc: None bottleneck: 1.1.0 tables: 3.3.0 numexpr: 2.6.1 matplotlib: 1.5.3 openpyxl: 2.4.0 xlrd: 1.0.0 xlwt: 1.1.2 xlsxwriter: 0.9.3 lxml: 3.6.4 bs4: 4.5.1 html5lib: None httplib2: None apiclient: None sqlalchemy: 1.1.3 pymysql: None psycopg2: None jinja2: 2.8 boto: 2.43.0 pandas_datareader: None </details>
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15,126
BUG: catch overflow in timestamp + offset operations
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1
2017-01-13T19:08:46Z
2017-01-14T17:02:20Z
2017-01-14T17:02:20Z
CONTRIBUTOR
null
xref https://github.com/statsmodels/statsmodels/issues/3374
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TST: tests.groupby needs to be a package to run tests
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2
2017-01-13T19:34:21Z
2017-01-13T20:21:22Z
2017-01-13T20:21:22Z
CONTRIBUTOR
null
skip parts of cummin test, xref #15109
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15,128
BUG: Fix Series constructor when copy=True
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4
2017-01-13T19:52:18Z
2017-01-15T16:11:16Z
2017-01-15T16:11:15Z
CONTRIBUTOR
null
Updates Series constructor to include copy argument when dtype argument is also provided. Adds tests for copy parameter. xref #15125 - [x] closes #15125 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry
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15,129
ENH: Added 'direction' parameter to merge_asof() (#14887)
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4
2017-01-13T20:57:14Z
2017-01-18T16:08:10Z
2017-01-18T16:08:10Z
CONTRIBUTOR
null
- [x] closes #14887 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry
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15,130
ERR: too strict validation on groupby.rolling with time-aware freq
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0
2017-01-13T21:23:22Z
2017-01-20T12:21:53Z
2017-01-20T12:21:53Z
CONTRIBUTOR
null
http://stackoverflow.com/questions/41642320/efficient-pandas-rolling-aggregation-over-date-range-by-group-python-2-7-windo/41643179?noredirect=1#comment70486923_41643179 ``` In [1]: data = [ ...: ['David', '1/1/2015', 100], ['David', '1/5/2015', 500], ['David', '5/30/2015', 50], ['David', '7/25/2015', 50], ...: ['Ryan', '1/4/2014', 100], ['Ryan', '1/19/2015', 500], ['Ryan', '3/31/2016', 50], ...: ['Joe', '7/1/2015', 100], ['Joe', '9/9/2015', 500], ['Joe', '10/15/2015', 50] ...: ] ...: ...: list_of_vals = [] ...: ...: dates_df = pd.DataFrame(data=data, columns=['name', 'date', 'amount'], index=None) ...: dates_df['date'] = pd.to_datetime(dates_df['date']) ...: ``` This check doesn't need to occur when we are grouping ``` In [7]: dates_df.groupby('name').rolling('180D', on='date')['amount'].sum() --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-7-8896cb99a66a> in <module>() ----> 1 dates_df.groupby('name').rolling('180D', on='date')['amount'].sum() /Users/jreback/pandas/pandas/core/groupby.py in rolling(self, *args, **kwargs) 1148 """ 1149 from pandas.core.window import RollingGroupby -> 1150 return RollingGroupby(self, *args, **kwargs) 1151 1152 @Substitution(name='groupby') /Users/jreback/pandas/pandas/core/window.py in __init__(self, obj, *args, **kwargs) 635 self._groupby.mutated = True 636 self._groupby.grouper.mutated = True --> 637 super(GroupByMixin, self).__init__(obj, *args, **kwargs) 638 639 count = GroupByMixin._dispatch('count') /Users/jreback/pandas/pandas/core/window.py in __init__(self, obj, window, min_periods, freq, center, win_type, axis, on, **kwargs) 76 self.win_type = win_type 77 self.axis = obj._get_axis_number(axis) if axis is not None else None ---> 78 self.validate() 79 80 @property /Users/jreback/pandas/pandas/core/window.py in validate(self) 1030 formatted = self.on or 'index' 1031 raise ValueError("{0} must be " -> 1032 "monotonic".format(formatted)) 1033 1034 from pandas.tseries.frequencies import to_offset ValueError: date must be monotonic ``` This is ok ``` In [9]: dates_df.groupby('name').apply(lambda x: x.rolling('180D', on='date')['amount'].sum()) Out[9]: name David 0 100.0 1 600.0 2 650.0 3 100.0 Joe 7 100.0 8 600.0 9 650.0 Ryan 4 100.0 5 500.0 6 50.0 Name: amount, dtype: float64 ```
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Pandas contains shebang pointing to /usr/bin/python which are problematic
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4
2017-01-14T14:17:13Z
2017-01-15T16:09:21Z
2017-01-15T16:09:02Z
CONTRIBUTOR
null
#### Problem description [this should explain **why** the current behaviour is a problem and why the expected output is a better solution.] We are packaging pandas pre-installed in a virtualenv using a rpm. RPM has a "convenient" feature to automatically scan for script files with shebang and adding those as requirements of the resulting rpm. For more information, see [this blog post](http://www.legendiary.at/2013/03/20/rpmbuild-shebang-auto-required-packages/). Most of the pandas scripts use ````bash #!/usr/bin/env python ```` which rpm resolves to whatever is in the PATH. However, some of the packages point to hard-coded path of python: ````bash $ grep '^#!' **/*.py|grep -v '/usr/bin/env python' pandas/tests/test_config.py:#!/usr/bin/python pandas/tests/test_testing.py:#!/usr/bin/python ```` #### Proposed solution I would propose that the above scripts would be changed to use `#!/usr/bin/env python`. This change would make the rpm process resolve python and bash using whatever is in the path. For example, with CentOS 6, the system python (in `/usr/bin/python`) is python 2.6, although the `python` in PATH could very well be `python2.7` or newer. In our case the virtualenv is activated during the build, and thus `#!/usr/bin/env python` would resolve to the correct interpreter. The additional benefit is that the scripts are more cross-platform. I'm willing to submit a pull requests if you agree with the fix? Numpy issue regarding the same matter: https://github.com/numpy/numpy/issues/8472 Scipy issue regarding the same matter: https://github.com/scipy/scipy/issues/6961
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15,132
pd.read_json(file, lines=True) does not work if json has quotes inside it
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7
2017-01-14T22:02:23Z
2017-01-19T13:54:35Z
2017-01-19T13:54:35Z
NONE
null
#### Code Sample, a copy-pastable example if possible ```python {"errors":["This check-in does not exist, it may have been deleted."]}, {"list":{"id":487004,"description":"foo.”\r\n\r\n* “I am aware that I’m drafting an email responding to a complaint.”\r\n\r\n* “I am aware that I’m wondering who will win.”\r\n\r\nThe great thing about this exercise is that it is generalizable. You practice during the meditation, but then you use it for your own goals during your day to day."..........} INSTALLED VERSIONS ------------------ commit: None python: 2.7.10.final.0 python-bits: 64 OS: Darwin OS-release: 15.6.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: None.None pandas: 0.19.2 nose: None pip: 9.0.1 setuptools: 32.3.1 Cython: None numpy: 1.11.3 scipy: 0.17.1 statsmodels: None xarray: None IPython: None sphinx: None patsy: None dateutil: 2.6.0 pytz: 2016.10 blosc: None bottleneck: None tables: None numexpr: None matplotlib: 1.5.3 openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: None httplib2: None apiclient: None sqlalchemy: None pymysql: None psycopg2: None jinja2: None boto: None pandas_datareader: None ``` #### Problem description List has quotes inside the json data. Expected to read this data line by line, but we get a UnicodeDecodeError at the position of that inner quote in the description #### Expected Output read successful #### Output: Traceback (most recent call last): data = pd.read_json(fileName, lines=True) File "/usr/local/lib/python2.7/site-packages/pandas/io/json.py", line 275, in r ead_json json = u'[' + u','.join(lines) + u']' UnicodeDecodeError: 'ascii' codec can't decode byte 0xe2 in position 4924: ordina l not in range(128)
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15,133
Some problems with list in read_excel()
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2017-01-15T12:32:06Z
2021-05-08T01:07:20Z
null
NONE
null
#### Code Sample, a copy-pastable example if possible ```python train = read_excel( 'Wilson_Schistosome Esophagus_differentially expressed transcripts.xlsx', sheetname=3, header=[0, 1], skip_footer=11) ``` OK ```python train = read_excel( 'Wilson_Schistosome Esophagus_differentially expressed transcripts.xlsx', sheetname=3, header=[0, 1], skip_footer=11, names=[0] + list(range(2, 12))) ``` ValueError: Length of new names must be 1, got 2 ```python train = read_excel('Wilson_Schistosome Esophagus_differentially expressed transcripts.xlsx', sheetname=2, header=[0, 1], skip_footer=6) ``` ValueError: Length of new names must be 1, got 2 ```python train = read_excel('Wilson_Schistosome Esophagus_differentially expressed transcripts.xlsx', sheetname=[1, 3], header=[0, 1], skip_footer=3) ``` ValueError: Length of new names must be 1, got 2 #### Problem description I am trying to read excel file ( https://github.com/ArtyomKaltovich/ikantam/blob/master/lab1/Wilson_Schistosome%20Esophagus_differentially%20expressed%20transcripts.xlsx ). But sometimes pandas does not accept list as parameters for read_excel. #### Expected Output #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit: None python: 3.5.2.final.0 python-bits: 64 OS: Linux OS-release: 4.4.0-21-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 pandas: 0.19.2 nose: None pip: 9.0.1 setuptools: 32.3.1 Cython: None numpy: 1.11.3 scipy: 0.18.1 statsmodels: None xarray: None IPython: None sphinx: None patsy: None dateutil: 2.6.0 pytz: 2016.10 blosc: None bottleneck: None tables: None numexpr: None matplotlib: 1.5.3 openpyxl: None xlrd: 1.0.0 xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: 0.999999999 httplib2: 0.9.2 apiclient: None sqlalchemy: None pymysql: None psycopg2: None jinja2: None boto: None pandas_datareader: None </details>
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15,134
MAINT: remove shebang from test modules
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2
2017-01-15T12:51:44Z
2017-07-11T10:06:15Z
2017-01-15T16:09:03Z
CONTRIBUTOR
null
- [x] closes #15131
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15,135
merge_asof fails when using left_index/right_index with a tolerance parameter
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2017-01-15T12:53:52Z
2017-01-17T13:43:35Z
2017-01-17T13:43:35Z
CONTRIBUTOR
null
#### Problem description Using the DataFrames *trades* and *quotes* from the `merge_asof` [documentation](http://pandas.pydata.org/pandas-docs/version/0.19.0/generated/pandas.merge_asof.html) as an example, there's an issue when using `merge_asof` to merge on indexes using `left_index`/`right_index` (rather than column names) and additionally specifying a tolerance parameter. Admittedly, the `left_index`/`right_index` parameters are not documented for this function (in the prototype description), but they are still valid parameters (and have an example in the docs). It could be seen as inconsistent that they sometimes work as expected and sometimes raise a cryptic error message. For example, first set the 'time' column of each DataFrame as the index: ```python >>> quotes = quotes.set_index('time') >>> trades = trades.set_index('time') ``` Then `pd.merge_asof(trades, quotes, left_index=True, right_index=True, by='ticker')` works exactly as `pd.asof_merge(trades, quotes, on='time', by='ticker')` (as expected). However, `pd.merge_asof(trades, quotes, left_index=True, right_index=True, by='ticker', tolerance=pd.Timedelta('2ms'))` (specifying a tolerance) raises an error: ``` /.../anaconda3/lib/python3.4/site-packages/pandas/tools/merge.py in _get_merge_keys(self) 1147 1148 else: -> 1149 raise MergeError("key must be integer or timestamp") 1150 1151 # validate allow_exact_matches MergeError: key must be integer or timestamp ``` I would have expected it work exactly as `pd.merge_asof(trades, quotes, on='time', by='ticker', tolerance=pd.Timedelta('2ms'))` works. Failing this, an error telling me that I cannot use a tolerance when merging on indexes would be welcome. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit: None python: 3.4.4.final.0 python-bits: 64 OS: Linux OS-release: 3.16.0-4-amd64 machine: x86_64 processor: byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_GB.UTF-8 pandas: 0.19.2 nose: 1.3.4 pip: 9.0.1 setuptools: 31.0.1.post20161215 Cython: 0.23.4 numpy: 1.11.3 scipy: 0.18.1 statsmodels: 0.6.1 xarray: 0.7.2 IPython: 5.1.0 sphinx: 1.2.3 patsy: 0.4.1 dateutil: 2.4.2 pytz: 2015.7 blosc: None bottleneck: None tables: 3.1.1 numexpr: 2.6.1 matplotlib: 1.5.1 openpyxl: 1.8.5 xlrd: 0.9.3 xlwt: None xlsxwriter: 0.6.7 lxml: 3.6.4 bs4: 4.3.2 html5lib: 0.99999 httplib2: None apiclient: None sqlalchemy: 0.9.9 pymysql: None psycopg2: None jinja2: 2.8 boto: 2.36.0 pandas_datareader: None </details>
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ENH: Timedelta isoformat
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16
2017-01-15T13:08:17Z
2017-04-05T02:07:21Z
2017-01-20T12:25:37Z
CONTRIBUTOR
null
Split from https://github.com/pandas-dev/pandas/pull/14904 A few questions before I finish all the docs / tests: The [spec](https://en.wikipedia.org/wiki/ISO_8601#Durations) seems very permissive about what's allowed, so we need to choose - Do we include components pandas can't represent like Years and Months with 0 `(P0Y0M...)` (no) - Do we include components whose value is 0, e.g. `P0DT...` vs. `PT...` (yes) - Do we 0 pad units, e.g. `...T5H` vs `T05H` (no) - Do we strip trailing 0s in the seconds decimal: `...6.000000000S` vs. `6S` (yes) We probably want a reader for this format as well, maybe in the Timedelta constructor. Not sure if I'll have time to get to that before 0.20 . I'd rather prioritize the JSON table schema PR.
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Timedelta to_json with date_format='iso'
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7
2017-01-15T15:56:56Z
2020-05-03T21:13:54Z
2020-05-03T21:13:53Z
CONTRIBUTOR
null
Is this formatting for timdelta columns intended / correct? ```python In [12]: df = pd.DataFrame({"A": pd.date_range("2016", periods=4, freq='D'), ...: "B": pd.timedelta_range('1D', periods=4, freq='D')}) In [13]: df.to_json(date_format='iso') Out[13]: '{"A":{"0":"2016-01-01T00:00:00.000Z","1":"2016-01-02T00:00:00.000Z","2":"2016-01-03T00:00:00.000Z","3":"2016-01-04T00:00:00.000Z"},"B":{"0":"1970-01-02T00:00:00.000Z","1":"1970-01-03T00:00:00.000Z","2":"1970-01-04T00:00:00.000Z","3":"1970-01-05T00:00:00.000Z"}}' ``` So a `pd.Timedelta('1D')` goes to `"1970-01-02T00:00:00.000Z"`. The expected might be a [duration](https://en.wikipedia.org/wiki/ISO_8601#Durations) like `"P1DT0H0M0S"`. It seems to be reusing the logic for datetime columns and treating it as the equivalent int -> iso format. Ideally `to_json` will have a new paramater `timedelta_format` that takes the same values as `date_format`.
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read_fwf 'infer' where first hundred lines differ from other lines
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5
2017-01-16T01:30:38Z
2018-11-26T00:35:45Z
2018-11-26T00:35:45Z
NONE
null
#### Code Sample, a copy-pastable example if possible ``` 1 1 -13.120080 0.229 0.484 -0.378 -0.872 1 2 -1.902843 -0.090 0.256 1.791 0.967 1 3 -22.050698 -0.176 -0.394 0.922 -0.454 1 4 -30.349928 0.081 -0.194 -0.327 -0.981 1 5 -22.204160 -0.168 -0.197 0.984 -0.266 1 6 -28.001753 -0.065 0.597 -0.203 -0.802 1 7 -17.247524 0.108 0.194 0.474 0.774 1 8 -28.014811 0.017 0.994 0.493 0.112 1 9 -13.325491 0.259 0.189 -1.275 0.149 1 10 -10.063621 0.327 0.108 -1.784 0.061 ... 115 18 5.697000 0.391 -0.027 0.252 1.000 115 19 8.324000 -0.283 0.132 0.227 -0.216 115 20 48.451000 0.070 -0.041 0.379 -0.082 115 21 0.146000 0.677 0.031 -0.561 -0.149 115 22 1.443000 -0.706 -0.033 -0.222 0.035 115 23 4.595000 0.654 -0.081 0.774 0.997 115 24 0.146000 -0.677 0.031 0.561 -0.149 115 25 4.595000 0.654 -0.081 0.774 0.997 115 26 6.769000 -0.363 -0.093 -0.298 0.996 115 27 24.157000 -0.280 -0.324 -0.142 -0.946 ``` #### Problem description I have a long [fixed-width file](https://paste.ubuntu.com/23806889/) (>100k lines) that whose head and tail are shown above. I want to read this file with pandas. I figure `pd.read_fwf` is the way to do this. The issue comes up because it reads the first hundred lines, which start with `' 1'` to say "lets start reading at [2]" whereas the last hundred lines start with `115`, so it skips the initial `11` and starts the line with `5`, so I lose data. A couple of approaches to solving this issue come to mind, though I'm sure there are others: - Don't infer until all lines are scanned - Take as an argument the number of lines to be scanned before concluding the format, including the option to scan all (e.g. `infer_from_all`) - Take as an argument which direction to scan -- top to bottom or bottom to top #### Output of ``pd.show_versions()`` <details> commit: None python: 2.7.6.final.0 python-bits: 64 OS: Linux OS-release: 3.13.0-107-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: None.None </details>
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BUG: Allow tolerance when left_index/right_index enabled for merge_asof() (#15135)
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1
2017-01-16T01:50:42Z
2017-01-17T13:43:35Z
2017-01-17T13:43:35Z
CONTRIBUTOR
null
- [x] closes #15135 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry
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15,140
Segmentation fault with `read_csv(io.StringIO("a\na"), float_precision="round_trip")`
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4
2017-01-16T05:16:01Z
2017-01-20T12:31:32Z
2017-01-20T12:31:32Z
CONTRIBUTOR
null
#### Code Sample, a copy-pastable example if possible ```python import io, pandas pandas.read_csv(io.StringIO("a\na"), float_precision="round_trip") ``` The input needs to be at least two lines and must contain non-numerical data. Experienced this problem on Arch Linux with Python 3. #### Problem description *Why is the current behaviour a problem?* (1) I can't parse a CSV file containing text with `round_trip` precision (2) Possible security vulnerability (3) It fills up my hard drive with core dumps #### Expected Output Nothing. #### Actual Output <details> <pre> #0 0x00007ffff7440350 in PyErr_Restore () from /usr/lib/libpython3.6m.so.1.0 #1 0x00007ffff7440a62 in PyErr_FormatV () from /usr/lib/libpython3.6m.so.1.0 #2 0x00007ffff7440b24 in PyErr_Format () from /usr/lib/libpython3.6m.so.1.0 #3 0x00007ffff73c2c60 in PyOS_string_to_double () from /usr/lib/libpython3.6m.so.1.0 #4 0x00007fffe5359a73 in ?? () from /usr/lib/python3.6/site-packages/pandas/parser.cpython-36m-x86_64-linux-gnu.so #5 0x00007fffe53675c6 in ?? () from /usr/lib/python3.6/site-packages/pandas/parser.cpython-36m-x86_64-linux-gnu.so #6 0x00007fffe535c74a in ?? () from /usr/lib/python3.6/site-packages/pandas/parser.cpython-36m-x86_64-linux-gnu.so #7 0x00007fffe537db89 in ?? () from /usr/lib/python3.6/site-packages/pandas/parser.cpython-36m-x86_64-linux-gnu.so #8 0x00007ffff74220b6 in PyCFunction_Call () from /usr/lib/libpython3.6m.so.1.0 #9 0x00007fffe534fe29 in ?? () from /usr/lib/python3.6/site-packages/pandas/parser.cpython-36m-x86_64-linux-gnu.so #10 0x00007fffe536f78e in ?? () from /usr/lib/python3.6/site-packages/pandas/parser.cpython-36m-x86_64-linux-gnu.so #11 0x00007fffe5344cf4 in ?? () from /usr/lib/python3.6/site-packages/pandas/parser.cpython-36m-x86_64-linux-gnu.so #12 0x00007ffff7421ddc in _PyCFunction_FastCallDict () from /usr/lib/libpython3.6m.so.1.0 #13 0x00007ffff740c21f in ?? () from /usr/lib/libpython3.6m.so.1.0 #14 0x00007ffff73cd067 in _PyEval_EvalFrameDefault () from /usr/lib/libpython3.6m.so.1.0 #15 0x00007ffff740ade9 in ?? () from /usr/lib/libpython3.6m.so.1.0 #16 0x00007ffff740bf9a in ?? () from /usr/lib/libpython3.6m.so.1.0 #17 0x00007ffff740c303 in ?? () from /usr/lib/libpython3.6m.so.1.0 #18 0x00007ffff73cd067 in _PyEval_EvalFrameDefault () from /usr/lib/libpython3.6m.so.1.0 #19 0x00007ffff740aaa1 in ?? () from /usr/lib/libpython3.6m.so.1.0 #20 0x00007ffff740bf9a in ?? () from /usr/lib/libpython3.6m.so.1.0 #21 0x00007ffff740c303 in ?? () from /usr/lib/libpython3.6m.so.1.0 #22 0x00007ffff73cd067 in _PyEval_EvalFrameDefault () from /usr/lib/libpython3.6m.so.1.0 #23 0x00007ffff740bd4a in ?? () from /usr/lib/libpython3.6m.so.1.0 #24 0x00007ffff740c303 in ?? () from /usr/lib/libpython3.6m.so.1.0 #25 0x00007ffff73cd067 in _PyEval_EvalFrameDefault () from /usr/lib/libpython3.6m.so.1.0 #26 0x00007ffff740ade9 in ?? () from /usr/lib/libpython3.6m.so.1.0 #27 0x00007ffff740bf9a in ?? () from /usr/lib/libpython3.6m.so.1.0 #28 0x00007ffff740c303 in ?? () from /usr/lib/libpython3.6m.so.1.0 #29 0x00007ffff73cde87 in _PyEval_EvalFrameDefault () from /usr/lib/libpython3.6m.so.1.0 #30 0x00007ffff740c757 in PyEval_EvalCodeEx () from /usr/lib/libpython3.6m.so.1.0 #31 0x00007ffff73ccd4b in PyEval_EvalCode () from /usr/lib/libpython3.6m.so.1.0 #32 0x00007ffff74ae112 in ?? () from /usr/lib/libpython3.6m.so.1.0 #33 0x00007ffff74b097d in PyRun_FileExFlags () from /usr/lib/libpython3.6m.so.1.0 #34 0x00007ffff74b0b67 in PyRun_SimpleFileExFlags () from /usr/lib/libpython3.6m.so.1.0 #35 0x00007ffff74a4a91 in Py_Main () from /usr/lib/libpython3.6m.so.1.0 #36 0x0000000000400a5d in main () </pre> </details> #### Output of ``pd.show_versions()`` <details> <pre> INSTALLED VERSIONS ------------------ commit: None python: 3.6.0.final.0 python-bits: 64 OS: Linux OS-release: 4.8.13-1-ARCH machine: x86_64 processor: byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8<br/> pandas: 0.19.2 nose: 1.3.7 pip: 9.0.1 setuptools: 33.0.0 Cython: 0.25.2 numpy: 1.11.3 scipy: 0.18.1 statsmodels: 0.6.1 xarray: None IPython: 5.1.0 sphinx: None patsy: 0.4.1 dateutil: 2.6.0 pytz: 2016.10 blosc: None bottleneck: None tables: None numexpr: None matplotlib: 2.0.0rc2+2914.g1fa4dd705.dirty openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: None httplib2: 0.9.2 apiclient: 1.5.5 sqlalchemy: 1.1.4 pymysql: None psycopg2: None jinja2: 2.9.4 boto: None pandas_datareader: None </pre> </details>
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15,141
pd.groupby(pd.TimeGrouper('W')).groups returns Timestamps instead of Periods
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15
2017-01-16T10:36:48Z
2017-01-17T19:08:33Z
2017-01-17T13:05:51Z
NONE
null
Lets consider the following DataFrame: ``` date_range = pd.date_range(dt(2010,1,1), dt(2010,1,31), freq='1D') df = pd.DataFrame(data = np.random.rand(len(date_range),2), index = date_range) ``` If I group the datapoints by periods of 1 week and visualize the groups definition, I get: ``` In: [1]:df.groupby(pd.TimeGrouper('W')).groups Out:[1]: {Timestamp('2010-01-03 00:00:00', freq='W-SUN'): 3, Timestamp('2010-01-10 00:00:00', freq='W-SUN'): 10, Timestamp('2010-01-17 00:00:00', freq='W-SUN'): 17, Timestamp('2010-01-24 00:00:00', freq='W-SUN'): 24, Timestamp('2010-01-31 00:00:00', freq='W-SUN'): 31} I retrieve the keys of that dictionary: In: [2]: list(df.groupby(pd.TimeGrouper('W')).keys()) Out:[2]: [Timestamp('2010-01-03 00:00:00', freq='W-SUN'), Timestamp('2010-01-10 00:00:00', freq='W-SUN'), Timestamp('2010-01-31 00:00:00', freq='W-SUN'), Timestamp('2010-01-17 00:00:00', freq='W-SUN'), Timestamp('2010-01-24 00:00:00', freq='W-SUN')] ``` However I am left with those funny variables such as Timestamp('2010-01-24 00:00:00', freq='W-SUN') that have the prefix Timestamp but are structured like Periods. I think this is not correct, and those variables should be Periods?
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CLN/DEPR: remove deprecated pandas.rpy module (GH9602)
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8
2017-01-16T15:47:42Z
2018-04-10T11:35:47Z
2017-01-22T12:15:10Z
MEMBER
null
Closes #9602
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201,134,959
MDExOlB1bGxSZXF1ZXN0MTAxNzY5NjUy
15,143
Categoricals hash consistently
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4
2017-01-16T22:48:30Z
2017-01-18T14:35:46Z
2017-01-18T14:35:44Z
CONTRIBUTOR
null
Previously categorical values were hashed using just their codes. This meant that the hash value depended on the ordering of the categories, rather than on the values the series represented. This caused problems in dask, where different partitions might have different categorical mappings. This PR makes the hashing dependent on the values the categorical represents, rather than on the codes. The categories are first hashed, and then the codes are remapped to the hashed values. This is slightly slower than before (still need to hash the categories, where we didn't before), but allows for more consistent hashing. Related to this work in dask: https://github.com/dask/dask/pull/1877.
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201,285,620
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15,144
read_fwf with passing converters and na_values at the same time
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9
2017-01-17T13:35:49Z
2017-01-24T05:02:04Z
2017-01-23T20:23:21Z
NONE
null
Duplicate of #13302 --- When reading via read_fwf and passing both na_values and converters at the same time, na_value detection does not work: #### Code Sample, a copy-pastable example if possible Assume a fwf-style text file like this: > A B C > 10 -1 10 > 10 10 10 ```python In: read_fwf(file_path, converters={'B':lambda x: int(x)/10.}, na_values={'B':-1}) Out: > A B C > 0 10 -0.1 10 > 1 10 1.0 10 ``` #### Expected Output ``` > A B C > 0 10 NaN 10 > 1 10 1.0 10 ``` It would be nice if this could be handled behind the scenes. In pandas.io.parsers._clean_na_values, float representations of the na_values are produced by _floatify_na_values. Maybe passing the converters to this function might be a solution? #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit: None python: 2.7.10.final.0 python-bits: 64 OS: Windows OS-release: 7 machine: AMD64 processor: Intel64 Family 6 Model 23 Stepping 10, GenuineIntel byteorder: little LC_ALL: None LANG: None LOCALE: None.None pandas: 0.19.2 (...) </details>
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201,290,145
MDU6SXNzdWUyMDEyOTAxNDU=
15,145
COMPAT: create UInt64Block
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8
2017-01-17T13:51:58Z
2019-07-23T17:09:46Z
2019-07-23T17:09:45Z
CONTRIBUTOR
null
xref https://github.com/pandas-dev/pandas/pull/14937#issuecomment-272705717 a number of indexing / conversion issues arise because we are treating ``uint`` as a direct ``int``, rather than a sub-class. (e.g. if we make UIntBlock a sub-class of IntBlock), I think can easily handle some small overrides to, for instance check for negative values when indexing. ``` In [1]: df = pd.DataFrame({'A' : np.array([1,2,3],dtype='uint64'), 'B': range(3)}) In [2]: df Out[2]: A B 0 1 0 1 2 1 2 3 2 In [4]: df.dtypes Out[4]: A uint64 B int64 dtype: object ``` Buggy ``` In [5]: df.iloc[1] = -1 In [6]: df Out[6]: A B 0 1 0 1 18446744073709551615 -1 2 3 2 ``` ``` In [7]: df.iloc[1] = np.nan In [8]: df Out[8]: A B 0 1.0 0.0 1 NaN NaN 2 3.0 2.0 ``` This is correct ``` In [9]: df.A.astype('uint64') --------------------------------------------------------------------------- ValueError: Cannot convert non-finite values (NA or inf) to integer ``` However, this is not ``` In [10]: df.iloc[1] = -1 In [11]: df Out[11]: A B 0 1.0 0.0 1 -1.0 -1.0 2 3.0 2.0 In [12]: df.dtypes Out[12]: A float64 B float64 dtype: object In [13]: df.A.astype('uint64') Out[13]: 0 1 1 18446744073709551615 2 3 Name: A, dtype: uint64 ``` Construction with invalid values ``` In [1]: Series([-1], dtype='uint64') Out[1]: 0 18446744073709551615 dtype: uint64 ```
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15,146
DEPR: remove freq attribute of Timestamp?
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12
2017-01-17T14:41:03Z
2021-06-07T16:47:33Z
2021-06-07T16:47:33Z
CONTRIBUTOR
null
xref https://github.com/pandas-dev/pandas/issues/15141#issuecomment-273184725 In theory these should be completely fungible. If so, we should remove the ``freq`` attribute of ``Timestamps`` and instead return ``Periods`` in those instances. ``` In [2]: t = Timestamp('2010-01-03 00:00:00', freq='W-SUN') In [3]: t Out[3]: Timestamp('2010-01-03 00:00:00', freq='W-SUN') In [4]: t.to_period() Out[4]: Period('2009-12-28/2010-01-03', 'W-SUN') In [5]: t.to_period().to_timestamp(how='start') Out[5]: Timestamp('2009-12-28 00:00:00') In [6]: t.to_period().to_timestamp(how='end') Out[6]: Timestamp('2010-01-03 00:00:00') ```
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201,307,610
MDU6SXNzdWUyMDEzMDc2MTA=
15,147
No error when mismatched vectors are assigned to pandas.DataFrame
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2
2017-01-17T14:49:20Z
2017-01-17T14:56:12Z
2017-01-17T14:55:49Z
NONE
null
#### Code Sample, a copy-pastable example if possible ```python # toy data x = [0, 1, 1, 1, 2, 3, 4] y = pd.qcut(sorted(np.unique(x), reverse=True), 5, labels=np.arange(5, 0, -1)) # size 5 only! table = pd.DataFrame() table['col_1'] = x # size 7 table['col_2'] = y # size 5! ``` #### Problem description I'm assigning vectors with mismatched sizes to the dataframe. I'm not even specifying the index to assign it. This became a problem because it didn't raise any error. I was expecting it to raise an error or a warning. This can be a problem for code using ```table```. #### Expected Output Raise some error upon this mismatched assignment. The error can be generated if I only call: ``` table --------------------------------------------------------------------------- IndexError Traceback (most recent call last) /usr/local/lib/python3.4/dist-packages/IPython/core/formatters.py in __call__(self, obj) 309 method = get_real_method(obj, self.print_method) 310 if method is not None: --> 311 return method() 312 return None 313 else: /usr/local/lib/python3.4/dist-packages/pandas/core/frame.py in _repr_html_(self) 608 609 return self.to_html(max_rows=max_rows, max_cols=max_cols, --> 610 show_dimensions=show_dimensions, notebook=True) 611 else: 612 return None /usr/local/lib/python3.4/dist-packages/pandas/core/frame.py in to_html(self, buf, columns, col_space, header, index, na_rep, formatters, float_format, sparsify, index_names, justify, bold_rows, classes, escape, max_rows, max_cols, show_dimensions, notebook, decimal, border) 1606 decimal=decimal) 1607 # TODO: a generic formatter wld b in DataFrameFormatter -> 1608 formatter.to_html(classes=classes, notebook=notebook, border=border) 1609 1610 if buf is None: /usr/local/lib/python3.4/dist-packages/pandas/formats/format.py in to_html(self, classes, notebook, border) 698 border=border) 699 if hasattr(self.buf, 'write'): --> 700 html_renderer.write_result(self.buf) 701 elif isinstance(self.buf, compat.string_types): 702 with open(self.buf, 'w') as f: /usr/local/lib/python3.4/dist-packages/pandas/formats/format.py in write_result(self, buf) 1022 indent += self.indent_delta 1023 indent = self._write_header(indent) -> 1024 indent = self._write_body(indent) 1025 1026 self.write('</table>', indent) /usr/local/lib/python3.4/dist-packages/pandas/formats/format.py in _write_body(self, indent) 1181 self._write_hierarchical_rows(fmt_values, indent) 1182 else: -> 1183 self._write_regular_rows(fmt_values, indent) 1184 else: 1185 for i in range(len(self.frame)): /usr/local/lib/python3.4/dist-packages/pandas/formats/format.py in _write_regular_rows(self, fmt_values, indent) 1215 row = [] 1216 row.append(index_values[i]) -> 1217 row.extend(fmt_values[j][i] for j in range(ncols)) 1218 1219 if truncate_h: /usr/local/lib/python3.4/dist-packages/pandas/formats/format.py in <genexpr>(.0) 1215 row = [] 1216 row.append(index_values[i]) -> 1217 row.extend(fmt_values[j][i] for j in range(ncols)) 1218 1219 if truncate_h: IndexError: list index out of range Out[33]: col_1 col_2 0 0 1 1 1 2 2 1 3 3 1 4 4 2 5 5 3 6 4 ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit: None python: 3.4.3.final.0 python-bits: 64 OS: Linux OS-release: 3.13.0-101-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: C LANG: en_US.UTF-8 LOCALE: None.None pandas: 0.19.2 nose: 1.3.1 pip: 9.0.1 setuptools: 32.3.1 Cython: None numpy: 1.11.0 scipy: 0.18.1 statsmodels: None xarray: None IPython: 5.1.0 sphinx: None patsy: None dateutil: 2.0 pytz: 2012c blosc: None bottleneck: None tables: None numexpr: None matplotlib: 1.5.3 openpyxl: None xlrd: None xlwt: 1.2.0 xlsxwriter: None lxml: None bs4: None html5lib: 0.999 httplib2: None apiclient: None sqlalchemy: None pymysql: None psycopg2: None jinja2: 2.8.1 boto: None pandas_datareader: None </details>
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201,402,226
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15,148
BUG: Segfault due to float_precision='round_trip'
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4
2017-01-17T20:54:01Z
2017-01-20T12:31:43Z
2017-01-20T12:31:32Z
CONTRIBUTOR
null
`round_trip` calls back into Python, so the GIL must be held. It also fails to silence the Python exception, leading to spurious errors. Closes #15140. - [x] closes #xxxx - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` (*Most of them are false positives because flake8 doesn't understand Cython or it was code I didn't even touch*) - [x] whatsnew entry
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15,149
BUG: unicode characters when reading JSON lines
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4
2017-01-18T05:56:12Z
2017-01-19T13:54:35Z
2017-01-19T13:54:35Z
CONTRIBUTOR
null
Fixes UnicodeDecodeError bug when reading JSON lines input with Ascii decoder, which is often the default setting in Python 2.7. Avoids issues with mixing unicode and ascii strings. xref #15132 - [x] closes #15132 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry
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15,150
BUG: crosstab cannot normalize multiple columns for the index
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3
2017-01-18T08:04:03Z
2020-01-21T18:26:47Z
2020-01-21T18:26:47Z
NONE
null
#### Code Sample, a copy-pastable example if possible ```python df = pd.DataFrame({'A': ['one', 'one', 'two', 'three'] * 6, 'B': ['A', 'B', 'C'] * 8, 'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 4, 'D': np.random.randn(24), 'E': np.random.randn(24)}) pd.crosstab([df.A, df.B], df.C, values=df.D, aggfunc=np.sum, normalize=True, margins=True) ``` #### Problem description This gives "ValueError: labels ['All'] not contained in axis" Currently, crosstab only normalize single index. I expect crosstab can normalize with multiple columns for the index, but I got ValueError: labels ['All'] not contained in axis. #### Expected Output normalized result #### Output of ``pd.show_versions()`` <details> # Paste the output here pd.show_versions() here INSTALLED VERSIONS ------------------ commit: None python: 2.7.12.final.0 python-bits: 64 OS: Linux OS-release: 4.4.0-47-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: None.None pandas: 0.19.1 nose: None pip: 9.0.1 setuptools: 28.8.0 Cython: None numpy: 1.11.2 scipy: 0.18.1 statsmodels: None xarray: None IPython: 5.1.0 sphinx: None patsy: None dateutil: 2.6.0 pytz: 2016.7 blosc: None bottleneck: None tables: None numexpr: None matplotlib: 1.5.3 openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: None httplib2: None apiclient: None sqlalchemy: 1.1.4 pymysql: None psycopg2: None jinja2: 2.8 boto: None pandas_datareader: None None </details>
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15,151
TypeError when subtracting from empty index
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1
2017-01-18T14:20:26Z
2017-01-18T14:39:54Z
2017-01-18T14:39:41Z
NONE
null
#### Code Sample, a copy-pastable example if possible While `pd.DataFrame().index + 1` returns the unchanged index `Index([], dtype='object')`, I see the following error with subtraction: ```python In [71]: pd.DataFrame().index - 1 Traceback (most recent call last): File "<ipython-input-71-cbb1aa99c0f9>", line 1, in <module> pd.DataFrame().index - 1 File "C:\Program Files\WinPython-64bit-3.5.2.3Qt5\python-3.5.2.amd64\lib\site-packages\pandas\indexes\base.py", line 1796, in __sub__ "{typ}".format(typ=type(self))) TypeError: cannot perform __sub__ with this index type: <class 'pandas.indexes.base.Index'> ``` #### Problem description I would like to have `pd.DataFrame().index - 1` return the unchanged index, too. The current behavior makes my code not generalize well for the edge case of empty dataframes (which happen in my environment). #### Expected Output The same as in the summation case. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit: None python: 3.5.2.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: en LOCALE: None.None pandas: 0.19.1 nose: 1.3.7 pip: 8.1.2 setuptools: 28.7.1 Cython: 0.25.1 numpy: 1.11.2 scipy: 0.18.1 statsmodels: 0.8.0rc1 xarray: 0.8.2 IPython: 5.1.0 sphinx: 1.4.8 patsy: 0.4.1 dateutil: 2.5.3 pytz: 2016.7 blosc: 1.4.4 bottleneck: 1.2.0 tables: 3.3.0 numexpr: 2.6.1 matplotlib: 1.5.3 openpyxl: None xlrd: 1.0.0 xlwt: None xlsxwriter: 0.9.3 lxml: 3.6.4 bs4: 4.5.1 html5lib: 0.999999999 httplib2: None apiclient: None sqlalchemy: 1.1.3 pymysql: None psycopg2: None jinja2: 2.8 boto: None pandas_datareader: 0.2.1 </details>
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15,152
DEPR: DeprecationWarning -> FutureWarning
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4
2017-01-18T14:37:14Z
2019-05-19T17:15:07Z
2019-05-19T17:15:07Z
CONTRIBUTOR
null
- [x] ``.ix`` #15113 (#26438) - [ ] ~~``.isnull/.notnull`` #16972 ``~~ Move to #6581 when converted to `FutureWarning`
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15,153
ValueError from read_csv when in loop but not when called independently
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2017-01-18T16:00:18Z
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2017-01-18T16:19:00Z
NONE
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#### Code Sample, a copy-pastable example if possible Given a directory with a large collection (> 1900) of files such as the attached sample ([sample_file.zip](https://github.com/pandas-dev/pandas/files/714335/sample_file.zip)), and the following pseudo-code "junk.py" (needs work to make it copy-pastable by defining dtypes and colnames, as well as generating thousands of similar files ```python import os.path as osp import pandas as pd for f in ec_files: print(osp.basename(f)) fdf = pd.read_csv(f, dtype=dtypes, header=None, parse_dates=[0, 1], index_col=1, names=colnames, na_values=["NAN"], true_values=["t"], false_values=["f"], low_memory=False) ``` #### Problem description Running this script stops at different files each time it is run with the following traceback: ```python Traceback (most recent call last): File "junk.py", line 9, in <module> false_values=["f"], low_memory=False) File "/usr/lib/python2.7/dist-packages/pandas/io/parsers.py", line 645, in parser_f return _read(filepath_or_buffer, kwds) File "/usr/lib/python2.7/dist-packages/pandas/io/parsers.py", line 400, in _read data = parser.read() File "/usr/lib/python2.7/dist-packages/pandas/io/parsers.py", line 938, in read ret = self._engine.read(nrows) File "/usr/lib/python2.7/dist-packages/pandas/io/parsers.py", line 1505, in read data = self._reader.read(nrows) File "pandas/parser.pyx", line 849, in pandas.parser.TextReader.read (pandas/parser.c:9907) File "pandas/parser.pyx", line 945, in pandas.parser.TextReader._read_rows (pandas/parser.c:11161) File "pandas/parser.pyx", line 1047, in pandas.parser.TextReader._convert_column_data (pandas/parser.c:12536) File "pandas/parser.pyx", line 1126, in pandas.parser.TextReader._convert_tokens (pandas/parser.c:13783) ValueError: invalid literal for float(): 2016-07-13 18:54:03.9 ``` The file where the error is thrown, as well as the particular ValueError vary with each run, i.e. it is never consistent. **Furthermore, every file where the error occurs reads without any problem with the very same read_csv command called on its own (not in a loop)**. Therefore, the read_csv command is correct for these files. If the number of files to loop through is relatively small (<100) the process **sometimes** finishes successfully, but not always. I initially reported this in [Stackoverflow](http://stackoverflow.com/questions/41664844/dtype-seemingly-ignored-in-read-csv-during-long-loop) #### Expected Output read_csv output and behaviour should be the same whether it is run in a loop or independently. #### Output of ``pd.show_versions()`` <details> # Paste the output here pd.show_versions() here ``` INSTALLED VERSIONS ------------------ commit: None python: 2.7.13.final.0 python-bits: 64 OS: Linux OS-release: 4.8.0-2-amd64 machine: x86_64 processor: byteorder: little LC_ALL: None LANG: en_CA.UTF-8 LOCALE: None.None pandas: 0.19.0+git14-ga40e185 nose: 1.3.7 pip: 9.0.1 setuptools: 32.0.0 Cython: None numpy: 1.12.0rc2 scipy: 0.18.1 statsmodels: 0.8.0rc1 xarray: None IPython: 5.1.0 sphinx: 1.4.9 patsy: 0.4.1+dev dateutil: 2.5.3 pytz: 2016.7 blosc: None bottleneck: None tables: 3.3.0 numexpr: 2.6.1 matplotlib: 2.0.0rc2 openpyxl: 2.3.0 xlrd: 1.0.0 xlwt: 0.7.5 xlsxwriter: None lxml: 3.7.1 bs4: 4.5.3 html5lib: 0.999999999 httplib2: 0.9.2 apiclient: 1.5.5 sqlalchemy: None pymysql: None psycopg2: 2.6.2 (dt dec mx pq3 ext lo64) jinja2: 2.8 boto: None pandas_datareader: None ``` </details>
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201,649,466
MDU6SXNzdWUyMDE2NDk0NjY=
15,154
CI: invalidate cache when .pxi.in only changes
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4
2017-01-18T18:04:54Z
2017-01-19T02:22:15Z
2017-01-19T02:22:14Z
CONTRIBUTOR
null
In the following, *only* the ``.pxi.in`` were changed and ``setup.py`` correctly has dependencies on these, so local building is fine. However, on travis, the cython cache does not seem to be invalidation (and I had to resort to manually clearing it). similar to my comment: https://github.com/pandas-dev/pandas/pull/14937#issuecomment-273270706 happened again on merging: #15139, see: https://travis-ci.org/pandas-dev/pandas/jobs/193092807
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201,656,786
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15,155
Groupby level fails to enumerate groups
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1
2017-01-18T18:36:35Z
2017-01-19T20:21:53Z
2017-01-19T20:21:53Z
CONTRIBUTOR
null
#### Code Sample ```python test = pd.DataFrame(data=np.arange(2,22,2), index=pd.MultiIndex(levels=[pd.CategoricalIndex(["a", "b"]), range(10)], labels=[[0]*5 + [1]*5, range(10)], names = ["Index1", "Index2"])) testGroupedBy = test.groupby(level=["Index1"]) testGroupedBy.get_group("a") ``` #### Problem description instead of returning the data as expected, an error is thrown: ```python --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-1466-65ba7dd9f763> in <module>() ----> 1 testGroupedBy.get_group("a") python2.7/site-packages/pandas/core/groupby.pyc in get_group(self, name, obj) 614 obj = self._selected_obj 615 --> 616 inds = self._get_index(name) 617 if not len(inds): 618 raise KeyError(name) python2.7/site-packages/pandas/core/groupby.pyc in _get_index(self, name) 461 def _get_index(self, name): 462 """ safe get index, translate keys for datelike to underlying repr """ --> 463 return self._get_indices([name])[0] 464 465 @cache_readonly python2.7/site-packages/pandas/core/groupby.pyc in _get_indices(self, names) 428 return [] 429 --> 430 if len(self.indices) > 0: 431 index_sample = next(iter(self.indices)) 432 else: python2.7/site-packages/pandas/core/groupby.pyc in __getattr__(self, attr) 527 528 raise AttributeError("%r object has no attribute %r" % --> 529 (type(self).__name__, attr)) 530 531 plot = property(GroupByPlot) AttributeError: 'DataFrameGroupBy' object has no attribute 'indices' ``` #### Expected Output I guess an alternative way to get this output is `test.loc["a"]` - which yields the expected output below: ```python 0 Index2 0 2 1 4 2 6 3 8 4 10 ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit: None python: 2.7.10.final.0 python-bits: 64 OS: Linux OS-release: 2.6.32-431.11.2.el6.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: en_GB.utf8 LANG: en_GB.utf8 LOCALE: None.None pandas: 0.19.2 nose: 1.3.7 pip: 9.0.1 setuptools: 32.3.1 Cython: None numpy: 1.11.3 scipy: 0.18.1 statsmodels: None xarray: None IPython: 5.1.0 sphinx: 1.5.1 patsy: None dateutil: 2.6.0 pytz: 2016.10 blosc: None bottleneck: None tables: None numexpr: None matplotlib: 1.5.3 openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: 0.9999999 httplib2: None apiclient: None sqlalchemy: None pymysql: None psycopg2: None jinja2: 2.9.4 boto: None pandas_datareader: None </details>
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15,156
Versioneer reported version of dev build is incorrect
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5
2017-01-18T19:51:03Z
2017-10-24T12:41:21Z
2017-10-24T12:41:21Z
CONTRIBUTOR
null
Sometimes it's nice to install projects from master to get recent fixes (e.g. `pip install git+https://github.com/pandas-dev/pandas.git`). However, since pandas switched to using dev branches for `0.19.1+`, versioneer incorrectly reports the version of the dev build as `0.19.0`. This breaks checks for version, e.g. the following would report false for a build off master, when semantically master is after the 0.19.2 release: ```python from distutils.version import LooseVersion import pandas as pd LooseVersion(pd.__version__) > '0.19.2' ``` I'm not sure if there is an easy/good fix for this, just opening this to discuss.
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15,157
BUG: MultiIndex.set_labels not correctly verifying
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8
2017-01-18T21:16:29Z
2021-05-08T00:34:42Z
2021-05-08T00:34:42Z
NONE
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I believe that label_length = len(self.labels[0]) should actually be label_length = len(labels[0]) I tested my proposed change and it fixed the problem I had in my code that started when I updated Pandas from 0.18.1 to 0.19.2. def _verify_integrity(self, labels=None, levels=None): """ Parameters ---------- labels : optional list Labels to check for validity. Defaults to current labels. levels : optional list Levels to check for validity. Defaults to current levels. Raises ------ ValueError * if length of levels and labels don't match or any label would exceed level bounds """ # NOTE: Currently does not check, among other things, that cached # nlevels matches nor that sortorder matches actually sortorder. labels = labels or self.labels levels = levels or self.levels if len(levels) != len(labels): raise ValueError("Length of levels and labels must match. NOTE:" " this index is in an inconsistent state.") label_length = len(self.labels[0]) for i, (level, label) in enumerate(zip(levels, labels)): if len(label) != label_length: raise ValueError("Unequal label lengths: %s" % ([len(lab) for lab in labels])) if len(label) and label.max() >= len(level): raise ValueError("On level %d, label max (%d) >= length of" " level (%d). NOTE: this index is in an" " inconsistent state" % (i, label.max(), len(level)))
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201,745,009
MDExOlB1bGxSZXF1ZXN0MTAyMTg4MTIy
15,158
DOC: fix doc-build for .ix deprecations in whatsnew
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1
2017-01-19T01:59:44Z
2017-01-19T02:01:46Z
2017-01-19T02:01:45Z
CONTRIBUTOR
null
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201,748,015
MDExOlB1bGxSZXF1ZXN0MTAyMTg5OTg3
15,159
CI: add .pxi.in to build caching
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0
2017-01-19T02:20:20Z
2017-01-19T02:22:15Z
2017-01-19T02:22:15Z
CONTRIBUTOR
null
closes #15154
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201,764,343
MDU6SXNzdWUyMDE3NjQzNDM=
15,160
to_excel should (optionally) freeze panes
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4
2017-01-19T04:51:05Z
2017-02-16T20:35:29Z
2017-02-16T17:47:46Z
CONTRIBUTOR
null
`DataFrame.to_excel()` is currently helpful in styling column and row headers distinctly from values. One other simple feature Excel provides to manage headers is "freeze panes". Panes can be frozen such that the column and row headers are fixed. This is achieved in openpyxl with `worksheet.freeze_panes = top_left_value_cell` and in xlwt with `worksheet.freeze_panes(bottommost_header_row, leftmost_header_col)`. I think making this available, as an option or by default, would give a valuable usability boost to the exported Excel spreadsheets.
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201,791,159
MDExOlB1bGxSZXF1ZXN0MTAyMjE5NTc2
15,161
Apply fontsize to both x- and y-axis tick labels
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5
2017-01-19T08:16:39Z
2017-01-20T17:13:03Z
2017-01-20T17:12:58Z
CONTRIBUTOR
null
- [x] closes #15108 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0MTAyMjQxODcz
15,162
BUG, TST: Patch handling of uint64 in algorithms
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3
2017-01-19T10:45:04Z
2017-01-22T15:49:54Z
2017-01-22T15:49:36Z
MEMBER
null
1) Patches handling of `uint64` in `value_counts` 2) Adds tests for handling of `uint64` in `factorize` xref #14934
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201,836,730
MDExOlB1bGxSZXF1ZXN0MTAyMjUwNTQy
15,163
Bug in groupby.get_group on categoricalindex
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2017-01-19T11:41:28Z
2017-11-10T11:11:23Z
2017-01-19T20:21:53Z
CONTRIBUTOR
null
- [x] closes #15155 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry
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201,854,416
MDExOlB1bGxSZXF1ZXN0MTAyMjYxNDk3
15,164
DOC: Clarified wording of coerce_float
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2
2017-01-19T12:58:06Z
2017-01-19T13:57:21Z
2017-01-19T13:57:19Z
NONE
null
I think this is the intended meaning (and it matches with use).
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15,165
read_csv() will throw a exception when csv file has str 'NaN'
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5
2017-01-19T14:17:35Z
2017-01-19T14:33:08Z
2017-01-19T14:32:59Z
NONE
null
#### Code Sample, a copy-pastable example if possible ```python import pandas as pd from bokeh.charts import Bar, Line from bokeh.plotting import output_file,show output_file("test.html") wt = pd.read_csv('test.csv', dtype={ 'word': str, 'times': int, }) print(wt['word']) line = Line(wt, x='word', y='times' ) show(line) ``` The test.csv file showed below: ```csv word,times nan,1 ``` #### Problem description read_csv() will throw a exception when read csv file has str 'NaN' [this should explain **why** the current behaviour is a problem and why the expected output is a better solution.] I think it has a str 'nan' becuase it conflicts with python built-in words. if i remove 'nan' string, it works good. #### Output ``` ``` #### Expected Output #### Output of ``pd.show_versions()`` <details> # Paste the output here pd.show_versions() here INSTALLED VERSIONS ------------------ commit: None python: 3.5.2.final.0 python-bits: 64 OS: Linux OS-release: 4.8.0-2-amd64 machine: x86_64 processor: byteorder: little LC_ALL: LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 pandas: 0.19.2 nose: None pip: 9.0.1 setuptools: 32.3.0 Cython: None numpy: 1.12.0 scipy: None statsmodels: None xarray: None IPython: 5.1.0 sphinx: None patsy: None dateutil: 2.6.0 pytz: 2016.10 blosc: None bottleneck: None tables: None numexpr: None matplotlib: 2.0.0 openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: 4.5.1 html5lib: 0.999999999 httplib2: 0.9.2 apiclient: None sqlalchemy: None pymysql: None psycopg2: None jinja2: 2.9.4 boto: None pandas_datareader: None </details>
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201,904,049
MDU6SXNzdWUyMDE5MDQwNDk=
15,166
BUG: .loc indexing not preserving Index type
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2
2017-01-19T16:13:29Z
2019-11-13T01:48:02Z
2019-11-13T01:48:02Z
CONTRIBUTOR
null
#### ```python test = pd.DataFrame(data=np.arange(2,22,2), index=pd.MultiIndex(levels=[pd.CategoricalIndex(["a", "b"]), range(10)], labels=[[0]*5 + [1]*5, range(10)], names = ["Index1", "Index2"])) test.index.levels[0] # CategoricalIndex([u'a', u'b'], categories=[u'a', u'b'], ordered=False, name=u'Index1', dtype='category') test.loc[["a"]].index.levels[0] # Index([u'a'], dtype='object', name=u'Index1') ``` #### Problem description When selecting data through a categorical index, the categorical index is lost. #### Expected Output ``` test.loc[["a"]].index.levels[0] # CategoricalIndex([u'a'], categories=[u'a', u'b'], ordered=False, name=u'Index1', dtype='category') ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit: None python: 2.7.10.final.0 python-bits: 64 OS: Linux OS-release: 2.6.32-431.11.2.el6.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: en_GB.utf8 LANG: en_GB.utf8 LOCALE: None.None pandas: 0.19.2 nose: 1.3.7 pip: 9.0.1 setuptools: 32.3.1 Cython: None numpy: 1.11.3 scipy: 0.18.1 statsmodels: None xarray: None IPython: 5.1.0 sphinx: 1.5.1 patsy: None dateutil: 2.6.0 pytz: 2016.10 blosc: None bottleneck: None tables: None numexpr: None matplotlib: 1.5.3 openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: 0.9999999 httplib2: None apiclient: None sqlalchemy: None pymysql: None psycopg2: None jinja2: 2.9.4 boto: None pandas_datareader: None </details>
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15,167
Bug: resample options with TimedeltaIndex not working
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1
2017-01-19T17:42:47Z
2017-01-19T18:51:08Z
2017-01-19T18:50:55Z
NONE
null
#### Code Sample, a copy-pastable example if possible ```python import pandas as pd index = pd.timedelta_range('0', periods=4, freq='s') series = pd.Series(range(4), index=index) print series print series.resample('2s', label='left').sum() print series.resample('2s', label='right').sum() print series.resample('2s', label='right', closed='right').sum() ``` #### The output ``` 00:00:00 0 00:00:01 1 00:00:02 2 00:00:03 3 Freq: S, dtype: int64 00:00:00 1 00:00:02 5 Freq: 2S, dtype: int64 00:00:00 1 00:00:02 5 Freq: 2S, dtype: int64 00:00:00 1 00:00:02 5 Freq: 2S, dtype: int64 ``` #### Problem description When calling the function resample with a DatetimeIndex works as expected. However, if called with a TimedeltaIndex the options label and closed does not work. #### Expected Output ``` 00:00:00 0 00:00:01 1 00:00:02 2 00:00:03 3 Freq: S, dtype: int64 00:00:00 1 00:00:02 5 Freq: 2S, dtype: int64 00:00:02 1 00:00:04 5 Freq: 2S, dtype: int64 00:00:00 0 00:00:02 3 00:00:04 3 Freq: 2S, dtype: int64 ``` #### Output of ``pd.show_versions()`` <details> # Paste the output here pd.show_versions() here INSTALLED VERSIONS ------------------ commit: None python: 2.7.6.final.0 python-bits: 64 OS: Linux OS-release: 3.19.0-74-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: None.None pandas: 0.19.2 nose: 1.3.1 pip: 9.0.1 setuptools: 28.8.0 Cython: 0.24.1 numpy: 1.12.0 scipy: 0.18.1 statsmodels: 0.5.0 xarray: None IPython: 4.1.2 sphinx: 1.4.6 patsy: 0.4.1 dateutil: 2.6.0 pytz: 2016.10 blosc: None bottleneck: None tables: 3.1.1 numexpr: 2.2.2 matplotlib: 1.5.3 openpyxl: 1.7.0 xlrd: 0.9.2 xlwt: 0.7.5 xlsxwriter: None lxml: None bs4: 4.2.1 html5lib: 0.999 httplib2: 0.8 apiclient: None sqlalchemy: None pymysql: None psycopg2: 2.4.5 (dt dec mx pq3 ext) jinja2: 2.8 boto: None pandas_datareader: None </details>
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15,168
to_sql & cx_oracle
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5
2017-01-19T17:53:16Z
2017-01-28T22:42:11Z
2017-01-28T22:42:04Z
NONE
null
Hello, I am having the following error: <type 'exceptions.TypeError'> ('expecting string, unicode or buffer object',) expecting string, unicode or buffer object but the issue is that this error occur only when the 'chunksize' is greater than 1. I did try different value for this parameter without success. Let me know how I can help solving this issue. Last but not least it looks like that is linked with one of the recent libraries update... was working just fine before 1 or 2 weeks ago. thanks in advance. JM
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15,169
resample().apply not returning multiple columns like groupby(pd.Timegrouper()).apply
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5
2017-01-19T18:32:33Z
2017-10-27T20:32:20Z
2017-10-27T20:32:20Z
CONTRIBUTOR
null
#### Code Sample, a copy-pastable example if possible ```python In [3]: date = pd.date_range('1-1-2015', '12-31-15', freq='D') In [4]: df = pd.DataFrame(data={'col1':np.random.rand(len(date))}, index=date) In [5]: def calc(x): s = pd.Series([1,2], index=['a', 'b']) return s In [6]: df.resample('M').apply(calc) Out[6]: col1 2015-01-31 NaN 2015-02-28 NaN 2015-03-31 NaN 2015-04-30 NaN 2015-05-31 NaN 2015-06-30 NaN 2015-07-31 NaN 2015-08-31 NaN 2015-09-30 NaN 2015-10-31 NaN 2015-11-30 NaN 2015-12-31 NaN In [7]: df.groupby(pd.TimeGrouper('M')).apply(calc) Out[7]: a b 2015-01-31 1 2 2015-02-28 1 2 2015-03-31 1 2 2015-04-30 1 2 2015-05-31 1 2 2015-06-30 1 2 2015-07-31 1 2 2015-08-31 1 2 2015-09-30 1 2 2015-10-31 1 2 2015-11-30 1 2 2015-12-31 1 2 ``` #### Problem description It is my understanding that ``resample`` with `apply` should work very similarly as ``groupby(pd.Timegrouper)`` with ``apply``. In a more complex example I was trying to return many aggregated results that are calculated with several columns. It seems `resample` with `apply` is unable to return anything but a Series that has the same index as the calling DataFrame columns. #### Expected Output Should look exactly like the output from ``df.groupby(pd.TimeGrouper('M')).apply(calc)`` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit: None python: 3.5.1.final.0 python-bits: 64 OS: Windows OS-release: 7 machine: AMD64 processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel byteorder: little LC_ALL: None LANG: None LOCALE: None.None pandas: 0.19.2 nose: 1.3.7 pip: 8.1.2 setuptools: 23.0.0 Cython: 0.24 numpy: 1.12.0 scipy: 0.18.1 statsmodels: 0.6.1 xarray: None IPython: 4.2.0 sphinx: 1.3.1 patsy: 0.4.1 dateutil: 2.5.3 pytz: 2016.4 blosc: None bottleneck: 1.0.0 tables: 3.2.2 numexpr: 2.6.1 matplotlib: 1.5.1 openpyxl: 2.3.2 xlrd: 0.9.4 xlwt: 1.0.0 xlsxwriter: 0.8.7 lxml: 3.6.0 bs4: 4.4.1 html5lib: 0.9999999 httplib2: None apiclient: None sqlalchemy: 1.0.13 pymysql: 0.7.5.None psycopg2: None jinja2: 2.8 boto: 2.40.0 pandas_datareader: 0.2.1 </details>
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15,170
CI: have 34_SLOW build use matplotlib latest from conda-forge
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0
2017-01-19T20:28:37Z
2017-01-19T21:53:38Z
2017-01-19T21:53:38Z
CONTRIBUTOR
null
CI: change 34_SLOW to use numpy 1.10* as mpl 2.0 requires anyhow
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15,171
ENH: Display options for Decimal objects.
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2
2017-01-19T20:47:35Z
2021-05-08T00:36:02Z
2021-05-08T00:36:02Z
NONE
null
This is a feature request #### Code Sample, a copy-pastable example if possible ```python import decimal import pandas as pd pd.set_option('display.precision', 3) ctx = decimal.getcontext() def D(number): return ctx.create_decimal(str(number)) df = pd.DataFrame({ "decimals": [D(1/9), D(1/11), D(1/13)], "floats": [1/9, 1/11, 1/13] }) df.head() ``` #### Problem description You cannot easily set the display options for `Decimal` objects. If you have lots of Decimal columns and you are working interactively, this can quickly get annoying. #### Expected Output IMHO at the very least, we should be able to set the display precision. Ideally we should be able to use a callable similar to `float_format`. If that is the case, we should then be able to use custom formatters like e.g. [moneyfmt](https://docs.python.org/3.6/library/decimal.html#recipes). If no specific options for `Decimal` objects are present, I think that it makes sense to fall back to the float options. If that is not acceptable, I think that at least adhering to the precision option is reasonable. #### Output of ``pd.show_versions()`` <details>INSTALLED VERSIONS ------------------ commit: None python: 3.6.0.final.0 python-bits: 64 OS: Linux OS-release: 4.8.13-1-ARCH machine: x86_64 processor: byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 pandas: 0.19.2 nose: None pip: 9.0.1 setuptools: 33.1.1 Cython: None numpy: 1.12.0 scipy: 0.18.1 statsmodels: None xarray: None IPython: 5.1.0 sphinx: None patsy: None dateutil: 2.6.0 pytz: 2016.10 blosc: None bottleneck: None tables: None numexpr: None matplotlib: 2.0.0 openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: 0.9999999 httplib2: None apiclient: None sqlalchemy: None pymysql: None psycopg2: None jinja2: 2.9.4 boto: None pandas_datareader: None </details>
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15,172
Issue Opening Pre 0.12 Pickles Import Error
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3
2017-01-19T21:12:19Z
2017-01-19T22:40:26Z
2017-01-19T21:17:00Z
NONE
null
I am having an issue opening some old pickle files that I created a couple of years ago (July of 2014). These files were pickled using in the `cPickle` module in python and I am pretty sure using a version of pandas <=0.12. I was using `cPickle` to load the files and getting a `TypeError` until I found the following answers on stack overflow: http://stackoverflow.com/questions/20444593/pandas-compiled-from-source-default-pickle-behavior-changed http://stackoverflow.com/questions/27950991/pandas-backwards-compatibility-issue-with-pickle-0-14-1-and-0-15-2 The solution is to be to use `pd.read_pickle` to load these files instead of using the python module `cPickle`. However when I try using `pd.read_pickle` I get the following `ImportError`: ```python --------------------------------------------------------------------------- ImportError Traceback (most recent call last) <ipython-input-8-451b9dbea93c> in <module>() 1 data_dir="J:\Pat's Projects\Dynamical Phase Transition\Mosaic Trajectories" 2 #fit_params=cPickle.load(open(data_dir+'circle_fitting_params_071614.pkl','r')) ----> 3 expt_data=pd.read_pickle(data_dir+'data_frames_071614.pkl') 4 #expt_list=cPickle.load(open(data_dir+'expt_list_071614.pkl','r')) C:\Users\Scherer Lab E\Anaconda2\envs\170112\lib\site-packages\pandas\io\pickle.pyc in read_pickle(path) 63 64 try: ---> 65 return try_read(path) 66 except: 67 if PY3: C:\Users\Scherer Lab E\Anaconda2\envs\170112\lib\site-packages\pandas\io\pickle.pyc in try_read(path, encoding) 60 except: 61 with open(path, 'rb') as fh: ---> 62 return pc.load(fh, encoding=encoding, compat=True) 63 64 try: C:\Users\Scherer Lab E\Anaconda2\envs\170112\lib\site-packages\pandas\compat\pickle_compat.pyc in load(fh, encoding, compat, is_verbose) 115 up.is_verbose = is_verbose 116 --> 117 return up.load() 118 except: 119 raise C:\Users\Scherer Lab E\Anaconda2\envs\170112\lib\pickle.pyc in load(self) 862 while 1: 863 key = read(1) --> 864 dispatch[key](self) 865 except _Stop, stopinst: 866 return stopinst.value C:\Users\Scherer Lab E\Anaconda2\envs\170112\lib\pickle.pyc in load_global(self) 1094 module = self.readline()[:-1] 1095 name = self.readline()[:-1] -> 1096 klass = self.find_class(module, name) 1097 self.append(klass) 1098 dispatch[GLOBAL] = load_global C:\Users\Scherer Lab E\Anaconda2\envs\170112\lib\pickle.pyc in find_class(self, module, name) 1128 def find_class(self, module, name): 1129 # Subclasses may override this -> 1130 __import__(module) 1131 mod = sys.modules[module] 1132 klass = getattr(mod, name) ImportError: No module named copy_reg ``` Like I said, I don't know exactly what version of pandas was used when these were pickled but I think it was <=0.12. I am using python 2.7 installed from the Anaconda distribution. #### Output of ``pd.show_versions()`` INSTALLED VERSIONS ------------------ commit: None python: 2.7.12.final.0 python-bits: 64 OS: Windows OS-release: 7 machine: AMD64 processor: Intel64 Family 6 Model 30 Stepping 5, GenuineIntel byteorder: little LC_ALL: None LANG: None pandas: 0.18.1 nose: 1.3.7 pip: 8.1.2 setuptools: 27.2.0 Cython: 0.24 numpy: 1.11.1 scipy: 0.17.1 statsmodels: None xarray: None IPython: 4.2.0 sphinx: 1.4.1 patsy: 0.4.1 dateutil: 2.5.3 pytz: 2016.4 blosc: None bottleneck: 1.1.0 tables: 3.2.2 numexpr: 2.6.0 matplotlib: 2.0.0 openpyxl: 2.3.2 xlrd: 1.0.0 xlwt: 1.1.2 xlsxwriter: 0.9.2 lxml: 3.6.0 bs4: 4.4.1 html5lib: None httplib2: None apiclient: None sqlalchemy: 1.0.13 pymysql: None psycopg2: None jinja2: 2.8 boto: 2.40.0 pandas_datareader: None
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15,173
DOC: add subsequent_indent to wrapped text
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2
2017-01-19T22:09:04Z
2017-01-22T11:35:24Z
2017-01-22T11:35:13Z
NONE
null
Hey guys, This should fix the parsing error in the [read_csv documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html#pandas.read_csv) : ![image](https://cloud.githubusercontent.com/assets/5640848/22127326/c84fa65c-de9b-11e6-8702-5c01e8ce4d22.png) `fill` wraps the line to 70 characters (what's left after the beginning of the line) but it wasn't indenting the subsequent line, so the parser was marking it as a new parameter (in bold). This PR adds the missing indent, which should fix the parsing.
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15,174
bound method NDFrame: df.sample(n=ExpectedSize) returns df.size!=ExpectedSize
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2017-01-20T00:08:40Z
2017-01-20T00:34:15Z
2017-01-20T00:34:15Z
NONE
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I have this code (see screenshot for it's output) ``` #prerequisite: var X_first_neutral_data gets filled via csv read print("got a var of type: " + str(type(X_first_neutral_data)) + " that I want to take 10 samples from") print("size before: " + str(X_first_neutral_data.size)) X_first_neutral_data_less_data=X_first_neutral_data.sample(n=10) print("size should be 10 now, right?!?! -> " + str(X_first_neutral_data_less_data.size)) print("original var still same: " + str(X_first_neutral_data_less_data.size)) print() print(X_first_neutral_data.head) ``` #### Problem description Type of variable is pandas.core.frame.DataFrame calling myvar=myvar.sample(n=10) does change the size of the variable but not to 10. I don't think that my code is wrong because this code works as expected: ``` df=pd.DataFrame() df=pd.DataFrame(np.random.randint(0,100,size=(10, 7)), columns=list('ABCDEFG')) print(type(df)) print(df) df=df.sample(3) print(df) ``` Documentation of sample [here](http://pandas.pydata.org/pandas-docs/version/0.19.2/generated/pandas.DataFrame.sample.html) states: n = Number of items from axis to return. Cannot be used with frac. Default = 1 if frac = None. #### Expected Output ![pandasproblem](https://cloud.githubusercontent.com/assets/10122382/22130340/a130067e-deab-11e6-8e69-4e594d12080e.png) I have the newest pandas version: pandas-0.19.2 Am I missing something or is this a bug?
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202,021,038
MDExOlB1bGxSZXF1ZXN0MTAyMzgxNjUy
15,175
BUG: no need to validate monotonicity when groupby-rolling
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0
2017-01-20T01:04:55Z
2017-01-20T12:21:53Z
2017-01-20T12:21:53Z
CONTRIBUTOR
null
closes #15130
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202,037,016
MDU6SXNzdWUyMDIwMzcwMTY=
15,176
`axis` param for pd.Panel doesn't work
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1
2017-01-20T03:04:53Z
2018-07-06T20:21:14Z
2017-01-20T12:16:43Z
NONE
null
#### Code Sample, a copy-pastable example if possible ```python import numpy as np import pandas as pd a = pd.Panel({"A":{"a":[1,2,3,4,5], "b":[10, 20, np.nan, 40, 50]}) a.ffill(axis=0)["A"] a.ffill(axis=1)["A"] a.ffill(axis=2)["A"] ``` #### Problem description change to the param `axis` doesn't change the result. It all gives: a b 0 1.0 10.0 1 2.0 20.0 2 3.0 20.0 3 4.0 40.0 4 5.0 50.0 #### Expected Output a b 0 1.0 10.0 1 2.0 20.0 2 3.0 3.0 3 4.0 40.0 4 5.0 50.0 #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit: None python: 3.5.2.final.0 python-bits: 64 OS: Windows OS-release: 7 machine: AMD64 processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel byteorder: little LC_ALL: None LANG: None LOCALE: None.None pandas: 0.19.2 nose: 1.3.7 pip: 9.0.1 setuptools: 32.1.3 Cython: 0.25.2 numpy: 1.12.0 scipy: 0.18.1 statsmodels: 0.6.1 xarray: None IPython: 5.1.0 sphinx: 1.4.6 patsy: 0.4.1 dateutil: 2.6.0 pytz: 2016.10 blosc: None bottleneck: 1.1.0 tables: 3.2.2 numexpr: 2.6.1 matplotlib: 1.5.3 openpyxl: 2.3.2 xlrd: 1.0.0 xlwt: 1.1.2 xlsxwriter: 0.9.3 lxml: 3.6.4 bs4: 4.5.1 html5lib: None httplib2: None apiclient: None sqlalchemy: 1.0.13 pymysql: 0.7.9.None psycopg2: None jinja2: 2.8 boto: 2.42.0 pandas_datareader: None </details>
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202,106,065
MDExOlB1bGxSZXF1ZXN0MTAyNDM4MDcx
15,177
BLD: Remove csv dialect warning in io.rst
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5
2017-01-20T10:29:53Z
2017-01-20T20:56:12Z
2017-01-20T20:53:11Z
MEMBER
null
Title is self-explanatory. xref <a href="https://github.com/pandas-dev/pandas/pull/14911#issuecomment-273896473">#14911 (comment)</a>.
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202,116,264
MDU6SXNzdWUyMDIxMTYyNjQ=
15,178
rolling window function with multiple arguments by group
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2017-01-20T11:19:28Z
2017-01-20T12:13:03Z
2017-01-20T12:12:56Z
NONE
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#### Code Sample, a copy-pastable example if possible ```python ## let's say we have the below DF rand = np.random.RandomState(1) dff = pd.DataFrame({'A' : np.arange(20), 'B' : rand.randint(100, 120, 20), 'C' : rand.randint(0, 2, 20)}) ### Ideal solution, this does not work of course #dff['tau'] = dff.groupby('C').sort_values('A').rolling(window = 5).apply(sp.stats.mstats.kendalltau, args = ({'x' : 'A', 'y' : 'B')) ## workaround - need to pass an index, use a global DF, and (?) hard-code columns to use, use a to-be-deprecated function etc etc def my_tau_indx(indx): x = dff.iloc[indx, 0] y = dff.iloc[indx, 1] tau = sp.stats.mstats.kendalltau(x, y)[0] return tau grp = dff.sort_values(['A', 'C']).groupby('C', group_keys=False) func = lambda x: pd.Series(pd.rolling_apply(np.arange(len(x)), 5, my_tau_indx), x.index) t = grp.apply(func) dff.reindex(t.index).assign(tau=t) ``` #### Problem description There is no simple way to run a scipy/custom function requiring multiple arguments (by group) in a rolling window. In the code above, let's say that the 'C' column below is used for grouping. I need to - Group by column 'C' - Within each group, sort by 'A' - Withing each group, apply a rolling function taking two (or more) arguments, like kendalltau, to arguments 'A' and 'B'. I asked this question here: http://stackoverflow.com/questions/41715814/python-pandas-rolling-function-with-two-arguments-in-a-grouped-dataframe/41719105#41719105 . And the only viable solution I received uses a to-be-deprecated pd.rolling_apply. #### Expected Output See the link above for the expected results. See the code sample for what I'd like to write to get the result. #### Output of ``pd.show_versions()`` <details> # Paste the output here pd.show_versions() here DEBUG:pip.vcs:Registered VCS backend: git DEBUG:pip.vcs:Registered VCS backend: hg DEBUG:pip.pep425tags:Config variable 'Py_DEBUG' is unset, Python ABI tag may be incorrect DEBUG:pip.pep425tags:Config variable 'WITH_PYMALLOC' is unset, Python ABI tag may be incorrect DEBUG:pip.pep425tags:Config variable 'Py_DEBUG' is unset, Python ABI tag may be incorrect DEBUG:pip.pep425tags:Config variable 'WITH_PYMALLOC' is unset, Python ABI tag may be incorrect DEBUG:pip.vcs:Registered VCS backend: svn DEBUG:pip.vcs:Registered VCS backend: bzr INSTALLED VERSIONS ------------------ commit: None python: 3.5.2.final.0 python-bits: 64 OS: Windows OS-release: 7 machine: AMD64 processor: Intel64 Family 6 Model 62 Stepping 4, GenuineIntel byteorder: little LC_ALL: None LANG: en LOCALE: None.None pandas: 0.19.2 nose: None pip: 9.0.1 setuptools: 27.2.0 Cython: None numpy: 1.12.0rc2 scipy: 0.18.1 statsmodels: None xarray: None IPython: 5.1.0 sphinx: 1.4.8 patsy: 0.4.1 dateutil: 2.6.0 pytz: 2016.7 blosc: None bottleneck: None tables: None numexpr: None matplotlib: 1.5.3 openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: None httplib2: None apiclient: None sqlalchemy: None pymysql: None psycopg2: None jinja2: 2.8 boto: None pandas_datareader: None </details>
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MDExOlB1bGxSZXF1ZXN0MTAyNDUyMjQ0
15,179
DOC: reorg dialect in io.rst
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0
2017-01-20T12:11:46Z
2017-01-20T20:59:38Z
2017-01-20T20:59:38Z
CONTRIBUTOR
null
seems we had a section after 'Error Handling' in Parsing Options which is really basic, but out of place. Also moved Dialect to a proper sub-section.
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15,180
Unable to control import of 1st column from Excel file (with multi-row header)
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2
2017-01-20T12:16:47Z
2017-01-20T14:43:03Z
2017-01-20T12:51:18Z
NONE
null
Let's assume there are two excel files with almost identical content: file_1.xlsx: ![Imgur](http://i.imgur.com/TBIbCOu.png) file_2.xlsx: ![Imgur](http://i.imgur.com/MMihYl6.png) Then reading first file in pandas: df1 = pd.read_excel('file_1.xlsx', header=0, index_col=None, converters={'A0': str, 'B0': str}) print(df1) Would produce expected result: A0 B0 C0 D0 E0 0 0001 0004 0.1 1 a 1 0002 0005 0.2 2 b 2 0003 0006 0.3 3 c However trying the same with the second file: df2 = pd.read_excel('file_2.xlsx', header=[0,1], index_col=None, converters={('A0', 'A1'): str, ('A0', 'B1'): str}, ) print(df2) Would yield somewhat different and unexpected (in comparison with previous example) result: A0 A0 C0 E0 A1 B1 C1 D1 E1 1 0004 0.1 1 a 2 0005 0.2 2 b 3 0006 0.3 3 c Since it is not possible to use `has_index_names=False` as it has been depreciated since 0.16.2, there seems to be no way to have control over how pandas imports this first column (i.e. no way to convert values before original formatting is lost). And there is no way to tell pandas DO_NOT_ASSIGN first column to index as it ignores `index_col=None` when `header` is a list. So the question is what would be the sensible way to regain control over import process of first columns with multi-index header: - revive or de-depreciate (would that be appreciate?) `has_index_names`; - make `index_col` play a role in parsing header?
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15,181
Relocate exceptions #14800
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4
2017-01-20T12:53:48Z
2017-03-20T00:13:02Z
2017-03-20T00:13:01Z
CONTRIBUTOR
null
- [x] closes #14800 - [x] tests added / passed - [x] passes ``git diff master | flake8 --diff`` - [x] whatsnew entry Deprecation warnings will only be issued when exceptions/warnings are raised. I did think of putting a deprecation warning in ``pandas.common.core`` indicating that these exceptions were deprecated but these would be seen when *anything* was imported from ``pandas.core.common``. Please advise.
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202,190,320
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15,182
CategoricalData is converted to Objects when merging dataframes
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2017-01-20T16:56:57Z
2017-01-20T17:10:01Z
2017-01-20T17:02:33Z
CONTRIBUTOR
null
#### Code Sample ```python testData = pd.DataFrame(data={"id" : range(10), "cat" : pd.Categorical(["a"]*5 + ["b"]*5)}) print(testData.cat.dtype) # category testData2 = pd.DataFrame(data={"id" : range(10), "other" : np.random.randint(1,24,10)}) merged = pd.merge(testData, testData2,how="left", on="id") print(merged.cat.dtype) # object ``` #### Problem description When merging two dataframes, an otherwise untouched categorical column is converted to an ordinary column. #### Expected Output ```python print(merged.cat.dtype) # category ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit: 742d4a52d4db55793f6ca20014b619b5979627b9 python: 2.7.10.final.0 python-bits: 64 OS: Linux OS-release: 2.6.32-431.11.2.el6.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: en_GB.utf8 LANG: en_GB.utf8 LOCALE: None.None pandas: 0.19.0+346.g742d4a5 nose: 1.3.7 pip: 9.0.1 setuptools: 33.1.1 Cython: 0.25.2 numpy: 1.12.0 scipy: None statsmodels: None xarray: None IPython: None sphinx: None patsy: None dateutil: 2.6.0 pytz: 2016.10 blosc: None bottleneck: None tables: None numexpr: None feather: None 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 jinja2: None s3fs: None pandas_datareader: None </details>
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202,293,445
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BUG: Timestamp array comparison may raise RecursionError
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2
2017-01-21T05:16:37Z
2017-12-19T11:09:45Z
2017-12-19T11:09:45Z
MEMBER
null
#### Code Sample, a copy-pastable example if possible ```python import numpy as np import pandas as pd n1 = np.array([pd.Timestamp('2011-01-01 00:00:00'), pd.Timestamp('2011-01-03 00:00:00-0500', tz='US/Eastern')], dtype=object) pd.Timestamp('2011-01-03 00:00:00-0500', tz='US/Eastern') == n1 # RecursionError: maximum recursion depth exceeded ``` #### Problem description Related to #12780. This issue causes datetime-like `.replace` to raise ``SystemError``. Recursion is caused by this line: https://github.com/pandas-dev/pandas/blob/master/pandas/tslib.pyx#L1040 #### Expected Output raises ``TypeError``. #### Output of ``pd.show_versions()`` <details> # Paste the output here pd.show_versions() here </details>
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15,184
DEPS: require modern openpyxl
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0
2017-01-21T16:29:44Z
2017-12-04T11:35:57Z
2017-12-04T11:35:45Z
CONTRIBUTOR
null
we currently support as old as 1.6.1 we should bump this to 2.3 (released mid-2015). then can remove the compat for openpyxl < 2 code and fully support styles.
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15,185
DEPR: remove openpyxl deprecation warnings
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0
2017-01-21T16:31:05Z
2017-01-21T16:49:22Z
2017-01-21T16:49:22Z
CONTRIBUTOR
null
closes #15090
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202,353,459
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15,186
ERR: handle category dtype in .query with engine=numexpr
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3
2017-01-22T02:00:03Z
2020-04-26T20:56:25Z
null
NONE
null
#### Code Sample, a copy-pastable example if possible ```python # Your code here import pandas as pd BANDS = ["{}AM".format(i) for i in range(0,10)] df = pd.DataFrame({'Time': BANDS}) df.Time = df.Time.astype('category', ordered=True, categories=BANDS) df.query("Time >= '5AM'" ) ``` #### Problem description The query fails with a ValueError: unknown type object. Since Time is an ordered categorical and the query should return df[df.Time >= '5AM'] #### Expected Output Time 5 5AM 6 6AM 7 7AM 8 8AM 9 9AM #### Output of ``pd.show_versions()`` <details> # Paste the output here pd.show_versions() here INSTALLED VERSIONS ------------------ commit: None python: 3.5.2.final.0 python-bits: 64 OS: Linux OS-release: 4.4.41-36.55.amzn1.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 pandas: 0.19.2 nose: 1.3.7 pip: 9.0.1 setuptools: 27.2.0 Cython: 0.25.2 numpy: 1.11.3 scipy: 0.18.1 statsmodels: 0.6.1 xarray: None IPython: 5.1.0 sphinx: 1.5.1 patsy: 0.4.1 dateutil: 2.6.0 pytz: 2016.10 blosc: None bottleneck: 1.2.0 tables: 3.3.0 numexpr: 2.6.1 matplotlib: 1.5.3 openpyxl: 2.4.1 xlrd: 1.0.0 xlwt: 1.2.0 xlsxwriter: 0.9.6 lxml: 3.7.2 bs4: 4.5.3 html5lib: None httplib2: None apiclient: None sqlalchemy: 1.1.4 pymysql: None psycopg2: 2.6.2 (dt dec pq3 ext lo64) jinja2: 2.9.4 boto: 2.45.0 pandas_datareader: None </details>
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15,187
COMPAT: clarify Index integer conversions when dtype is specified in construction
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29
2017-01-22T15:40:30Z
2017-03-29T04:31:00Z
2017-03-10T23:41:49Z
CONTRIBUTOR
null
xref #15162 so [8], and [9] are our current model, IOW, we make an effort to convert to the specified type, but will coerce to an available type if the data is not valid for that dtype. ideally we would also be consistent w.r.t. #15145, IOW Series construction with a specified dtype (not that we upcast to the available Index types but don't do this for Series, e.g. [18]) So we should be consistent on this. ``` In [8]: Index([np.nan],dtype='int64') Out[8]: Float64Index([nan], dtype='float64') In [9]: Index([np.nan],dtype='uint64') Out[9]: Float64Index([nan], dtype='float64') In [10]: Index([np.iinfo(np.int64).max-1],dtype='int64') Out[10]: Int64Index([9223372036854775806], dtype='int64') In [11]: Index([np.iinfo(np.int64).max-1],dtype='uint64') Out[11]: UInt64Index([9223372036854775806], dtype='uint64') # I guess this should convert to Float64Index? In [12]: Index([np.iinfo(np.uint64).max-1],dtype='int64') --------------------------------------------------------------------------- OverflowError Traceback (most recent call last) <ipython-input-12-f2ec7d3c38a4> in <module>() ----> 1 Index([np.iinfo(np.uint64).max-1],dtype='int64') /Users/jreback/pandas/pandas/indexes/base.py in __new__(cls, data, dtype, copy, name, fastpath, tupleize_cols, **kwargs) 318 # other iterable of some kind 319 subarr = _asarray_tuplesafe(data, dtype=object) --> 320 return Index(subarr, dtype=dtype, copy=copy, name=name, **kwargs) 321 322 """ /Users/jreback/pandas/pandas/indexes/base.py in __new__(cls, data, dtype, copy, name, fastpath, tupleize_cols, **kwargs) 199 inferred = lib.infer_dtype(data) 200 if inferred == 'integer': --> 201 data = np.array(data, copy=copy, dtype=dtype) 202 elif inferred in ['floating', 'mixed-integer-float']: 203 OverflowError: Python int too large to convert to C long In [13]: Index([np.iinfo(np.uint64).max-1],dtype='uint64') Out[13]: UInt64Index([18446744073709551614], dtype='uint64') In [14]: Index([-1], dtype='int64') Out[14]: Int64Index([-1], dtype='int64') # this looks wrong In [15]: Index([-1], dtype='uint64') Out[15]: UInt64Index([18446744073709551615], dtype='uint64') # we do this type of same-dtype upcasting already (this is correct / good thing) In [18]: Index([np.iinfo(np.int32).max+1], dtype='int64') Out[18]: Int64Index([2147483648], dtype='int64') ```
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MDExOlB1bGxSZXF1ZXN0MTAyNjI1Njgz
15,188
CLN: remove build warnings in join_asof indexers
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2017-01-22T16:08:02Z
2017-01-22T17:20:19Z
2017-01-22T17:20:19Z
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15,189
CI: add matplotlib, xlwt to build for 3.6
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2017-01-22T16:08:25Z
2017-01-23T20:01:16Z
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202,396,030
MDU6SXNzdWUyMDIzOTYwMzA=
15,190
BLD: remove warnings for building parser / hashtable extensions
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7
2017-01-22T17:18:09Z
2017-01-31T08:18:08Z
2017-01-30T14:15:09Z
CONTRIBUTOR
null
https://travis-ci.org/jreback/pandas/jobs/194236743 see what we can do about some of these warnings, I think *some* are the result of the uint64 hashtable code. this would be after https://github.com/pandas-dev/pandas/pull/15188 (merging shortly) in parser.pyx ``` cythoning pandas/parser.pyx to pandas/parser.c building 'pandas.parser' extension creating build/temp.linux-x86_64-3.5/pandas/src creating build/temp.linux-x86_64-3.5/pandas/src/parser gcc -pthread -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -Ipandas/src/klib -Ipandas/src -I/home/travis/miniconda3/envs/pandas/lib/python3.5/site-packages/numpy/core/include -I/home/travis/miniconda3/envs/pandas/include/python3.5m -c pandas/parser.c -o build/temp.linux-x86_64-3.5/pandas/parser.o -Wno-unused-function In file included from /home/travis/miniconda3/envs/pandas/lib/python3.5/site-packages/numpy/core/include/numpy/ndarraytypes.h:1777:0, from /home/travis/miniconda3/envs/pandas/lib/python3.5/site-packages/numpy/core/include/numpy/ndarrayobject.h:18, from /home/travis/miniconda3/envs/pandas/lib/python3.5/site-packages/numpy/core/include/numpy/arrayobject.h:4, from pandas/parser.c:437: /home/travis/miniconda3/envs/pandas/lib/python3.5/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp] In file included from pandas/parser.c:439:0: pandas/src/numpy_helper.h: In function ‘transfer_object_column’: pandas/src/numpy_helper.h:131:19: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] In file included from pandas/parser.c:441:0: pandas/src/klib/khash_python.h: In function ‘asint64’: pandas/src/klib/khash_python.h:17:3: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing] pandas/parser.c: In function ‘__pyx_f_6pandas_6parser_10TextReader__get_header’: pandas/parser.c:9109:49: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/parser.c:9177:49: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/parser.c:9209:49: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/parser.c:10249:45: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/parser.c:10308:23: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/parser.c:10401:34: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/parser.c:10437:34: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/parser.c: In function ‘__pyx_f_6pandas_6parser__string_box_factorize’: pandas/parser.c:22294:35: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/parser.c: In function ‘__pyx_f_6pandas_6parser__string_box_utf8’: pandas/parser.c:22743:35: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/parser.c: In function ‘__pyx_f_6pandas_6parser__string_box_decode’: pandas/parser.c:23203:35: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/parser.c: In function ‘__pyx_f_6pandas_6parser__categorical_convert’: pandas/parser.c:23670:39: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/parser.c: In function ‘__pyx_f_6pandas_6parser__apply_converter’: pandas/parser.c:30308:37: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/parser.c:30446:37: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/parser.c:30558:37: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/parser.c: In function ‘__pyx_f_6pandas_6parser__fill_structured_column’: pandas/parser.c:31598:35: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/parser.c: In function ‘__pyx_f_6pandas_6parser_10TextReader__get_header’: pandas/parser.c:10283:61: warning: ‘__pyx_v_data_line’ may be used uninitialized in this function [-Wuninitialized] gcc -pthread -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -Ipandas/src/klib -Ipandas/src -I/home/travis/miniconda3/envs/pandas/lib/python3.5/site-packages/numpy/core/include -I/home/travis/miniconda3/envs/pandas/include/python3.5m -c pandas/src/parser/tokenizer.c -o build/temp.linux-x86_64-3.5/pandas/src/parser/tokenizer.o -Wno-unused-function pandas/src/parser/tokenizer.c: In function ‘tokenize_bytes’: pandas/src/parser/tokenizer.c:747:21: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/src/parser/tokenizer.c:762:21: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/src/parser/tokenizer.c:785:21: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/src/parser/tokenizer.c:827:21: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/src/parser/tokenizer.c:854:29: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/src/parser/tokenizer.c:863:25: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/src/parser/tokenizer.c:900:21: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/src/parser/tokenizer.c:948:21: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/src/parser/tokenizer.c:1015:21: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/src/parser/tokenizer.c:1033:21: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/src/parser/tokenizer.c:1043:21: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/src/parser/tokenizer.c:1046:25: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/src/parser/tokenizer.c:1049:25: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/src/parser/tokenizer.c:1060:25: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/src/parser/tokenizer.c:1077:41: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/src/parser/tokenizer.c: In function ‘parser_consume_rows’: pandas/src/parser/tokenizer.c:1165:15: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/src/parser/tokenizer.c:1202:19: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/src/parser/tokenizer.c: In function ‘parser_trim_buffers’: pandas/src/parser/tokenizer.c:1229:17: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/src/parser/tokenizer.c:1252:17: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/src/parser/tokenizer.c:1280:17: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/src/parser/tokenizer.c: In function ‘_tokenize_helper’: pandas/src/parser/tokenizer.c:1333:47: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/src/parser/tokenizer.c: In function ‘str_to_uint64’: pandas/src/parser/tokenizer.c:1991:16: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] gcc -pthread -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -Ipandas/src/klib -Ipandas/src -I/home/travis/miniconda3/envs/pandas/lib/python3.5/site-packages/numpy/core/include -I/home/travis/miniconda3/envs/pandas/include/python3.5m -c pandas/src/parser/io.c -o build/temp.linux-x86_64-3.5/pandas/src/parser/io.o -Wno-unused-function pandas/src/parser/io.c: In function ‘buffer_mmap_bytes’: pandas/src/parser/io.c:218:32: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] gcc -pthread -shared -L/home/travis/miniconda3/envs/pandas/lib -Wl,-rpath=/home/travis/miniconda3/envs/pandas/lib,--no-as-needed build/temp.linux-x86_64-3.5/pandas/parser.o build/temp.linux-x86_64-3.5/pandas/src/parser/tokenizer.o build/temp.linux-x86_64-3.5/pandas/src/parser/io.o -L/home/travis/miniconda3/envs/pandas/lib -lpython3.5m -o ``` in hashtable.pyx ``` cythoning pandas/hashtable.pyx to pandas/hashtable.c building 'pandas.hashtable' extension gcc -pthread -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -Ipandas/src/klib -Ipandas/src -I/home/travis/miniconda3/envs/pandas/lib/python3.5/site-packages/numpy/core/include -I/home/travis/miniconda3/envs/pandas/include/python3.5m -c pandas/hashtable.c -o build/temp.linux-x86_64-3.5/pandas/hashtable.o -Wno-unused-function In file included from /home/travis/miniconda3/envs/pandas/lib/python3.5/site-packages/numpy/core/include/numpy/ndarraytypes.h:1777:0, from /home/travis/miniconda3/envs/pandas/lib/python3.5/site-packages/numpy/core/include/numpy/ndarrayobject.h:18, from /home/travis/miniconda3/envs/pandas/lib/python3.5/site-packages/numpy/core/include/numpy/arrayobject.h:4, from pandas/hashtable.c:436: /home/travis/miniconda3/envs/pandas/lib/python3.5/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp] In file included from pandas/hashtable.c:438:0: pandas/src/klib/khash_python.h: In function ‘asint64’: pandas/src/klib/khash_python.h:17:3: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing] In file included from pandas/hashtable.c:439:0: pandas/src/numpy_helper.h: In function ‘transfer_object_column’: pandas/src/numpy_helper.h:131:19: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/hashtable.c: In function ‘__pyx_f_6pandas_9hashtable_value_count_float64’: pandas/hashtable.c:22504:39: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/hashtable.c: In function ‘__pyx_pf_6pandas_9hashtable_2duplicated_float64’: pandas/hashtable.c:23298:37: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/hashtable.c: In function ‘__pyx_f_6pandas_9hashtable_value_count_uint64’: pandas/hashtable.c:23829:39: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/hashtable.c: In function ‘__pyx_pf_6pandas_9hashtable_6duplicated_uint64’: pandas/hashtable.c:24623:37: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/hashtable.c: In function ‘__pyx_f_6pandas_9hashtable_value_count_object’: pandas/hashtable.c:25180:33: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/hashtable.c: In function ‘__pyx_pf_6pandas_9hashtable_10duplicated_object’: pandas/hashtable.c:25824:31: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/hashtable.c: In function ‘__pyx_f_6pandas_9hashtable_value_count_int64’: pandas/hashtable.c:26337:39: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/hashtable.c: In function ‘__pyx_pf_6pandas_9hashtable_14duplicated_int64’: pandas/hashtable.c:27130:37: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/hashtable.c: In function ‘__pyx_pf_6pandas_9hashtable_16mode_int64’: pandas/hashtable.c:27456:39: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/hashtable.c: In function ‘__pyx_pf_6pandas_9hashtable_18mode_uint64’: pandas/hashtable.c:27831:39: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] pandas/hashtable.c: In function ‘__pyx_pf_6pandas_9hashtable_20mode_object’: pandas/hashtable.c:28199:33: warning: comparison between signed and unsigned integer expressions [-Wsign-compare] gcc -pthread -shared -L/home/travis/miniconda3/envs/pandas/lib -Wl,-rpath=/home/travis/miniconda3/envs/pandas/lib,--no-as-needed build/temp.linux-x86_64-3.5/pandas/hashtable.o -L/home/travis/miniconda3/envs/pandas/lib -lpython3.5m -o /home/travis/build/jreback/pandas/pandas/hashtable.cpython-35m-x86_64-linux-gnu.so ```
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15,191
BLD: remove more build warnings
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0
2017-01-22T17:36:03Z
2017-01-22T21:42:57Z
2017-01-22T21:42:57Z
CONTRIBUTOR
null
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202,400,811
MDU6SXNzdWUyMDI0MDA4MTE=
15,192
Line breaks in Exception messages
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3
2017-01-22T18:41:04Z
2018-07-06T20:21:14Z
2017-01-24T18:58:24Z
CONTRIBUTOR
null
#### Code Sample, a copy-pastable example if possible ```python import pandas as pd df = pd.DataFrame({'a': [1, '2']}) df.to_hdf('tmp.h5', 'key', format='table') ``` #### Problem description I wonder if there is a reason why the exception message contains a line break: ``` TypeError: Cannot serialize the column [a] because its data contents are [mixed-integer] object dtype ``` The line breaks are explicitly introduced in module pandas/io/pytables.py: ``` raise TypeError( "Cannot serialize the column [%s] because\n" "its data contents are [%s] object dtype" % (item, inferred_type) ) ``` It should be noted that besides this message, there are many others exception massages with line breaks in this module. I make this question because I am logging the output and I think it would be better to have an event log per line. #### Expected Output ``` TypeError: Cannot serialize the column [a] because its data contents are [mixed-integer] object dtype ``` #### Output of ``pd.show_versions()`` <details> commit: None python: 3.5.1.final.0 python-bits: 64 OS: Linux OS-release: 3.13.0-107-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: ca_ES.UTF-8 pandas: 0.18.0 nose: 1.3.7 pip: 8.1.1 setuptools: 20.3 Cython: 0.23.4 numpy: 1.10.4 scipy: 0.17.0 statsmodels: 0.6.1 xarray: None IPython: 4.1.2 sphinx: 1.3.5 patsy: 0.4.0 dateutil: 2.5.1 pytz: 2016.2 blosc: None bottleneck: 1.0.0 tables: 3.2.2 numexpr: 2.5 matplotlib: 1.5.1 openpyxl: 2.3.2 xlrd: 0.9.4 xlwt: 1.0.0 xlsxwriter: 0.8.4 lxml: 3.6.0 bs4: 4.4.1 html5lib: None httplib2: None apiclient: None sqlalchemy: 1.0.12 pymysql: None psycopg2: None jinja2: 2.8 boto: 2.39.0 </details>
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202,419,471
MDU6SXNzdWUyMDI0MTk0NzE=
15,193
pivot_table over Categorical columns
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4
2017-01-22T23:24:09Z
2017-03-04T21:41:53Z
2017-03-04T21:41:53Z
NONE
null
#### Code Sample, a copy-pastable example if possible ```python stations = ['Kings Cross Station', 'Newtown Station', 'Parramatta Station', 'Town Hall Station', 'Central Station', 'Circular Quay Station', 'Martin Place Station', 'Museum Station', 'St James Station', 'Bondi Junction Station', 'North Sydney Station'] df = pd.DataFrame({'Station': ['Kings Cross Station', 'Newtown Station', 'Parramatta Station', 'Kings Cross Station', 'Newtown Station', 'Parramatta Station', 'Kings Cross Station', 'Newtown Station', 'Parramatta Station'], 'Date': pd.DatetimeIndex(['1/1/2017', '1/1/2017', '1/1/2017', '2/1/2017', '2/1/2017', '2/1/2017', '3/1/2017', '3/1/2017', '3/1/2017',]), 'Exit': range(0, 9)}) df.Station = df.Station.astype('category', ordered=True, categories=stations) df.pivot_table(index = 'Date', columns= 'Station', values = 'Exit', dropna=True) ``` #### Problem description When the column is a Categorical the output of pivot_table 1. Includes columns that are all NaN which should not be the case as dropna=True 2. Includes columns for categories that aren't in the input Data Frame, which is strange. 3. Is not the same as when the output before the column is converted to a categorical This was not the behaviour in earlier versions (18?) #### Expected Output Should be the same as the output when the column is not a categorical Station Kings Cross Station Newtown Station Parramatta Station Date 2017-01-01 0 1 2 2017-02-01 3 4 5 2017-03-01 6 7 8 #### Output of ``pd.show_versions()`` <details> # Paste the output here pd.show_versions() here INSTALLED VERSIONS ------------------ commit: None python: 3.5.2.final.0 python-bits: 64 OS: Linux OS-release: 4.4.41-36.55.amzn1.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 pandas: 0.19.2 nose: 1.3.7 pip: 9.0.1 setuptools: 27.2.0 Cython: 0.25.2 numpy: 1.11.3 scipy: 0.18.1 statsmodels: 0.6.1 xarray: None IPython: 5.1.0 sphinx: 1.5.1 patsy: 0.4.1 dateutil: 2.6.0 pytz: 2016.10 blosc: None bottleneck: 1.2.0 tables: 3.3.0 numexpr: 2.6.1 matplotlib: 1.5.3 openpyxl: 2.4.1 xlrd: 1.0.0 xlwt: 1.2.0 xlsxwriter: 0.9.6 lxml: 3.7.2 bs4: 4.5.3 html5lib: None httplib2: None apiclient: None sqlalchemy: 1.1.4 pymysql: None psycopg2: 2.6.2 (dt dec pq3 ext lo64) jinja2: 2.9.4 boto: 2.45.0 pandas_datareader: None </details>
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202,426,704
MDU6SXNzdWUyMDI0MjY3MDQ=
15,194
`lines` kwarg in `pd.read_json`
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1
2017-01-23T01:12:15Z
2017-01-23T01:14:55Z
2017-01-23T01:14:42Z
NONE
null
#### Code Sample, a copy-pastable example if possible ```python with open("clean.json", "r+") as data: df = pd.read_json(path_or_buf=data, lines=True) ``` #### Problem description In the documentation, `lines` is a kwarg that can be used to specify whether the JSON file has multiple lines. This code worked a month ago, where `df` was a dataframe where each entry was a line of `clean.json`. #### Output Instead, I get `TypeError: read_json() got an unexpected keyword argument 'lines'`
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MDExOlB1bGxSZXF1ZXN0MTAyNjY0OTAy
15,195
BUG: Fixed incorrect stream size check (#14125)
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7
2017-01-23T06:38:42Z
2017-01-24T23:14:06Z
2017-01-24T23:13:38Z
CONTRIBUTOR
null
- [X] closes #14125 - [X] tests added / passed: test_large_difference_in_columns in c_parser_only.py, Other tests passed. - [X] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry: Added to 0.20 whatsnew Issue summary: Previously, self->stream_cap was copied into a local variable called maxstreamsize each time tokenize_bytes ran, and then this was checked in the PUSH_CHAR macro. However, there is one other place in the file where function make_stream_space() is called (in end_line()), and when this happens self->stream_cap is increased but maxstreamsize is not updated, making the check incorrect. In rare circumstances (see original issue or test case) this could cause a crash. The resolution is to just check self->stream_cap directly.
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15,196
Fix bug in window function count should count anything non-null
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4
2017-01-23T13:39:54Z
2017-01-24T18:02:30Z
2017-01-24T18:02:16Z
CONTRIBUTOR
null
- [x] closes #12541 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` - [x] whatsnew entry
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15,197
PERF: DataFrame.groupby.nunique is non-performant
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1
2017-01-23T17:22:32Z
2017-01-24T14:20:30Z
2017-01-24T14:20:30Z
CONTRIBUTOR
null
xref #14376 ``` # from the asv In [10]: n = 10000 ...: df = DataFrame({'key1': randint(0, 500, size=n), ...: 'key2': randint(0, 100, size=n), ...: 'ints': randint(0, 1000, size=n), ...: 'ints2': randint(0, 1000, size=n), }) ...: In [11]: %timeit df.groupby(['key1', 'key2']).nunique() 1 loop, best of 3: 4.25 s per loop In [12]: result = df.groupby(['key1', 'key2']).nunique() In [13]: g = df.groupby(['key1', 'key2']) In [14]: expected = pd.concat([getattr(g, col).nunique() for col in g._selected_obj.columns], axis=1) In [15]: result.equals(expected) Out[15]: True In [16]: %timeit pd.concat([getattr(g, col).nunique() for col in g._selected_obj.columns], axis=1) 100 loops, best of 3: 6.94 ms per loop ``` ``Series.groupby.nunique`` has a very performant implementation, but the way the ``DataFrame.groupby.nunique`` is implemented (via ``.apply``) it ends up in a python loop over the groups, which nullifies this. should be straightforward to fix this. need to make sure to test with ``as_index=True/False``
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15,198
API: .str ops on category should return category if result is non-boolean
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4
2017-01-23T18:23:00Z
2021-05-08T00:37:51Z
null
CONTRIBUTOR
null
In the PR implementing ``.str/.dt`` on Categoricals, https://github.com/pandas-dev/pandas/pull/11582. This is perfectly reasonable. We perform the string op on the uniques. This routine is a boolean result, so we return a boolean result. ``` In [2]: s = pd.Series(list('aabb')).astype('category') In [3]: s Out[3]: 0 a 1 a 2 b 3 b dtype: category Categories (2, object): [a, b] In [4]: s.str.contains("a") Out[4]: 0 True 1 True 2 False 3 False dtype: bool ``` However, I don't recall the rationale for: performing the op on the uniques (as its a categorical), but then returning an ``object`` dtype. ``` In [5]: s.str.upper() Out[5]: 0 A 1 A 2 B 3 B dtype: object ``` These are by-definition pure transforms, and so a new categorical makes sense. e.g. in this case ``` In [6]: pd.Series(pd.Categorical.from_codes(s.cat.codes, s.cat.categories.str.upper())) Out[6]: 0 A 1 A 2 B 3 B dtype: category Categories (2, object): [A, B] ``` This will be way more efficient than actually converting to object.
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BLD: numpy-dev causing some issues
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2017-01-23T20:37:45Z
2017-01-24T18:52:39Z
2017-01-24T18:52:39Z
CONTRIBUTOR
null
https://travis-ci.org/pandas-dev/pandas/jobs/194558937 https://github.com/numpy/numpy/pull/8279 was just merged, which broke our tests :>
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COMPAT: don't rely on dtype being False after numpy >= 1.13
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1
2017-01-23T21:18:57Z
2017-01-24T18:52:39Z
2017-01-24T18:52:39Z
CONTRIBUTOR
null
closes #15199
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PERF: DataFrame.groupby.nunique
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0
2017-01-23T22:15:25Z
2017-01-24T14:20:30Z
2017-01-24T14:20:30Z
CONTRIBUTOR
null
closes #15197 ``` In [2]: %timeit df.groupby(['key1', 'key2']).nunique() 100 loops, best of 3: 9.31 ms per loop ``` ``` before after ratio [be3f2aea] [6d026165] - 473.67μs 427.27μs 0.90 groupby.GroupBySuite.time_nunique('float', 100) - 4.41s 9.53ms 0.00 groupby.groupby_nunique.time_groupby_nunique SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY. ```
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BUG: GH14233 resample().median() failed if duplicate column names wer…
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2
2017-01-23T22:48:25Z
2017-02-28T15:43:52Z
2017-01-24T18:46:27Z
CONTRIBUTOR
null
Simple fix for median issue. Should use cython implementation. - [X] closes #14233 - [X] tests added / passed - [X] passes ``git diff upstream/master | flake8 --diff`` - [X] whatsnew entry
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202,696,985
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15,203
read_csv: Rows with extra columns set data in index
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10
2017-01-24T01:04:42Z
2019-01-23T16:44:45Z
null
CONTRIBUTOR
null
#### Code Sample ```python from io import StringIO import pandas as pd # Notice that the second row has an extra column data = """UNITID,XTUIT1,TUITION1 100636,"R",0, """ f = StringIO(data) df = pd.read_csv(f) print(df) ``` #### Actual Output ``` UNITID XTUIT1 TUITION1 100636 R 0 ``` #### Problem description This behavior shifts column names, where the first column of data no longer matches the first column name. It also sets the extra data as the index, leading to very unexpected results. #### Expected Output ``` UNITID XTUIT1 TUITION1 Unnamed: 3 0 100636 R 0 ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit: None python: 3.6.0.final.0 python-bits: 64 OS: Darwin OS-release: 16.3.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 pandas: 0.19.2 nose: None pip: 9.0.1 setuptools: 34.0.1 Cython: None numpy: 1.12.0 scipy: None statsmodels: None xarray: None IPython: 5.1.0 sphinx: None patsy: None dateutil: 2.6.0 pytz: 2016.10 blosc: None bottleneck: None tables: None numexpr: None matplotlib: 2.0.0 openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: 0.9999999 httplib2: None apiclient: None sqlalchemy: None pymysql: None psycopg2: None jinja2: 2.9.4 boto: None pandas_datareader: None </details>
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202,727,319
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15,204
ENH: Allow elementwise coloring in background_gradient with axis=None
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8
2017-01-24T05:19:20Z
2018-07-12T12:38:38Z
2018-07-12T12:38:38Z
CONTRIBUTOR
null
At present the axis argument to `background_gradient` only accepts 0 or 1 so that the resulting "heatmap" can only be applied columnwise or rowwise rather than to the whole table. ``` Signature: df.style.background_gradient(cmap='PuBu', low=0, high=0, axis=0, subset=None) Parameters ---------- cmap: str or colormap matplotlib colormap low, high: float compress the range by these values. axis: int or str 1 or 'columns' for colunwise, 0 or 'index' for rowwise subset: IndexSlice a valid slice for ``data`` to limit the style application to ``` I propose that `axis=None` be allowed to mean applying a color gradient elementwise.
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202,752,293
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15,205
BUG: groupby.cummin/cummax nans inf values for int64
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4
2017-01-24T08:22:38Z
2017-12-20T02:04:04Z
2017-01-25T19:05:23Z
MEMBER
null
- [x] closes #15109 - [x] tests added / passed - [x] passes ``git diff upstream/master | flake8 --diff`` I migrated the `accum` portions of the code into cython and attempted to implement your suggested method to fix this issue @jreback. Let me know if this is along the right track.
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DEPS: drop numpy < 1.9
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6
2017-01-24T13:57:20Z
2017-08-22T09:50:58Z
2017-08-22T09:50:58Z
CONTRIBUTOR
null
this would provide some code cleanup possibilities. - [ ] use ``np.full`` (see https://github.com/pandas-dev/pandas/pull/17000 for example)
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ValueError("Cannot describe a DataFrame without columns") ValueError: Cannot describe a DataFrame without columns
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3
2017-01-24T14:55:37Z
2017-01-24T15:46:19Z
2017-01-24T15:02:48Z
NONE
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This is my TestWorkbook.xlsx in same location as my python script: ``` Data Time Variable Value 08/01/17 06:00:30 Occupancy.1m.15s.mean 497.714 08/01/17 06:00:30 QueueTime.1m.15s.mean 698.999 08/01/17 06:00:30 QueueStatus.1m.15s.mean 42.8639 08/01/17 06:00:30 QueueTime.1m.15s.mean 113.139 ``` ```python import pandas as pd import numpy as np import glob all_data = pd.DataFrame() res = glob.glob("*.xlsx") for f in glob.glob("TestWorkbook.xlsx"): df = pd.read_excel(f, sheetname='Sheet1') all_data = all_data.append(df, ignore_index=True) all_data.describe() ``` Not sure why it is returning error raise ValueError("Cannot describe a DataFrame without columns") ValueError: Cannot describe a DataFrame without columns
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