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} | 4 | 2016-03-01T16:59:03Z | 2016-03-03T22:21:48Z | 2016-03-03T21:55:35Z | MEMBER | null | Addresses issue in #12494 by allowing `grow_buffer` to grow the size of the parser buffer when buffer capacity is achieved. Previously, you had to exceed capacity for this to occur, but that was inconsistent with the `end_field` check later on when handling the EOF terminator, where reached capacity was considered a buffer overflow.
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} | 1 | 2016-03-02T00:44:41Z | 2016-03-03T14:43:57Z | 2016-03-02T12:46:00Z | CONTRIBUTOR | null | - [x] closes #12386
- [x] tests passed
- [x] whatsnew entry
Removed pandas.util.testing.choice() method.
Replaced all references to it with calls to np.random.choice().
Added entry to whatsnew.
#12386
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} | 50 | 2016-03-02T01:45:47Z | 2016-04-13T01:32:13Z | 2016-04-13T01:30:28Z | MEMBER | null | closes #12493
in which the `usecols` argument was not being respected for empty data. This is because no filtering was applied when the first (and only) chunk was being read.
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| null | 2 | 2016-03-02T02:43:31Z | 2016-03-02T12:36:03Z | 2016-03-02T12:35:29Z | NONE | null | #### Code Sample, a copy-pastable example if possible
``` python
import pandas as pd
pd.show_versions()
df = pd.DataFrame({'a':['01','02','03','04'], 'b':[5,6,7,8]})
df
df.dtypes
df.to_excel('tmp.xlsx')
df.to_csv('tmp.csv')
# Load without specifying dtypes or converter
loaded_df1 = pd.read_excel('tmp.xlsx')
loaded_df1
loaded_df1.dtypes
# Load specifying dtypes
loaded_df2 = pd.read_excel('tmp.xlsx',dtypes={'a':str,'b':int})
loaded_df2
loaded_df2.dtypes # Note that column 'a' is still of type int
# Load specifying converters
loaded_df3 = pd.read_excel('tmp.xlsx',dtypes={'a':str,'b':int})
loaded_df3
loaded_df3.dtypes # Note that column 'a' is now a string, as anticipated
# Load specifying an invalid kwarg
loaded_df4 = pd.read_excel('tmp.xlsx', invalid_kwarg=['foo','bar'])
# Load using read_csv with an invalid kwarg (should raise a TypeError)
loaded_df5 = pd.read_csv('tmp.csv', invalid_kwarg=['foo','bar'])
```
#### Expected Output
This issue is related to #8212. The dtypes kwarg is not documented as an available kwarg for the read_excel documentation. However, it also does not raise an error as an invalid kwarg if specified. When used, it appears to be ignored, but it seems that raising an error would be more helpful to the user. I know that the converters kwarg should be used instead, but this isn't obvious if you are used to read_csv and read_table, which both rely on the dtypes kwarg. More broadly, it seems like read_excel allows for any kwarg, unlike read_csv or read_table.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.0.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-51-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 8.0.3
setuptools: 20.1.1
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
IPython: 4.0.2
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.2
lxml: 3.5.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
Jinja2: None
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} | 2 | 2016-03-02T09:36:25Z | 2017-03-09T12:06:13Z | 2017-03-09T12:05:47Z | NONE | null | Hello Developers,
I could not find any information about "Pandas.IndexSlice" function in API Reference.There is only one example at this [link](http://pandas.pydata.org/pandas-docs/stable/advanced.html#advanced-mi-slicers) . I want to filter the pivot table as in Excel.But i am confused to using between loc(slice(...)) and IndexSlice.Please could you show some practical easy methods to filter the pivot table.
Thank you very much...
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} | 3 | 2016-03-02T11:21:17Z | 2019-10-21T19:52:15Z | null | NONE | null | I just ran into the same problem as the person asking this question on SO
http://stackoverflow.com/questions/29709918/pandas-and-category-replacement
[Jeff](http://stackoverflow.com/users/644898/jeff) gave an excellent answer as usual, I believe he is a pandas developer as well?
So I was wondering whether something like [hist answer](http://stackoverflow.com/a/29712287/1532474) might be planned to become the default behaviour, when appending Categoricals.
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| null | 2 | 2016-03-02T12:49:48Z | 2016-03-02T13:11:10Z | 2016-03-02T12:59:46Z | NONE | null | This is using a fresh install of Anaconda in an Ubuntu 15.10 based Google Cloud VM. At first glance it seemed to relate to difficulties getting console encoding (see `from pandas.core.format import detect_console_encoding` below)?
``` python
import pandas
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-1-f0ee645c240d> in <module>()
----> 1 import pandas as pd
2 import dask.dataframe as dd
3 from dask.diagostics import ProgressBar as pb
/home/michael/anaconda3/lib/python3.5/site-packages/pandas/__init__.py in <module>()
40
41 # let init-time option registration happen
---> 42 import pandas.core.config_init
43
44 from pandas.core.api import *
/home/michael/anaconda3/lib/python3.5/site-packages/pandas/core/config_init.py in <module>()
15 is_instance_factory, is_one_of_factory,
16 get_default_val)
---> 17 from pandas.core.format import detect_console_encoding
18
19
/home/michael/anaconda3/lib/python3.5/site-packages/pandas/core/format.py in <module>()
8 from pandas.core.base import PandasObject
9 from pandas.core.common import adjoin, notnull
---> 10 from pandas.core.index import Index, MultiIndex, _ensure_index
11 from pandas import compat
12 from pandas.compat import(StringIO, lzip, range, map, zip, reduce, u,
/home/michael/anaconda3/lib/python3.5/site-packages/pandas/core/index.py in <module>()
29 from pandas.core.strings import StringAccessorMixin
30 from pandas.core.config import get_option
---> 31 from pandas.io.common import PerformanceWarning
32
33
/home/michael/anaconda3/lib/python3.5/site-packages/pandas/io/common.py in <module>()
66
67 try:
---> 68 from boto.s3 import key
69 class BotoFileLikeReader(key.Key):
70 """boto Key modified to be more file-like
/home/michael/anaconda3/lib/python3.5/site-packages/boto/__init__.py in <module>()
1214 return storage_uri(uri_str)
1215
-> 1216 boto.plugin.load_plugins(config)
/home/michael/anaconda3/lib/python3.5/site-packages/boto/plugin.py in load_plugins(config)
90 return
91 directory = config.get('Plugin', 'plugin_directory')
---> 92 for file in glob.glob(os.path.join(directory, '*.py')):
93 _import_module(file)
/home/michael/anaconda3/lib/python3.5/posixpath.py in join(a, *p)
87 path += sep + b
88 except (TypeError, AttributeError, BytesWarning):
---> 89 genericpath._check_arg_types('join', a, *p)
90 raise
91 return path
/home/michael/anaconda3/lib/python3.5/genericpath.py in _check_arg_types(funcname, *args)
141 else:
142 raise TypeError('%s() argument must be str or bytes, not %r' %
--> 143 (funcname, s.__class__.__name__)) from None
144 if hasstr and hasbytes:
145 raise TypeError("Can't mix strings and bytes in path components") from None
TypeError: join() argument must be str or bytes, not 'NoneType'
```
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} | 5 | 2016-03-02T13:37:13Z | 2016-03-03T12:11:02Z | 2016-03-03T12:11:02Z | CONTRIBUTOR | null | close #12489
xref #11915
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} | 13 | 2016-03-02T15:14:52Z | 2016-04-06T19:29:40Z | 2016-04-06T19:17:26Z | MEMBER | null | closes #9755
closes #12678
Continuing on my conquest of `read_csv` bugs, this PR fixes a bug brought up in #9755 in processing `parse_dates` with the C engine in which the wrong indices (those of the filtered column names) were being used to determine the date columns to not be dtype-parsed by the C engine. The correct indices are those of the original column names, as they are used later on in the actual data processing.
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} | 13 | 2016-03-02T16:19:56Z | 2021-05-17T22:50:26Z | 2020-01-01T16:28:29Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
``` Python
import pandas as pd
import numpy as np
array_dim2 = np.arange(10).reshape((5, 2))
df = pd.DataFrame(array_dim2 , dtype='datetime64[ns, UTC]') # doesn't work
```
The error:
``` Python
TypeError Traceback (most recent call last)
<ipython-input-4-7101cf798aa3> in <module>()
----> 1 df = pd.DataFrame(array_dim2 , dtype='datetime64[ns, UTC]')
C:\D\Projects\Github\pandas\pandas\core\frame.py in __init__(self, data, index,
columns, dtype, copy)
252 else:
253 mgr = self._init_ndarray(data, index, columns, dtype=dty
pe,
--> 254 copy=copy)
255 elif isinstance(data, (list, types.GeneratorType)):
256 if isinstance(data, types.GeneratorType):
C:\D\Projects\Github\pandas\pandas\core\frame.py in _init_ndarray(self, values,
index, columns, dtype, copy)
412
413 if dtype is not None:
--> 414 if values.dtype != dtype:
415 try:
416 values = values.astype(dtype)
TypeError: data type not understood
```
#### Expected Output
``` Python
In [5]: df = pd.DataFrame(array_dim2 , dtype='datetime64[ns, UTC]')
In [6]: df
Out[6]:
0 1
0 1970-01-01 00:00:00.000000000+00:00 1970-01-01 00:00:00.000000001+00:00
1 1970-01-01 00:00:00.000000002+00:00 1970-01-01 00:00:00.000000003+00:00
2 1970-01-01 00:00:00.000000004+00:00 1970-01-01 00:00:00.000000005+00:00
3 1970-01-01 00:00:00.000000006+00:00 1970-01-01 00:00:00.000000007+00:00
4 1970-01-01 00:00:00.000000008+00:00 1970-01-01 00:00:00.000000009+00:00
```
#### output of `pd.show_versions()`
```
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.0rc1+66.gce3ac93
nose: 1.3.7
pip: 8.0.2
setuptools: 19.2
Cython: 0.23.4
numpy: 1.10.1
scipy: None
statsmodels: None
xarray: None
IPython: 4.0.2
sphinx: 1.3.1
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: 2.3.3
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: None
lxml: 3.5.0
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12514 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12514/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12514/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12514/events | https://github.com/pandas-dev/pandas/pull/12514 | 137,934,491 | MDExOlB1bGxSZXF1ZXN0NjE0MzM3NTU= | 12,514 | Add a UK England and Wales holiday calendar | {
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} | 2 | 2016-03-02T17:13:07Z | 2016-03-03T02:13:20Z | 2016-03-03T02:13:09Z | NONE | null | The rules are described at:
https://en.wikipedia.org/wiki/Public_holidays_in_the_United_Kingdom#England.2C_Northern_Ireland_and_Wales
Sample data used to check those rules (which is also included in tests)
is from the government's website: https://www.gov.uk/bank-holidays
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| null | 14 | 2016-03-02T22:42:42Z | 2016-03-03T22:34:27Z | 2016-03-03T02:00:39Z | CONTRIBUTOR | null | z-scoring (centering a variable at its mean and dividing by its standard deviation) is a common statistical operation. However, it ends up being rather hard to do if one's data is represented by a pandas `Series` object.
There's a few things one might think of to try. The `scipy.stats` module has a `zscore` function, but applying it to a `Series` raises an exception (I think because it doesn't know what to do with a column vector? I'm not sure):
``` python
s = pd.Series(np.random.rand(10))
s.apply(stats.zscore)
```
``` pytb
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-966-4f828190fd49> in <module>()
1 s = pd.Series(np.random.rand(10))
----> 2 s.apply(stats.zscore)
/Users/mwaskom/anaconda/lib/python2.7/site-packages/pandas/core/series.pyc in apply(self, func, convert_dtype, args, **kwds)
2167 values = lib.map_infer(values, lib.Timestamp)
2168
-> 2169 mapped = lib.map_infer(values, f, convert=convert_dtype)
2170 if len(mapped) and isinstance(mapped[0], Series):
2171 from pandas.core.frame import DataFrame
pandas/src/inference.pyx in pandas.lib.map_infer (pandas/lib.c:62578)()
/Users/mwaskom/anaconda/lib/python2.7/site-packages/scipy/stats/stats.pyc in zscore(a, axis, ddof)
2105 """
2106 a = np.asanyarray(a)
-> 2107 mns = a.mean(axis=axis)
2108 sstd = a.std(axis=axis, ddof=ddof)
2109 if axis and mns.ndim < a.ndim:
/Users/mwaskom/anaconda/lib/python2.7/site-packages/numpy/core/_methods.pyc in _mean(a, axis, dtype, out, keepdims)
54 arr = asanyarray(a)
55
---> 56 rcount = _count_reduce_items(arr, axis)
57 # Make this warning show up first
58 if rcount == 0:
/Users/mwaskom/anaconda/lib/python2.7/site-packages/numpy/core/_methods.pyc in _count_reduce_items(arr, axis)
48 items = 1
49 for ax in axis:
---> 50 items *= arr.shape[ax]
51 return items
52
IndexError: tuple index out of range
```
Actually writing out the formula using a lambda returns a `Series` of nulls:
``` python
s.apply(lambda x: (x - x.mean()) / x.std())
```
```
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
5 NaN
6 NaN
7 NaN
8 NaN
9 NaN
dtype: float64
```
And calling `stats.zscore` does not preserve the pandas metadata:
``` python
stats.zscore(s)
```
```
array([-0.3957885 , -0.89938219, 1.51650231, -1.01533894, 1.73900501,
-1.08299489, -0.90929288, 0.08315865, 0.80475036, 0.15938106])
```
This is at least partially scipy's fault (particularly for the last example), but is it possible to change something in pandas so that this is easier? I am not exactly sure what's causing the exception in the simplest option.
Perhaps the best thing to do would be to add a `zscore` method to `Series objects?
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 13.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: None
setuptools: 18.3.1
Cython: 0.20.1
numpy: 1.10.2
scipy: 0.16.1
statsmodels: 0.6.1
IPython: 4.0.3
sphinx: 1.2.3
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.5.1
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.5
lxml: 3.3.5
bs4: 4.3.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
Jinja2: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12516 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12516/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12516/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12516/events | https://github.com/pandas-dev/pandas/issues/12516 | 138,023,646 | MDU6SXNzdWUxMzgwMjM2NDY= | 12,516 | Bug: In pandas.util.testing.assert_almost_equal with regards to datetime64 values | {
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| null | 1 | 2016-03-02T23:17:42Z | 2016-03-03T02:12:39Z | 2016-03-03T02:12:27Z | NONE | null | #### Code Sample, a copy-pasteable example if possible
``` python
from dateutil.parser import parse
from datetime import datetime
import numpy as np
import pandas as pd
import pandas.util.testing
test_data = pd.DataFrame({
'_18_months_eo': np.array([0.05, 0.06, 0.12, 0.15], dtype='float64'),
'_pct_dlq': np.array([0.432, 1.352, 53.23, 0.532], dtype='float64'),
'_dollar_spent': np.array([1000.00, -1000.00, 15000.00, 0.01], dtype='float64'),
'_borrower_name': np.array(['John', 'Mary', 'Sue', 'Nathan'], dtype='object'),
'_origination_date': np.array(['2015-04-04', pd.NaT, '2008-03-03', 'Mar 2014'], dtype='object')
})
i = pd.to_datetime(datetime.date(parse('Mar 2014')))
print(i)
test_data['_origination_date'][3] = i
expected_result = pd.DataFrame({
'_borrower_name': np.array(['John', 'Mary', 'Sue', 'Nathan'], dtype='object'),
'_dollar_spent': np.array([1000.00, -1000.00, 15000.00, 0.01], dtype='float64'),
'_pct_dlq': np.array([0.432, 1.352, 53.23, 0.532], dtype='float64'),
'_18_months_eo': np.array([0.05, 0.06, 0.12, 0.15], dtype='float64'),
'_origination_date': pd.Series(['2015-04-04', pd.NaT, '2008-03-03', '2014-03-01'], dtype='datetime64[ns]')
})
expected_result = expected_result.reindex_axis(sorted(expected_result.columns), axis=1)
test_data = test_data.reindex_axis(sorted(test_data.columns), axis=1)
pd.util.testing.assert_frame_equal(expected_result, test_data)
```
The Output:
``` python
2014-03-02 00:00:00
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/ipykernel/__main__.py:18: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-17-9ddff7b0ad9c> in <module>()
30 test_data = test_data.reindex_axis(sorted(test_data.columns), axis=1)
31
---> 32 pd.util.testing.assert_frame_equal(expected_result, test_data)
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/util/testing.pyc in assert_frame_equal(left, right, check_dtype, check_index_type, check_column_type, check_frame_type, check_less_precise, check_names, by_blocks, check_exact, check_datetimelike_compat, obj)
1047 check_names=check_names,
1048 check_datetimelike_compat=check_datetimelike_compat,
-> 1049 obj='DataFrame.iloc[:, {0}]'.format(i))
1050
1051
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/util/testing.pyc in assert_series_equal(left, right, check_dtype, check_index_type, check_series_type, check_less_precise, check_names, check_exact, check_datetimelike_compat, obj)
944 else:
945 assert_almost_equal(left.get_values(), right.get_values(),
--> 946 check_less_precise, obj='{0}'.format(obj))
947
948 # metadata comparison
pandas/src/testing.pyx in pandas._testing.assert_almost_equal (pandas/src/testing.c:3809)()
pandas/src/testing.pyx in pandas._testing.assert_almost_equal (pandas/src/testing.c:2685)()
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/util/testing.pyc in raise_assert_detail(obj, message, left, right)
817 [left]: {2}
818 [right]: {3}""".format(obj, message, left, right)
--> 819 raise AssertionError(msg)
820
821
AssertionError: DataFrame.iloc[:, 3] are different
DataFrame.iloc[:, 3] values are different (25.0 %)
[left]: [2015-04-03T20:00:00.000000000-0400, NaT, 2008-03-02T19:00:00.000000000-0500, 2014-02-28T19:00:00.000000000-0500]
[right]: [2015-04-03T20:00:00.000000000-0400, NaT, 2008-03-02T19:00:00.000000000-0500, 2014-03-01T19:00:00.000000000-0500]
```
#### Expected Output
Assertion returns 'True' if one were to change expected_result['_origination_date'][3] to '2014-03-02'.
However, the value of '2014-02-28' in the assertion is concerning given that the values in the DataFrame at that position is '2014-03-02 00:00:00.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: None
pip: 8.0.2
setuptools: 20.0
Cython: 0.23.4
numpy: 1.10.2
scipy: 0.14.0
statsmodels: None
IPython: 4.1.1
sphinx: None
patsy: 0.3.0
dateutil: 2.3
pytz: 2014.10
blosc: None
bottleneck: None
tables: None
numexpr: 2.4.6
matplotlib: None
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
Jinja2: None
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| null | 3 | 2016-03-03T02:40:30Z | 2016-03-03T03:05:22Z | 2016-03-03T02:55:40Z | MEMBER | null | Recent changes to pyflakes (see <a href="https://github.com/pyflakes/pyflakes/blob/0532189b34086740d7fe553bd87c0a6cf682a93b/pyflakes/checker.py#L544-L546">here</a>) suddenly started raising errors in `datetools.py` due to the star imports at the top, which the `#noqa` was not preventing. The correct syntax is `# flake8: noqa` <br><br><b>PLEASE MERGE ASAP</b>
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| null | 6 | 2016-03-03T08:33:20Z | 2016-03-03T23:05:49Z | 2016-03-03T12:23:04Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```
In [2]: pd.read_csv('/tmp/csv.csv', header=None)
Out[2]:
0 1 2
0 0 2 2
1 1 2 2
2 2 2 2
In [3]: mi = pd.MultiIndex.from_tuples([(1,2), (3,4)], names=['l1', 'l2'])
In [4]: pd.read_csv('/tmp/csv.csv', header=None, names=mi)
Out[4]:
1 3
2 4
0 2 2
1 2 2
2 2 2
```
#### Expected Output
```
In [4]: pd.read_csv('/tmp/csv.csv', header=None, names=mi)
Out[4]:
l1 1 3
l2 2 4
0 2 2
1 2 2
2 2 2
```
Might be worth fixing together with https://github.com/pydata/pandas/issues/7589 (just a rough guess, didn't look at the code)
#### output of `pd.show_versions()`
```
In [8]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: eba78032e927d2680850c88190f3bafe18cac6ba
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.3.0-1-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
pandas: 0.18.0rc1+66.geba7803
nose: 1.3.6
pip: 1.5.6
setuptools: 20.1.1
Cython: 0.23.2
numpy: 1.10.0.post2
scipy: 0.16.0
statsmodels: 0.8.0.dev0+755fa81
xarray: None
IPython: 4.1.1
sphinx: 1.3.1
patsy: 0.3.0-dev
dateutil: 2.4.2
pytz: 2015.6
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.3
matplotlib: 1.5.dev1
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: 0.7.3
lxml: None
bs4: 4.4.0
html5lib: 0.999
httplib2: 0.9.1
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
jinja2: 2.8
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12519 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12519/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12519/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12519/events | https://github.com/pandas-dev/pandas/issues/12519 | 138,174,318 | MDU6SXNzdWUxMzgxNzQzMTg= | 12,519 | Read CSV error_bad_lines does not error for too many values in first data row | {
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} | 4 | 2016-03-03T13:13:37Z | 2019-10-21T19:52:15Z | null | NONE | null | Hi, I would like to report an unexpected behaviour connected with option error_bad_lines (I just reference this for make it easier to find this bug if someone was to report the same).
Given the following two CSV files:
simple.csv
```
Col1,Col2
4,Here
5,7,Invalid
8,Row
9,Does
10,Break
```
simple2.csv
```
Col1,Col2
5,7,Invalid
4,Row
8,Does
9,Not
10,Break
```
The following code breaks, as expected:
```
df = pd.read_csv('simple.csv')
```
The fact that the following code does not break is up to discussion (though it is inconsistent depending on whether an error is in first row or futher ones):
```
df2 = pd.read_csv('simple2.csv')
```
As the index is read as the first column and hence it comes down to another potential issue reported (ragarding erroring on too few values), so I am not going into this here.
So the result is as follows:
```
Col1 Col2
5 7 Invalid
4 Row NaN
8 Does NaN
9 Not NaN
10 Break NaN
```
However even if I specify explicitly, that there is no index in CSV:
```
df3 = pd.read_csv('simple2.csv', index_col=False)
```
It still works, yielding the result:
```
Col1 Col2
0 5 7
1 4 Row
2 8 Does
3 9 Not
4 10 Break
```
And this is definitely a bug I believe. I discovered it by accident, as in CSV that I was about to read comma was used also as decimal separator in first column and the totally corrupted CSV ended up read and parsed as DataFrame.
#### output of `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 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.1
nose: 1.3.7
pip: 8.0.3
setuptools: 20.1.1
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.0
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
Jinja2: 2.8
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} | 8 | 2016-03-03T18:39:47Z | 2021-08-09T02:11:55Z | null | NONE | null | There are known ways of droping inf and nan (.replace(np.inf, np.nan).dropna()) but still, the API could be expanded:
```
.dropinf(inplace=False)
.dropna(drop_inf = False)
```
Are both self explanatory wishes and it is no stretch to see how they are usefull.
Note for instance that today's API does not provide a one liner to drop inf in place while keeping nans.
Best
PS: not sure how to submit wishes.
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} | 3 | 2016-03-03T18:52:39Z | 2016-06-23T12:47:22Z | 2016-06-23T12:47:22Z | CONTRIBUTOR | null | Did this for statsmodels the other week, works pretty well. Something like
``` bash
jupyter nbconvert --ExecutePreprocessor.timeout=600 --to html --execute style.ipynb
```
https://nbconvert.readthedocs.org/en/latest/execute_api.html
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} | 11 | 2016-03-03T23:46:57Z | 2016-04-17T16:03:39Z | 2016-04-17T16:03:30Z | CONTRIBUTOR | null | Just ran into this gotcha and almost filed it as a bug report. Added a few examples to fill in the section header.
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| null | 5 | 2016-03-04T14:04:13Z | 2016-03-05T02:12:19Z | 2016-03-05T02:12:19Z | CONTRIBUTOR | null | Master started failing on https://github.com/pydata/pandas/pull/12260 [travis](https://travis-ci.org/pydata/pandas/builds/113480877). Not entirely sure why yet, but was able to reproduce one of the failures locally, and fix it with the revert.
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https://api.github.com/repos/pandas-dev/pandas/issues/12525 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12525/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12525/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12525/events | https://github.com/pandas-dev/pandas/pull/12525 | 138,501,007 | MDExOlB1bGxSZXF1ZXN0NjE3MjU5NzQ= | 12,525 | TST: Rework style test skip | {
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} | 2 | 2016-03-04T15:22:30Z | 2016-03-05T15:13:14Z | 2016-03-05T15:13:14Z | CONTRIBUTOR | null | Also redoes some of the changes that were inadvertently reverted
in https://github.com/pydata/pandas/pull/12260.
That PR had two commits, one of which was an older version of
what was eventually merged in
https://github.com/pydata/pandas/pull/12162.
The stale commit incorrectly merged was a15248ae7.
It should have been a3c38fe82.
For the most part the changes were just style, but there was a python2
import error, which our tests didn't fail on because I was trying to
catch a jinja ImportError, and instead caught all ImportErrors.
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| null | 2 | 2016-03-04T15:50:49Z | 2016-05-07T17:48:56Z | 2016-05-07T17:48:56Z | NONE | null | Addresses issue in #12519 by raising exception when 'filepath_or_buffer'
in 'read_csv' contains different number of fields in input lines.
However, according to tests the behaviour reported in bug is expected (lines 376-390): https://github.com/pydata/pandas/blob/master/pandas/io/tests/test_cparser.py#L376
So should those tests be removed?
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} | 2 | 2016-03-04T17:33:44Z | 2016-04-20T00:04:17Z | 2016-04-20T00:04:17Z | NONE | null | #### Updated
I have a MultiIndex'd DataFrame that returns a KeyError for one integer and an IndexError for a different integer, neither integer is in the first level of the index. This only occurs when attempting to access a scalar value, a slice always give KeyError. The behavior does not occur on a cut down version of the (>200M when pickled) data frame, or I would attach a working example. Can send the file if needed though.
```
>>> isinstance(n, int)
True
>>> df.loc[(n, 0), 'dest']
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.5/site-packages/pandas/core/indexing.py", line 1196, in __getitem__
return self._getitem_tuple(key)
File "/usr/local/lib/python3.5/site-packages/pandas/core/indexing.py", line 709, in _getitem_tuple
return self._getitem_lowerdim(tup)
File "/usr/local/lib/python3.5/site-packages/pandas/core/indexing.py", line 817, in _getitem_lowerdim
return self._getitem_nested_tuple(tup)
File "/usr/local/lib/python3.5/site-packages/pandas/core/indexing.py", line 889, in _getitem_nested_tuple
obj = getattr(obj, self.name)._getitem_axis(key, axis=axis)
File "/usr/local/lib/python3.5/site-packages/pandas/core/indexing.py", line 1343, in _getitem_axis
return self._get_label(key, axis=axis)
File "/usr/local/lib/python3.5/site-packages/pandas/core/indexing.py", line 86, in _get_label
return self.obj._xs(label, axis=axis)
File "/usr/local/lib/python3.5/site-packages/pandas/core/generic.py", line 1483, in xs
drop_level=drop_level)
File "/usr/local/lib/python3.5/site-packages/pandas/core/index.py", line 5432, in get_loc_level
return (self._engine.get_loc(_values_from_object(key)),
File "pandas/index.pyx", line 137, in pandas.index.IndexEngine.get_loc (pandas/index.c:3979)
File "pandas/index.pyx", line 146, in pandas.index.IndexEngine.get_loc (pandas/index.c:3693)
File "pandas/src/util.pxd", line 41, in util.get_value_at (pandas/index.c:13199)
IndexError: index out of bounds
```
#### Expected Output
```
>>> df.loc[(m, 0), 'dest']
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.5/site-packages/pandas/core/indexing.py", line 1196, in __getitem__
return self._getitem_tuple(key)
File "/usr/local/lib/python3.5/site-packages/pandas/core/indexing.py", line 709, in _getitem_tuple
return self._getitem_lowerdim(tup)
File "/usr/local/lib/python3.5/site-packages/pandas/core/indexing.py", line 817, in _getitem_lowerdim
return self._getitem_nested_tuple(tup)
File "/usr/local/lib/python3.5/site-packages/pandas/core/indexing.py", line 889, in _getitem_nested_tuple
obj = getattr(obj, self.name)._getitem_axis(key, axis=axis)
File "/usr/local/lib/python3.5/site-packages/pandas/core/indexing.py", line 1343, in _getitem_axis
return self._get_label(key, axis=axis)
File "/usr/local/lib/python3.5/site-packages/pandas/core/indexing.py", line 86, in _get_label
return self.obj._xs(label, axis=axis)
File "/usr/local/lib/python3.5/site-packages/pandas/core/generic.py", line 1483, in xs
drop_level=drop_level)
File "/usr/local/lib/python3.5/site-packages/pandas/core/index.py", line 5432, in get_loc_level
return (self._engine.get_loc(_values_from_object(key)),
File "pandas/index.pyx", line 137, in pandas.index.IndexEngine.get_loc (pandas/index.c:3979)
File "pandas/index.pyx", line 147, in pandas.index.IndexEngine.get_loc (pandas/index.c:3719)
KeyError: (300067502, 0)
```
The expected behavior occurs for nearly all integers I try that are not in the first level of the index. How could special integers give an IndexError?
#### output of `pd.show_versions()`
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.0
nose: 1.3.7
pip: 8.0.2
setuptools: 19.4
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.16.1
statsmodels: None
IPython: 4.0.0
sphinx: None
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: 2.4.6
matplotlib: 1.5.0
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
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} | 1 | 2016-03-04T19:20:42Z | 2016-03-04T21:37:23Z | 2016-03-04T21:34:33Z | NONE | null | Suppose we wish to concatenate two dataframes with the same name. Because the columns share the same name, loc will not distinguish them. So we use iloc to manipulate them individually.
``` python
import pandas as pd,numpy as np
df0=pd.DataFrame(np.arange(10))
df1=pd.DataFrame(np.arange(10,20))
#1) Wrong
df2=pd.concat([df0,df1],axis=1)
df2.iloc[:,0]/=2.
```
This gives the surprising output that the result on the first column is applied to both.
``` python
0 0
0 0.0 0.0
1 0.5 0.5
2 1.0 1.0
3 1.5 1.5
4 2.0 2.0
5 2.5 2.5
6 3.0 3.0
7 3.5 3.5
8 4.0 4.0
9 4.5 4.5
```
The expected output is this.
``` python
0 0
0 0.0 10
1 0.5 11
2 1.0 12
3 1.5 13
4 2.0 14
5 2.5 15
6 3.0 16
7 3.5 17
8 4.0 18
9 4.5 19
```
This could be achieved with this code (although that requires renaming the columns).
``` python
df2=pd.concat([df0,df1],axis=1)
df2.columns=range(df2.shape[1])
df2.iloc[:,0]/=2.
```
Why do we get a different result when we use iloc? I understand the renaming the column would matter if we were using loc. Please help me understand why iloc cares about column names.
I'm using pandas: 0.17.1, numpy: 1.9.2.
Thank you.
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} | 8 | 2016-03-05T00:06:02Z | 2016-11-18T22:43:29Z | 2016-11-18T22:42:37Z | NONE | null | Using pandas-0.18.0rc1-cp27-cp27m-win_amd64.whl from Christoh Gohlkes site:
```
import pandas as pd
df = pd.DataFrame({u'a':[1,2], u'degC_°C':[3,4]})
df.to_clipboard()
df.to_clipboard(excel=True, sep='\t')
df.to_clipboard(excel=False, sep='\t')
df.to_clipboard(sep=',')
```
Always gives the same content in the clipboard:
```
a degC_°C
0 1 3
1 2 4
```
which cannot be pasted to Excel since it does not use tab as separator.
Copy/paste worked fine in pandas 0.17.1 except for unicode characters.
Output of `pd.show_versions()`:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: AMD64 Family 22 Model 0 Stepping 1, AuthenticAMD
byteorder: little
LC_ALL: None
LANG: de_DE
pandas: 0.18.0rc1
nose: None
pip: 8.0.2
setuptools: 19.6.1
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.1.0rc1
sphinx: 1.3.5
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
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
```
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} | 5 | 2016-03-05T04:24:30Z | 2016-03-21T12:27:56Z | 2016-03-21T12:27:56Z | CONTRIBUTOR | null | - [x] closes #10331
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Includes implementation, unit tests, documentation fixes and whatsnew.
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} | 3 | 2016-03-05T14:29:49Z | 2016-03-15T13:39:29Z | 2016-03-15T13:39:17Z | MEMBER | null | - [x] related to #11558
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Made `CategoricalIndex.get_loc` to return the same result as normal `Index`.
```
data2 = pd.DataFrame([[1,2,3],[3,4,5]], index=pd.Categorical(['one', 'two']))
# in current master, it returns DataFrame with 1 row
data2.ix['one']
# 0 1 2
# one 1 2 3
# after this PR, it returns Series
data2.ix['one']
#0 1
#1 2
#2 3
# Name: one, dtype: int64
```
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} | 8 | 2016-03-05T15:07:41Z | 2016-04-18T17:17:48Z | 2016-04-18T17:17:44Z | MEMBER | null | - [x] closes #12473
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Needs to decide what categorical dtype `map` should return. Even though `apply` returns `object` dtype, but returning `category` is more consistent?
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} | 1 | 2016-03-05T15:45:26Z | 2016-04-29T17:05:48Z | 2016-04-29T17:05:48Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
Allow `.where` and `.mask` to accept `callable` as cond. This is useful if `DataFrame` is changed during method chaining.
```
df = pd.DataFrame(np.random.randn(2, 2))
df.where(lambda x: x > 0)
# currently raises ValueError
```
#### Expected Output
Should be the same as:
```
df.where(df > 0)
# 0 1
#0 0.689202 NaN
#1 NaN 0.261644
```
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} | 4 | 2016-03-05T22:15:34Z | 2018-01-05T00:45:31Z | 2018-01-05T00:45:31Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd
dates = pd.Series(pd.date_range('1700-01-01', periods=4500, freq='m'))
days_delta = (dates-dates.min()).astype('timedelta64[D]')
```
#### Expected Output
Should return only positive timedeltas, however for deltas greater than 292 years the values are negative.
#### output of `pd.show_versions()`
commit: None
python: 3.4.4.final.0
python-bits: 64
OS: Windows
OS-release: 8.1
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.1
nose: None
pip: 8.0.3
setuptools: 20.1.1
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
IPython: 4.0.3
sphinx: 1.3.5
patsy: 0.4.1
dateutil: 2.5.0
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.5.0
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: 0.6.2.None
psycopg2: None
Jinja2: 2.8
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} | 1 | 2016-03-05T23:55:16Z | 2019-07-15T20:20:20Z | 2019-07-15T20:20:20Z | CONTRIBUTOR | null | - need to include nans in `Datetimelike`. they work, but need to adjust tests.
xref #12376
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} | 1 | 2016-03-06T00:03:45Z | 2019-07-01T16:41:06Z | 2019-07-01T16:41:06Z | CONTRIBUTOR | null | xref example in : #13327
```
In [4]: df = DataFrame({'A' : pd.date_range('20130101',periods=3),'B' : range(3)})
In [5]: df
Out[5]:
A B
0 2013-01-01 0
1 2013-01-02 1
2 2013-01-03 2
```
This is a supported op
```
In [7]: df.rolling(window=2).count()
Out[7]:
A B
0 1.0 1.0
1 2.0 2.0
2 2.0 2.0
```
This works
```
In [9]: df.rolling(window=2).B.sum()
Out[9]:
0 NaN
1 1.0
2 3.0
Name: B, dtype: float64
```
```
df.rolling(window=2).sum()
```
should work (but excluded `A`) from the results.
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} | 1 | 2016-03-06T00:11:36Z | 2016-03-07T18:42:44Z | 2016-03-07T18:42:44Z | CONTRIBUTOR | null | ```
In [1]: df = DataFrame({'A' : pd.date_range('20130101',periods=3),'B' : range(3)})
In [2]: df.rolling(window=2).count()
Out[2]:
A B
0 1.0 1.0
1 2.0 2.0
2 2.0 2.0
In [3]: df.rolling(window=2).count().dtypes
Out[3]:
A float64
B float64
dtype: object
```
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} | 30 | 2016-03-06T02:02:31Z | 2016-04-29T17:06:14Z | 2016-04-29T17:05:49Z | MEMBER | null | - [x] closes #12533
- [x] closes #11485
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Current impl is very simple like `assign`. Another idea is applying some restriction to return value from `callable` (for example, only allow aligned `DataFrame` or scalar).
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} | 1 | 2016-03-06T02:13:43Z | 2016-03-15T14:22:40Z | 2016-03-15T14:22:14Z | MEMBER | null | - [x] closes #11854
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Also, added `NS` to normal alias not to show warnings (as the same as `MS` and `NS`)
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} | 10 | 2016-03-06T15:56:09Z | 2017-01-24T18:02:16Z | 2017-01-24T18:02:16Z | CONTRIBUTOR | null | Right now `np.Inf` and non-numeric types are not counted.
#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
import numpy as np
from pandas import DataFrame
df = DataFrame({'A': pd.date_range('20130101',periods=4), 'B': [0, 1, 2, np.Inf], 'C':['a', 'b', 'c', 'd']})
roll = df.rolling(window=2)
res = roll.count()
print(res)
## partial output A, B don't error
A B
0 1.0 1.0
1 2.0 2.0
2 2.0 2.0
3 2.0 1.0
## column C errors
```
#### Expected Output
```
A B C
0 1 1 1
1 2 2 2
2 2 2 2
3 2 2 2
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: a0aaad98e8c567a3a04aef165cdb159fbd98d13f
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-76-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0rc1+77.ga0aaad9.dirty
nose: 1.3.7
pip: 8.0.1
setuptools: 19.4
Cython: 0.23.4
numpy: 1.10.2
scipy: None
statsmodels: None
xarray: None
IPython: 4.0.0
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.10
pymysql: None
psycopg2: None
jinja2: None
boto: None
```
I think it can be patched, together with #12538, by
```
--- a/pandas/core/window.py
+++ b/pandas/core/window.py
@@ -509,19 +509,9 @@ class _Rolling_and_Expanding(_Rolling):
blocks, obj = self._create_blocks(how=None)
results = []
for b in blocks:
-
- if com.needs_i8_conversion(b.values):
- result = b.notnull().astype(int)
- else:
- try:
- result = np.isfinite(b).astype(float)
- except TypeError:
- result = np.isfinite(b.astype(float)).astype(float)
-
- result[pd.isnull(result)] = 0
-
- result = self._constructor(result, window=window, min_perio
- center=self.center).sum()
+ result = self._constructor(
+ b.notnull(), window=window, min_periods=0, center=self.
+ ).sum().astype(int)
```
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| null | 1 | 2016-03-06T16:16:46Z | 2016-03-06T21:32:23Z | 2016-03-06T21:32:23Z | NONE | null | i want use qcut() to process my data?but i don't want to direct to binning by width.
so i want use a supervised binning.such as Entropy-bor based Binning.or can you tell another package for this?
thank you?
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} | 6 | 2016-03-06T17:40:02Z | 2016-03-06T22:45:52Z | 2016-03-06T22:42:23Z | CONTRIBUTOR | null | Travis-built docs do not appear to have math rendered correctly. See https://pandas-docs.github.io/pandas-docs-travis/computation.html#exponentially-weighted-windows for one example.
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} | 3 | 2016-03-06T20:37:22Z | 2016-03-08T18:34:29Z | 2016-03-06T22:42:23Z | CONTRIBUTOR | null | - Closes #12543
- `texlive-latex-base` needed for `latex`
- `texlive-latex-extra` needed for `utf8x.def`
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} | 3 | 2016-03-06T22:08:30Z | 2016-08-21T14:17:47Z | 2016-08-21T14:17:47Z | NONE | null | #### Code Sample, a copy-pastable example if possible
Following example returns error if run with `engine = "python"`:
```
ite = pd.read_csv(infile, sep = '\t', index_col = False,
# dtype=pd.np.float32, na_filter=False,low_memory=False,
usecols = pd.np.arange(nindexcolumns, ncol),
iterator = True, chunksize = chunksize , engine = "python" )
print(list(ite)[0])
```
#### Error
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-6-e39439e21b50> in <module>()
17 iterator = True, chunksize = chunksize , engine = "python" )
18
---> 19 print(list(ite)[0])
/home/.usr/py3/lib/pandas-0.18.0rc1+80.g820e110.dirty-py3.3-linux-x86_64.egg/pandas/io/parsers.py in __next__(self)
739
740 def __next__(self):
--> 741 return self.get_chunk()
742
743 def _make_engine(self, engine='c'):
/home/.usr/py3/lib/pandas-0.18.0rc1+80.g820e110.dirty-py3.3-linux-x86_64.egg/pandas/io/parsers.py in get_chunk(self, size)
780 if size is None:
781 size = self.chunksize
--> 782 return self.read(nrows=size)
783
784
/home/.usr/py3/lib/pandas-0.18.0rc1+80.g820e110.dirty-py3.3-linux-x86_64.egg/pandas/io/parsers.py in read(self, nrows)
759 raise ValueError('skip_footer not supported for iteration')
760
--> 761 ret = self._engine.read(nrows)
762
763 if self.options.get('as_recarray'):
/home/.usr/py3/lib/pandas-0.18.0rc1+80.g820e110.dirty-py3.3-linux-x86_64.egg/pandas/io/parsers.py in read(self, rows)
1617 content = content[1:]
1618
-> 1619 alldata = self._rows_to_cols(content)
1620 data = self._exclude_implicit_index(alldata)
1621
/home/.usr/py3/lib/pandas-0.18.0rc1+80.g820e110.dirty-py3.3-linux-x86_64.egg/pandas/io/parsers.py in _rows_to_cols(self, content)
1997 raise ValueError(msg)
1998
-> 1999 if self.usecols:
2000 if self._implicit_index:
2001 zipped_content = [
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.3.2.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-573.12.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0rc1+80.g820e110.dirty
nose: 1.3.0
pip: 8.0.2
setuptools: 0.9.8
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.12.1
statsmodels: None
xarray: None
IPython: 4.1.0-dev
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.7.dev0
matplotlib: 1.5.1
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
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| null | 17 | 2016-03-06T22:20:43Z | 2017-05-08T09:42:56Z | 2017-05-06T23:19:52Z | MEMBER | null | - [x] closes #12354
- [ ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
- [ ] plotting no try to use `cycler` regardless of mpl version (previously tried if mpl >= 1.5)
- [ ] bug in area plot kwds handling
## Cyclic option handling
Allows to accept some option (currently only `hatch`) as a list, each element is applied to corresponding columns.
This can be more generalized, but I think it's better to refactor plotting submodule beforehand.
## Hatch example
```
df = pd.DataFrame({'A': np.random.randint(0, 10, 10),
'B': np.random.randint(0, 10, 10)})
# we cannot see hatch if alpha=1.0
df.plot.area(hatch='*', alpha=0.5);
```
```
from cycler import cycler
df.plot.area(hatch=cycler('hatch', ['*', '//']), alpha=0.5);
```
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} | 2 | 2016-03-06T22:38:14Z | 2017-04-15T16:02:38Z | 2017-04-15T16:02:25Z | MEMBER | null | - Make `plotting` as subpackage
- Split utility functions / non- `MPLPlot` classes to other files.
- Move tests under `plotting/*`
- Add small unit tests to guarantee utility function APIs.
- Refactor `MPLPlot` class properties. Because `MPLPlot` now has lots of properties, split them to `FigureProperties` and `AxesProperties` classes and delegates the logic.
This should makes #12547, #7670, #8018 easier.
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} | 0 | 2016-03-06T23:45:36Z | 2020-11-01T22:12:55Z | 2016-03-07T15:11:06Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- Added some `Period` related tests in preparation to add period dtype.
- Moved some tests `test_timeseries` to correct test class.
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} | 5 | 2016-03-07T03:04:32Z | 2020-02-27T21:42:39Z | 2020-02-27T21:42:39Z | NONE | null | I think there should be a way to take a DataFrame and use the values in its columns as indices into a MultiIndexed Series. Here is an example:
```
>>> d = pandas.DataFrame({"Let": ["A", "B", "C"], "Num": [1, 2, 3]})
>>> d
Let Num
0 A 1
1 B 2
2 C 3
>>> ser = pandas.Series(
... ['a', 'b', 'c', 'd', 'e', 'f'],
... index=pandas.MultiIndex.from_arrays([["A", "B", "C"]*2, [1, 2, 3, 4, 5, 6]])
... )
>>> ser
A 1 a
B 2 b
C 3 c
A 4 d
B 5 e
C 6 f
dtype: object
```
With this data, you should be able to do `d.map(ser)` (or whatever method name instead of `map`) and get the same result as this:
```
>>> ser.ix[d.apply(tuple, axis=1)]
A 1 a
B 2 b
C 3 c
dtype: object
```
You currently cannot do this without converting the rows to tuples (`ser[d]` gives `ValueError: Cannot index with multidimensional key`). Converting to tuple is an awkward way to do a very natural task, which is using a tabular array of data to look up values in a tabular index (i.e., a MultiIndex).
I'm creating an issue to resurrect this request from much earlier discussion [here](https://groups.google.com/forum/#!searchin/pydata/dataframe$20map/pydata/sibkQ5Ea-Hc/RKMwiNsH7TMJ) and [here](http://stackoverflow.com/questions/22293683/equivalent-of-series-map-for-dataframe).
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} | 10 | 2016-03-07T10:28:42Z | 2016-03-20T15:10:13Z | 2016-03-20T15:10:03Z | MEMBER | null | Closes #12203 by overriding the row alignment checks for both engines when the `usecols` parameter is passed into `read_csv`.
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| null | 3 | 2016-03-07T12:16:03Z | 2021-04-23T01:15:15Z | null | NONE | null | I want to interpolate (upscale) nonequispaced time-series to obtain equispaced time-series.
Currently I am doing it in following way:
1. take original timeseries.
2. create new timeseries with NaN values at each 30 seconds intervals ( using resample('30S').asfreq() )
3. concat original timeseries and new timeseries
4. sort the timeseries to restore order of times (This I do not like - sorting has complexity of O = n log(n) )
5. interpolate
6. remove original points from the timeseries
is there a more simple way? like in matlab you have original timeseries and you pass new times as a parameter to the interpolate() function to receive values at desired times. Ideally I would like to have a function such as
`origTimeSeries.interpolate(newIndex=newTimeIndex, method='spline')`
I remark that times of original timeseries might not be be a subset of the times of desired timeseries.
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
values = [271238, 329285, 50, 260260, 263711]
timestamps = pd.to_datetime(['2015-01-04 08:29:4',
'2015-01-04 08:37:05',
'2015-01-04 08:41:07',
'2015-01-04 08:43:05',
'2015-01-04 08:49:05'])
ts = pd.Series(values, index=timestamps)
ts
ts[ts==-1] = np.nan
newFreq=ts.resample('60S').asfreq()
new=pd.concat([ts,newFreq]).sort_index()
new=new.interpolate(method='time')
ts.plot(marker='o')
new.plot(marker='+',markersize=15)
new[newFreq.index].plot(marker='.')
lines, labels = plt.gca().get_legend_handles_labels()
labels = ['original values (nonequispaced)', 'original + interpolated at new frequency (nonequispaced)', 'interpolated values without original values (equispaced!)']
plt.legend(lines, labels, loc='best')
plt.show()
```
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} | 4 | 2016-03-07T14:58:17Z | 2016-12-04T17:42:07Z | 2016-12-04T17:42:07Z | NONE | null | #### Code Sample, a copy-pastable example if possible
``` python
In [18]: b = pandas.Series()
In [19]: g = b.groupby(level = 0)
In [20]: g.nunique()
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-20-fbbfc3108eac> in <module>()
----> 1 g.nunique()
/usr/local/lib/python2.7/dist-packages/pandas/core/groupby.pyc in nunique(self, dropna)
2693
2694 out = np.add.reduceat(inc, idx).astype('int64', copy=False)
-> 2695 return Series(out if ids[0] != -1 else out[1:],
2696 index=self.grouper.result_index,
2697 name=self.name)
IndexError: index 0 is out of bounds for axis 0 with size 0
```
#### Expected Output
``` python
0
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-79-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.1
pip: 8.0.2
setuptools: 20.1.1
Cython: None
numpy: 1.10.4
scipy: 0.13.3
statsmodels: 0.5.0
IPython: 4.1.1
sphinx: None
patsy: 0.2.1
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.2.2
matplotlib: 1.3.1
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: None
Jinja2: None
```
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} | 34 | 2016-03-07T17:18:17Z | 2016-04-27T14:28:37Z | 2016-04-27T14:28:37Z | NONE | null | #### Code Sample, a copy-pastable example if possible
import pandas
df = pandas.DataFrame({'a': [complex(3, -1)]})
df.to_json()
#### Expected Output
'{"a":{"0":3-1j}}'
#### Actual output:
```
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-3-b53888fd1707> in <module>()
----> 1 df.to_json()
.../lib/python2.7/site-packages/pandas/core/generic.pyc in to_json(self, path_or_buf, orient, date_format, double_precision, force_ascii, date_unit, default_handler)
892 force_ascii=force_ascii,
893 date_unit=date_unit,
--> 894 default_handler=default_handler)
895
896 def to_hdf(self, path_or_buf, key, **kwargs):
.../lib/python2.7/site-packages/pandas/io/json.pyc in to_json(path_or_buf, obj, orient, date_format, double_precision, force_ascii, date_unit, default_handler)
33 obj, orient=orient, date_format=date_format,
34 double_precision=double_precision, ensure_ascii=force_ascii,
---> 35 date_unit=date_unit, default_handler=default_handler).write()
36 else:
37 raise NotImplementedError("'obj' should be a Series or a DataFrame")
.../lib/python2.7/site-packages/pandas/io/json.pyc in write(self)
76 date_unit=self.date_unit,
77 iso_dates=self.date_format == 'iso',
---> 78 default_handler=self.default_handler)
79
80
RuntimeError: Unhandled numpy dtype 15
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 8.0.3
setuptools: 19.7
Cython: 0.23.4
numpy: 1.8.2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 4.0.1
sphinx: 1.3.3
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
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
```
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} | 1 | 2016-03-07T17:25:53Z | 2016-03-07T17:30:36Z | 2016-03-07T17:30:36Z | CONTRIBUTOR | null | - Added pandas logo and separator at the top of page
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| null | 1 | 2016-03-07T18:26:47Z | 2016-03-07T18:38:07Z | 2016-03-07T18:38:03Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import numpy as np
import pandas as pd
num_tries = 1000
length = 100000
for i in range(num_tries):
df = pd.DataFrame({"A" : 100000 * (200 + np.random.random(length)),
"B" : 100000 * (200 + np.random.random(length))})
df['A*B'] = df.A * df.B
if (df['A*B'] < 100).any():
print "found the bug in iteration", i
```
#### Expected Output
Empty
#### Actual Output Example:
```
found the bug in iteration 7
found the bug in iteration 36
found the bug in iteration 103
found the bug in iteration 113
found the bug in iteration 120
....
```
#### Commentary
The output is different on each run.
The problem does not occur if we set `length = 10000`. Thus, the problem only occurs for long vectors.
The problem also does not occur if we cast the pandas column to numpy arrays. Thus, if we replace the multiplication line by
`df['A*B'] = np.array(df.A) * np.array(df.B)` then the problem does not occur
I suspect this is a CPU-related problem, and that some users will not be able to reproduce it. I'm running on a 64-bit Intel i7-3610QM. It is entirely possible that my CPU is malfunctioning, but it's still odd that the problem is so prevalent, and that it gets solved when using numpy multiplication instead of pandas multiplication.
#### more info
here is an example of the head of the data-frame from a buggy iteration:
```
A B A*B
0 20061284.685632 20098766.753960 6.698204e-316
1 20049742.754308 20038548.079325 8.741955e-316
2 20002832.802216 20045228.660298 2.000283e+07
3 20009535.502458 20098483.298851 2.000954e+07
4 20091479.138939 20031304.664403 2.009148e+07
5 20053896.596382 20014109.767089 2.005390e+07
6 20019252.981156 20074785.383487 2.001925e+07
7 20073563.758979 20007609.514515 2.007356e+07
8 20092312.956176 20007082.949870 2.009231e+07
9 20070058.044947 20049647.829544 2.007006e+07
10 20010300.783713 20011630.430404 2.001030e+07
```
#### output of `pd.show_versions()`
```
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 8.1
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.1
nose: 1.3.7
pip: 8.0.3
setuptools: 20.1.1
Cython: 0.23.2
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
IPython: 4.0.1
sphinx: 1.2.3
patsy: 0.4.0
dateutil: 2.4.1
pytz: 2015.6
blosc: None
bottleneck: None
tables: 3.2.0
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 2.0.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.8
pymysql: None
psycopg2: None
Jinja2: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12557 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12557/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12557/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12557/events | https://github.com/pandas-dev/pandas/pull/12557 | 139,066,380 | MDExOlB1bGxSZXF1ZXN0NjE5NjUxNjI= | 12,557 | BUG: Fix for issue #12553 | {
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| null | 2 | 2016-03-07T19:16:34Z | 2016-03-08T13:14:29Z | 2016-03-08T08:43:56Z | NONE | null | closes #12553
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} | 1 | 2016-03-08T03:42:59Z | 2016-03-12T17:46:32Z | 2016-03-12T17:46:32Z | CONTRIBUTOR | null | By default, `value_counts` ignores missing values. To get them displayed, one must add the option `dropna=False`. But when one normalizes, they are entering into the denominator even when `dropna=True`.
#### Code Sample, a copy-pastable example if possible
```
s = pd.Series([1,2,3,np.nan, np.nan, np.nan])
s.value_counts(normalize=True)
0.1667
0.1667
0.1667
```
#### Expected Output
```
0.333
0.333
0.333
```
#### output of `pd.show_versions()`
pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 3.4.4.final.0
python-bits: 64
OS: Darwin
OS-release: 15.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 8.1.0
setuptools: 20.2.2
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.16.1
statsmodels: None
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
Jinja2: 2.8
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} | 9 | 2016-03-08T07:36:45Z | 2016-05-30T14:39:39Z | 2016-05-30T14:39:34Z | CONTRIBUTOR | null | - [x] closes #12388
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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| null | 12 | 2016-03-08T08:48:49Z | 2016-12-16T23:57:36Z | 2016-12-16T23:57:36Z | NONE | null | Closes #12553
xref #13239
Made a new pull request to clean up from #12557
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} | 1 | 2016-03-08T17:43:06Z | 2016-03-08T18:19:34Z | 2016-03-08T18:19:34Z | CONTRIBUTOR | null | since the GIT_\* env variables are not available
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} | 1 | 2016-03-08T20:01:50Z | 2019-10-21T19:34:17Z | null | CONTRIBUTOR | null | right now we take a different path for encoded input, and the encoded input is basically a python writer, while non-encoded input writes per-block and is done via cython.
Ideally could fold the encoded writer into the current writer.
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} | 9 | 2016-03-08T20:23:58Z | 2016-04-25T15:07:16Z | 2016-04-25T14:53:27Z | CONTRIBUTOR | null | - Enable platform-dependent config in asv (needs asv git version for it to do something)
- Enable wheel cache in asv (in asv git version)
- Fix a few easily fixed broken benchmarks
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} | 7 | 2016-03-08T22:29:11Z | 2016-04-03T14:06:00Z | 2016-04-03T14:06:00Z | CONTRIBUTOR | null | Apparently there's an issue when comparing the equality of a scalar value against a categorical column as part of a DataFrame. In the example below, I'm checking against `-np.inf`, but comparing to a string or integer gives the same results.
This raises `ValueError: Wrong number of dimensions`.
#### Code Sample
``` python
from sys import version
import pandas as pd # Version 0.17.1 on Linux and Windows
import numpy as np
print(version)
print(pd.__version__)
# Arbitrary data set
columns = ['Name', 'Type', 'Age', 'Weight (kg)', 'Cuteness']
dataset = [['Snuggles', 'Cat', 5.2, 4.2, 9.7],
['Rex', 'Dog', 2.1, 12, 2.1],
['Mrs. Quiggleworth', 'Cat', 7.4, 3, 7],
['Squirmy', 'Snake', 1.1, 0.2, 0.1],
['Tarantula', 'Legs', 0.2, 0.01, -np.inf],
['Groucho', 'Dog', 6.9, 8, 5.1]]
df = pd.DataFrame(dataset, columns=columns).set_index(['Name'])
# Works fine
print("String type - are any of the columns negative infinity?")
neg_inf = (df == -np.inf)
print(neg_inf.any(axis=1))
# Convert 'type' to a categorical
df['Type'] = df['Type'].astype('category')
print("Categorical type - is the Type column negative infinity?")
print(df['Type'] == -np.inf) # Works fine
print("Categorical type in dataframe - are any of them negative infinity?")
print(df[['Type']] == -np.inf) # Danger, Will Robinson!
```
#### Expected Output
```
3.5.1 |Anaconda 2.5.0 (64-bit)| (default, Dec 7 2015, 11:16:01)
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]
0.17.1
String type - are any of the columns negative infinity?
Name
Snuggles False
Rex False
Mrs. Quiggleworth False
Squirmy False
Tarantula True
Groucho False
dtype: bool
Categorical type - is the Type column negative infinity?
Name
Snuggles False
Rex False
Mrs. Quiggleworth False
Squirmy False
Tarantula False
Groucho False
Name: Type, dtype: bool
Categorical type in dataframe - are any of them negative infinity?
Traceback (most recent call last):
File "pandas_demo.py", line 30, in <module>
print(df[['Type']] == -np.inf) # Danger, Will Robinson!
File "~/anaconda3/lib/python3.5/site-packages/pandas/core/ops.py", line 1115, in f
res = self._combine_const(other, func, raise_on_error=False)
File "~/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py", line 3482, in _combine_const
new_data = self._data.eval(func=func, other=other, raise_on_error=raise_on_error)
File "~/anaconda3/lib/python3.5/site-packages/pandas/core/internals.py", line 2840, in eval
return self.apply('eval', **kwargs)
File "~/anaconda3/lib/python3.5/site-packages/pandas/core/internals.py", line 2823, in apply
applied = getattr(b, f)(**kwargs)
File "~/anaconda3/lib/python3.5/site-packages/pandas/core/internals.py", line 1155, in eval
fastpath=True,)]
File "~/anaconda3/lib/python3.5/site-packages/pandas/core/internals.py", line 169, in make_block
return make_block(values, placement=placement, ndim=ndim, **kwargs)
File "~/anaconda3/lib/python3.5/site-packages/pandas/core/internals.py", line 2454, in make_block
placement=placement)
File "~/anaconda3/lib/python3.5/site-packages/pandas/core/internals.py", line 78, in __init__
raise ValueError('Wrong number of dimensions')
ValueError: Wrong number of dimensions
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-51-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 8.0.3
setuptools: 20.2.2
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
IPython: 4.0.3
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.0
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.5.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
Jinja2: None
```
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} | 2 | 2016-03-09T00:01:38Z | 2017-07-15T23:13:49Z | 2017-07-15T23:13:49Z | NONE | null | Hi,
I have found that fontsize is ignored on the secondary_y axis in DataFrame.plot in version 17.1. Is this by intention? (Apologies, if this is the wrong place to ask this or if it has already been asked as I was unable to find it.)
Thanks!
Example:
``` python
>>> import pandas as pd, numpy as np, matplotlib.pyplot as plt
>>> x = pd.DataFrame(np.random.randn(100,2), columns=list('AB')).assign(C = lambda df: df.B.cumsum())
>>> x.plot(secondary_y='C', fontsize=20).legend(fontsize=16)
<matplotlib.legend.Legend object at 0x05F0B990>
>>> plt.show() # Righthand y axis remains small
```
Here are my package versions:
``` python
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 32
OS: Windows
OS-release: 8
machine: AMD64
processor: AMD64 Family 21 Model 16 Stepping 1, AuthenticAMD
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.1
nose: None
pip: 1.5.6
setuptools: 7.0
Cython: None
numpy: 1.9.3
scipy: 0.16.0
statsmodels: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.0
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: None
```
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} | 4 | 2016-03-09T00:29:03Z | 2016-03-12T17:46:56Z | 2016-03-12T17:46:32Z | CONTRIBUTOR | null | - [x] closes #12558
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 2 | 2016-03-09T01:33:57Z | 2016-03-09T15:23:57Z | 2016-03-09T14:37:31Z | CONTRIBUTOR | null | CI: remove numpy_dev 2.7 build
CI: remove extraneous requirements
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TST: skip some unreliable tests in io/tests/test_data.py
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} | 8 | 2016-03-09T06:25:36Z | 2016-04-25T14:34:14Z | 2016-04-25T14:34:14Z | CONTRIBUTOR | null | It'd be great to have a simple `normalization` option for cross tab to get shares rather than frequencies.
Something that would do something like:
```
def normalize(x):
return len(x)/len(w_mobile.language)
pd.crosstab(w_mobile.language,w_mobile.carrier, values=w_mobile.language, aggfunc=normalize)
```
as just an option.
(The ability to do row-normalizations and column normalizations would also be great -- so all entries in a row add to 1 or all entries in a column add to 1). Similar in behavior (for row normalizations) as:
```
l = list()
df = pd.DataFrame({'carrier':['a','a','b','b','b'], 'language':['english','spanish', 'english','spanish','spanish']})
for i in df.carrier.unique():
temp = df.query('carrier=="{}"'.format(i)).language.value_counts(normalize=True)
temp.name = i
l.append(temp)
ctab = pd.concat(l, axis=1)
Out[1]:
a b
english 0.5 0.333333
spanish 0.5 0.666667
```
But with a command like: `pd.crosstab(df.language, df.carrier, normalization='row')`
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https://api.github.com/repos/pandas-dev/pandas/issues/12570 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12570/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12570/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12570/events | https://github.com/pandas-dev/pandas/issues/12570 | 139,604,621 | MDU6SXNzdWUxMzk2MDQ2MjE= | 12,570 | read_msgpack can't parse categorical data | {
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| null | 3 | 2016-03-09T15:16:27Z | 2016-03-09T15:20:55Z | 2016-03-09T15:18:59Z | CONTRIBUTOR | null | #### A copy-pastable code sample
``` python
import pandas
c = pandas.Categorical(["A", "B", "C", "D"], categories=["A", "B", "C", "D"])
df = pandas.DataFrame({"x": c})
bytes = df.to_msgpack()
pandas.read_msgpack(bytes)
```
#### What happens
``` pytb
<ipython-input-1-ec1dc8306f4c> in <module>()
1 import pandas
2 df = pandas.DataFrame({"x": pandas.Categorical(["A", "B", "C", "D"], categories=["A", "B", "C", "D"])})
----> 3 pandas.read_msgpack(df.to_msgpack())
/home/pwaller/.local/lib/python3.4/site-packages/pandas/io/packers.py in read_msgpack(path_or_buf, encoding, iterator, **kwargs)
153 try:
154 fh = compat.BytesIO(path_or_buf)
--> 155 return read(fh)
156 finally:
157 if fh is not None:
/home/pwaller/.local/lib/python3.4/site-packages/pandas/io/packers.py in read(fh)
131
132 def read(fh):
--> 133 l = list(unpack(fh, encoding=encoding, **kwargs))
134 if len(l) == 1:
135 return l[0]
pandas/msgpack/_unpacker.pyx in pandas.msgpack._unpacker.Unpacker.__next__ (pandas/msgpack/_unpacker.cpp:5277)()
pandas/msgpack/_unpacker.pyx in pandas.msgpack._unpacker.Unpacker._unpack (pandas/msgpack/_unpacker.cpp:4268)()
/home/pwaller/.local/lib/python3.4/site-packages/pandas/io/packers.py in decode(obj)
526 placement=placement)
527
--> 528 blocks = [create_block(b) for b in obj['blocks']]
529 return globals()[obj['klass']](BlockManager(blocks, axes))
530 elif typ == 'datetime':
/home/pwaller/.local/lib/python3.4/site-packages/pandas/io/packers.py in <listcomp>(.0)
526 placement=placement)
527
--> 528 blocks = [create_block(b) for b in obj['blocks']]
529 return globals()[obj['klass']](BlockManager(blocks, axes))
530 elif typ == 'datetime':
/home/pwaller/.local/lib/python3.4/site-packages/pandas/io/packers.py in create_block(b)
513
514 def create_block(b):
--> 515 values = unconvert(b['values'], dtype_for(b['dtype']),
516 b['compress']).reshape(b['shape'])
517
/home/pwaller/.local/lib/python3.4/site-packages/pandas/io/packers.py in dtype_for(t)
181 if t in dtype_dict:
182 return dtype_dict[t]
--> 183 return np.typeDict[t]
184
185 c2f_dict = {'complex': np.float64,
KeyError: 'category'
```
#### Expected Output
A DataFrame containing a categorical variable `x`.
#### output of `pd.show_versions()`
**Note** I have also tested 0.18.0rc1.
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-30-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.17.1
nose: 1.3.6
pip: 8.1.0
setuptools: 20.0
Cython: 0.23.4
numpy: 1.10.2
scipy: 0.16.1
statsmodels: 0.6.1
IPython: 4.1.1
sphinx: None
patsy: 0.4.1
dateutil: 2.5.0
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.7.3
lxml: None
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.9
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
Jinja2: None
```
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} | 3 | 2016-03-09T16:05:08Z | 2016-03-10T12:49:01Z | 2016-03-10T12:48:49Z | NONE | null | Using pandas 0.17.1 i was trying to use `panel.to_json`,
like documented here:
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Panel.to_json.html
This raises an `NotImplementedError`.
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} | 3 | 2016-03-09T16:57:09Z | 2016-03-11T01:39:04Z | 2016-03-11T01:39:04Z | NONE | null | In pandas/io/gbq.py there is the function:
`def _test_google_api_imports():
```
try:
import httplib2 # noqa
from apiclient.discovery import build # noqa
from apiclient.errors import HttpError # noqa
from oauth2client.client import AccessTokenRefreshError # noqa
from oauth2client.client import OAuth2WebServerFlow # noqa
from oauth2client.client import SignedJwtAssertionCredentials # noqa
from oauth2client.file import Storage # noqa
from oauth2client.tools import run_flow, argparser # noqa
except ImportError as e:
raise ImportError("Missing module required for Google BigQuery "
"support: {0}".format(str(e)))`
```
This will fail for users with oauth2client version higher than 2.0.0 since SignedJwtAssertionCredentials has been removed from the module.
Google suggests other ways of handling service account credentials. See:
https://github.com/google/oauth2client/blob/master/CHANGELOG.md#v200
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} | 17 | 2016-03-09T16:59:14Z | 2016-04-25T22:06:59Z | 2016-04-25T22:06:37Z | CONTRIBUTOR | null | Supercedes #12191.
I've made a best-effort to rebase this against master.
It seems to work here.
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} | 1 | 2016-03-09T18:12:26Z | 2016-03-16T13:59:42Z | 2016-03-16T13:59:42Z | CONTRIBUTOR | null | xref https://github.com/pydata/pandas/pull/12573#discussion_r55561598
``` python
In [37]: pd.Series(pd.Categorical([1,2,3]), dtype='category')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-37-87a1a3228158> in <module>()
----> 1 pd.Series(pd.Categorical([1,2,3]), dtype='category')
ValueError: cannot specify a dtype with a Categorical
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12575 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12575/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12575/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12575/events | https://github.com/pandas-dev/pandas/issues/12575 | 139,673,567 | MDU6SXNzdWUxMzk2NzM1Njc= | 12,575 | Provide logo as vector graphic | {
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| null | 3 | 2016-03-09T19:16:28Z | 2016-05-17T02:39:21Z | 2016-05-17T02:39:21Z | NONE | null | For presentations etc. it would be nice to be able to use
the pandas logo in any size.
Could you provide it as svg or pdf?
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| null | 4 | 2016-03-09T21:41:52Z | 2016-03-14T01:20:23Z | 2016-03-14T01:20:23Z | CONTRIBUTOR | null | Closes #12558
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} | 4 | 2016-03-09T21:57:10Z | 2016-03-16T13:17:21Z | 2016-03-16T13:17:21Z | CONTRIBUTOR | null | `crosstab` also has a bug -- it counts np.nan in margin totals even when `dropna=True`.
Appears independent of #12569 and #4003
```
df = pd.DataFrame({'a':[1,2,2,2,2,np.nan],'b':[3,3,4,4,4,4]})
pd.crosstab(df.a,df.b, margins=True)
Out[233]:
b 3 4 All
a
1.0 1 0 1
2.0 1 3 4
All 2 4 6
```
#### Expected Output
```
Out[233]:
b 3 4 All
a
1.0 1 0 1
2.0 1 3 4
All 2 3 5
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.4.4.final.0
python-bits: 64
OS: Darwin
OS-release: 15.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 8.1.0
setuptools: 20.2.2
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.16.1
statsmodels: None
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
Jinja2: 2.8
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Note does NOT address #12577
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---
- [x] closes #12529
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
I added the whatsnew entry to 0.18.0.
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- removed Panel.to_json from docs, as it is not yet implemented.
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} | 13 | 2016-03-10T03:12:37Z | 2016-03-12T13:53:36Z | 2016-03-11T01:39:04Z | CONTRIBUTOR | null | - [x] closes #12572
- [x] tests passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 7 | 2016-03-10T07:13:23Z | 2016-08-13T19:55:09Z | 2016-08-13T19:55:05Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
pd.to_datetime(["31/12/2014", "10/03/2011"])
Out[37]:
DatetimeIndex(['2014-12-31', '2011-10-03'], dtype='datetime64[ns]', freq=None)
```
#### Expected Output
```
DatetimeIndex(['2014-12-31', '2011-03-10'], dtype='datetime64[ns]', freq=None)
```
**_Reason: Expect a default behavior (without extra `format` or `dayfirst` parameter) that implements a consistent datetime parsing within the same column.**_
### Comments
Within the internal helper function `pd.tseries.tools._to_datetime`, the datetime format will be first inferred based on the first non-nan element, followed by a parsing via `tslib.array_strptime`([Line 357](https://github.com/pydata/pandas/blob/master/pandas/tseries/tools.py)), which gives expected result.
```
pd.tseries.tools._guess_datetime_format_for_array(["31/12/2014", "10/03/2011"])
Out[25]:
'%d/%m/%Y'
pd.tslib.array_strptime(pd.tseries.tools.com._ensure_object(["31/12/2014", "10/03/2011"]), "%d/%m/%Y")
Out[46]:
array(['2014-12-31T08:00:00.000000000+0800',
'2011-03-10T08:00:00.000000000+0800'], dtype='datetime64[ns]')
```
But the fallback function `tslib.array_to_datetime` doesn't use the inferred format([Line 373](https://github.com/pydata/pandas/blob/master/pandas/tseries/tools.py))
```
pd.tslib.array_to_datetime(pd.tseries.tools.com._ensure_object(["31/12/2014", "10/03/2011"]), format="%d/%m/%Y")
Out[51]:
array(['2014-12-31T08:00:00.000000000+0800',
'2011-10-03T08:00:00.000000000+0800'], dtype='datetime64[ns]')
```
**_This is caused by the default value (`False`) of parameter `infer_datetime_format` to the `_to_datetime` ([Line 285](https://github.com/pydata/pandas/blob/master/pandas/tseries/tools.py))
Suggestion: make parameter `infer_datetime_format` to the `_to_datetime` default to `True`, it won't solve all problems, but at least half of them, depending on the format of the first element.**_
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 3.8.13-44.el6uek.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 8.0.2
setuptools: 19.6.2
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
IPython: 4.0.3
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.5.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
Jinja2: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12584 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12584/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12584/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12584/events | https://github.com/pandas-dev/pandas/issues/12584 | 139,831,492 | MDU6SXNzdWUxMzk4MzE0OTI= | 12,584 | `apply` to multiple columns consumes a lot of memory | {
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| null | 13 | 2016-03-10T09:25:03Z | 2016-03-10T19:42:04Z | 2016-03-10T14:02:45Z | NONE | null | I was encouraged to post here after asking a [question on stackoverflow](https://stackoverflow.com/questions/35911319/pandas-apply-into-multiple-columns-consuming-lots-of-memory).
#### Code Sample, a copy-pastable example if possible
```
import numpy as np
import pandas as pd
# placeholder for real function
def process(x):
return np.random.rand(100)
s = pd.Series(range(2700000))
df = s.apply(lambda x: pd.Series(process(x)))
```
#### Expected Output
Never finishes on my machine, as it consumes in excess of 16GB of RAM.
If I instead write `df = s.apply(lambda x: process(x))` then it finishes quite quickly and without consuming that much memory, but in an incorrect layout for my use. I would expect the above to perform close to the same, as the only difference is in the layout of the dataframe.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.3-300.fc23.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_DK.UTF-8
pandas: 0.17.1
nose: None
pip: 7.1.0
setuptools: 18.0.1
Cython: None
numpy: 1.9.2
scipy: 0.14.1
statsmodels: None
IPython: 3.2.1
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.4
blosc: None
bottleneck: 0.6.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
Jinja2: None
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| null | 32 | 2016-03-10T10:30:31Z | 2021-09-23T17:41:02Z | null | MEMBER | null | As this comes up regularly:
- [x] https://github.com/pydata/pandas/issues/12583
- [x] https://github.com/pydata/pandas/issues/12501
- [x] https://github.com/pydata/pandas/issues/7348
- [x] https://github.com/pandas-dev/pandas/issues/15075
- [x] #12611 deal with `yearfirst/datefirst` in `read_csv`
- [x] #12730, compat with dateutil 2.5.2 `dayfirst=True, yearfirst=True` fixes
- [x] https://github.com/pydata/pandas/issues/14301 raise if inconsistency in `read_csv` with `dayfirst/yearfirst`
- [x] performance https://github.com/pandas-dev/pandas/issues/25848
**The problem**
Depending on the string formats, you can get inconsistent parsing (meaning: different interpretation of parts of the strings as days vs months between different values in the passed list/array of strings).
For example:
```
In [22]: pd.to_datetime(["31/12/2014", "10/03/2011"])
Out[22]: DatetimeIndex(['2014-12-31', '2011-10-03'])
```
where the first is parsed as 'dayfirst' and the second not. Above particular case can be solved by passing `dayfirst=True`, but in any case, the default behaviour of `to_datetime` results in inconsistent parsing here.
Another typical case if where you do provide `dayfirst=True` but have a malformed date, then it will fall back to 'not dayfirst' (https://github.com/pydata/pandas/issues/3341).
**The reason**
The reason is the flexibility of `dateutil.parser.parse`. This is in the first place also a feature, as it lets you parse messy datetime colums. But when it comes to flexibility regarding `dayfirst`/`yearfirst`, this gives unexpected results.
So dateutil will 'try' to use `dayfirst=True`, but when this does not give a valid date, it will ignore `dayfirst=True` and parse the string anyway:
```
In [3]: dateutil.parser.parse('1/12/2012', dayfirst=True)
Out[3]: datetime.datetime(2012, 12, 1, 0, 0)
In [4]: dateutil.parser.parse('1/13/2012', dayfirst=True)
Out[4]: datetime.datetime(2012, 1, 13, 0, 0)
```
The same is true for `dayfirst=False` (the default) as this will also try with 'day first' if the other way around failed.
The issue for the lack of strictness of dayfirst is actually https://github.com/pydata/pandas/issues/3341.
**Possible solutions**
- In some cases passing `dayfirst=True` is sufficient (if you don't have messy or malformed dates).
- The typical answer is saying: "if you want to be sure to have strict and consistent parsing, provide a `format` argument.
We should probably stress this more in the docstring of `to_datetime`
But, regardless of the two points above, fact is that the _default_ behaviour of `to_datetime` (without passing any arguments) can do inconsistent parsing and so give unexpected results.
Possible ways to have better default behaviour:
- Have the possibility to specify that `dateutil` should be strict about `dayfirst`. I raised an issue for that here: https://github.com/dateutil/dateutil/issues/214
- Changing the default of `infer_datetime_format` to `True` would solve it in some cases (where the first datetime has the correct format you want), but certainly not all.
- The idea of raising a warning when inconsistent parsing happened, is AFAIK not possible as pandas does not know how dateutil parsed the dates. And always warning when no format is passed or could be inferred (so in practice, when dateutil is used), is maybe to drastically, as the flexible parsing is also a feature.
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} | 16 | 2016-03-10T13:36:16Z | 2017-04-05T02:06:37Z | 2016-03-11T14:33:31Z | CONTRIBUTOR | null | The PDF doc build was chocking on the `Parameters` section being the first item.
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} | 2 | 2016-03-10T13:59:17Z | 2016-03-11T02:10:06Z | 2016-03-11T02:10:06Z | CONTRIBUTOR | null | waiting on breakage in: https://github.com/conda/conda-build/issues/820
to fix the conda-recipe
can just use `conda-build=1.19.0` ATM.
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} | 1 | 2016-03-11T00:55:24Z | 2017-04-16T10:44:06Z | 2017-04-16T10:44:00Z | CONTRIBUTOR | null | so most of these are in the top-level namespace, so let's rename to use `_` leading if not already like that.
e.g. `_lib` rather than `lib`
just requires some small changes in `setup.py`, but a fair number of imports need to be changed.
This is a slight API change, in that people could be relying upon it. So we _could_ create a deprecation warning if these names are accessed (and then have it work).
```
~/pandas]$ find pandas -name '*.so'
pandas/_period.so
pandas/_sparse.so
pandas/_testing.so
pandas/algos.so
pandas/hashtable.so
pandas/index.so
pandas/io/sas/saslib.so
pandas/json.so
pandas/lib.so
pandas/msgpack/_packer.so
pandas/msgpack/_unpacker.so
pandas/parser.so
pandas/tslib.so
```
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} | 0 | 2016-03-11T02:11:23Z | 2016-03-11T02:22:01Z | 2016-03-11T02:22:01Z | CONTRIBUTOR | null | windows doesn't like gcc/clang options, big surprise, so disable for now
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} | 2 | 2016-03-11T09:58:24Z | 2016-03-17T13:48:14Z | 2016-03-17T13:48:14Z | CONTRIBUTOR | null | Following builtin `set`, I would expect the method name to be `symmetric_difference`.
How about deprecating `sym_diff` in favour of this (see also #6016, #8226)?
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| null | 4 | 2016-03-11T10:23:45Z | 2016-03-11T14:37:35Z | 2016-03-11T14:07:54Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
In [50]: df
Out[50]:
a b c
0 NaN NaN NaN
1 1 1 1
2 1 NaN NaN
In [51]: df['total'] = df[['a', 'b', 'c']].sum(axis=1, skipna=True)
In [52]: df
Out[52]:
a b c total
0 NaN NaN NaN 0
1 1 1 1 3
2 1 NaN NaN 1
In [53]:
```
#### Expected Output
```
In [51]: df['total'] = df[['a', 'b', 'c']].sum(axis=1, skipna=True)
In [52]: df
Out[52]:
a b c total
0 NaN NaN NaN NaN
1 1 1 1 3
2 1 NaN NaN 1
In [53]:
```
#### output of `pd.show_versions()`
```
In [68]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.3-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.17.1
nose: 1.3.6
pip: 7.1.2
setuptools: 18.2
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: None
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.6
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.4.3
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: 3.4.4
bs4: None
html5lib: 0.9999999
httplib2: 0.9.1
apiclient: 1.4.2
sqlalchemy: 1.0.11
pymysql: 0.6.6.None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
Jinja2: None
In [69]:
```
#### Comment
As per [docs](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sum.html): `Exclude NA/null values. If an entire row/column is NA, the result will be NA`.
So for a row where all columns are NA expected output is NA, not a zero. Same behaviour also happens for a `Series`.
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| null | 2 | 2016-03-11T13:04:10Z | 2016-03-11T14:17:45Z | 2016-03-11T14:17:45Z | NONE | null | #### Code Sample, a copy-pastable example if possible
I got no error messages but there are no cell generated after I execute my python script. I'm using EI-Capitan and python 2.7.
```
import pandas as pd
import warnings
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="white", color_codes=True)
adult = pd.read_csv("data.csv")
adult.head()
```
#### Expected Output
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.17.0
nose: 1.3.7
pip: 8.1.0
setuptools: 19.4
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.16.1
statsmodels: None
IPython: 4.0.0
sphinx: None
patsy: None
dateutil: 1.5
pytz: 2015.7
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: 0.9.2
apiclient: 1.4.2
sqlalchemy: None
pymysql: None
psycopg2: None
```
Please advice. Thank you.
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} | 4 | 2016-03-11T14:09:02Z | 2016-03-17T13:48:23Z | 2016-03-17T13:48:15Z | CONTRIBUTOR | null | closes #12591
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} | 19 | 2016-03-11T15:31:51Z | 2016-06-07T12:40:26Z | 2016-03-17T15:13:06Z | CONTRIBUTOR | null | - [ ] Enhances previous bugfix to #12373
- [ ] Added test_rolling_min_max_test_types to test_windows
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] Added documentation to computation.rst for the new as_float argument and updated whatsnew for v0.18.0
- Changed the rolling min/max functions in algos.pyx so
that they use a cython fused type as input instead of a float64
so that the function can accept arrays of any numeric type
- Merged the functionality of rolling min/max into a common
function with branches based on whether or not it's running
min/max
- When running rolling min/max for intergral types and there
are not enough minimum periods, the output values returned
are zero
- Added a unit test to test_moments to make sure that rolling
min/max works for all integral types and float32/64
- Updated computations and whatsnew doc
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https://api.github.com/repos/pandas-dev/pandas/issues/12596 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12596/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12596/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12596/events | https://github.com/pandas-dev/pandas/issues/12596 | 140,216,823 | MDU6SXNzdWUxNDAyMTY4MjM= | 12,596 | Pandas 0.18.1rc2 requires GLIBC_2.14, but uses only a few (basic) symbols - cannot be used on Debian wheezy | {
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| null | 1 | 2016-03-11T15:44:19Z | 2016-03-11T16:07:34Z | 2016-03-11T16:07:34Z | NONE | null | We can install pandas 0.18.1rc2 on our wheezy VM but when we import it we get the following error:
```
Python 2.7.11 |Continuum Analytics, Inc.| (default, Dec 6 2015, 18:08:32)
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux2
>>> import pandas
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/miniconda/lib/python2.7/site-packages/pandas/__init__.py", line 31, in <module>
"extensions first.".format(module))
ImportError: C extension: /lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_2.14' not found (required by /opt/miniconda/lib/python2.7/site-packages/pandas/hashtable.so) not built. If you want to import pandas from the source directory, you may need to run 'python setup.py build_ext --inplace' to build the C extensions first.
```
Given that said hashtable.so only needs the following (non-libpython.so) symbols:
```
# nm -D /opt/miniconda/lib/python2.7/site-packages/pandas/hashtable.so | fgrep -v Py | fgrep ' U '
U calloc
U free
U malloc
U memcpy
U memset
U realloc
U strcmp
U strlen
U vsnprintf
```
would it be possible to make the releases in the pandas channel work with older versions of libc? Note that wheezy is still the version used by Google for their Appengine Python runtime image, among others. Thanks!
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| null | 3 | 2016-03-11T17:11:58Z | 2016-03-11T17:27:32Z | 2016-03-11T17:26:27Z | NONE | null | I think it would be useful to have a command similar to `table` in R or `tab` in Stata.
Given a vector `v` `table(v)` should return the frequency of each value in `v`. `table(v1, v2)` should return the cross tabulation of `v1` and `v2`
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| null | 2 | 2016-03-11T17:57:17Z | 2016-03-11T21:46:30Z | 2016-03-11T19:05:54Z | NONE | null | Include index names in column names of HTML output of DataFrame Styler, if the index names are available (previous behaviour is not to include index column names and instead leave blank spaces)
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} | 1 | 2016-03-11T20:13:35Z | 2016-05-23T13:05:43Z | 2016-05-23T13:05:43Z | NONE | null | #### Code Sample
import pandas as pd
def main():
```
df1 = pd.Series() #dtype = float64
df1["a"] = pd.Timestamp("2014-01-02") #dtype = datetime64
df1["b"] = "z" # OK ! => dtype = object
df1["c"] = 3.0 # OK! => dtype = object
df2 = pd.Series() #dtype = float64
df2["a"] = pd.Timestamp("2014-01-02") #dtype = datetime64
df2["c"] = 3.0 #BOOM !! => TypeError: invalid type promotion
df2["b"] = "z"
```
if **name** == "**main**":
main()
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 8.1
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.1
nose: 1.3.7
pip: 8.0.2
setuptools: 19.6.2
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
IPython: 4.0.3
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.5.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
Jinja2: 2.8
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} | 16 | 2016-03-12T03:35:12Z | 2016-05-30T19:00:05Z | 2016-03-17T22:57:43Z | CONTRIBUTOR | null | First, `conda install pandas` is currently pulling down 0.18.0, which was unexpected since I only see RCs right now, but, on to the real issue...
In 0.18.0 I cannot pass a series to np.round...
``` python
In [35]: np.round(pd.Series([1.11, 2.21]), 1)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-35-4cc66a785b99> in <module>()
----> 1 np.round(pd.Series([1.11, 2.21]), 1)
/Users/holmgren/miniconda3/envs/pvlib35/lib/python3.5/site-packages/numpy/core/fromnumeric.py in round_(a, decimals, out)
2784 except AttributeError:
2785 return _wrapit(a, 'round', decimals, out)
-> 2786 return round(decimals, out)
2787
2788
TypeError: round() takes from 1 to 2 positional arguments but 3 were given
```
np.round does still work on a DataFrame in 0.18.0...
``` python
In [5]: np.round(pd.DataFrame([1.11,2.22]), 1)
Out[5]:
0
0 1.1
1 2.2
```
A Series on pandas 0.17.1...
``` python
In [4]: np.round(pd.Series([1.12, 2.22]), 1)
Out[4]:
0 1.1
1 2.2
dtype: float64
```
Ok, I know that there's now a round method in pandas (which is great), but I like to use np.round in unit tests so that I can test against earlier versions of pandas.
Version info:
``` python
In [34]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.0
setuptools: 20.2.2
Cython: None
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.1.1
sphinx: 1.3.5
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
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
```
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} | 10 | 2016-03-12T11:12:20Z | 2018-06-28T10:23:02Z | 2018-06-28T10:23:02Z | MEMBER | null | Related to #8306. On current master, `Timestamp` comparison results in `TypeError` if its timezones are different. However, `Index` and `Series` implicitly converts tz to GMT
```
pd.Timestamp('2016-01-01 12:00', tz='US/Eastern') > pd.Timestamp('2016-01-01 08:00')
# TypeError: Cannot compare tz-naive and tz-aware timestamps
# same result as idx.tz_convert(None) > pd.Timestamp('2016-01-01 08:00')
idx = pd.date_range('2016-01-01 12:00', periods=10, freq='H', tz='Asia/Tokyo')
idx > pd.Timestamp('2016-01-01 08:00')
# array([False, False, False, False, False, False, True, True, True, True], dtype=bool)
```
Numeric ops raises `TypeError` as expected.
```
idx - pd.Timestamp('2016-01-01 08:00')
# TypeError: Timestamp subtraction must have the same timezones or no timezones
```
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} | 1 | 2016-03-12T16:25:39Z | 2016-03-12T17:31:20Z | 2016-03-12T17:31:14Z | CONTRIBUTOR | null | fixes the syntax error in the "comparison with R" docs
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} | 10 | 2016-03-12T23:38:10Z | 2016-03-17T23:16:03Z | 2016-03-17T22:57:44Z | MEMBER | null | Closes #12600 by re-accepting the `out` parameter as a `**kwargs` argument. Also removes the `out` parameter from the signature for `DataFrame.round` and replaces it with `**kwargs` like its `Series` counterpart. Furthermore, since `out` is a parameter that is not supported in `pandas`, checks that `out` is `None` and throws an error if it isn't.
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