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} | 9 | 2014-01-26T06:44:12Z | 2014-01-27T06:26:17Z | 2014-01-27T06:26:17Z | NONE | null | Hi all,
I've tried to install pandas 0.13.0 through `pip install pandas`, but failed to install it.
The last few lines of the error says:
```
fatal error: too many errors emitted, stopping now [-ferror-limit=]
1 warning and 20 errors generated.
error: command 'clang' failed with exit status 1
```
, and more detailed massages from clang suggests that the issue occurred while building the msgpack extension:
```
building 'pandas.msgpack' extension
clang -fno-strict-aliasing -fno-common -dynamic -I/usr/local/include -I/usr/local/opt/sqlite/include -I/usr/local/opt/tcl-tk/include -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -D__LITTLE_ENDIAN__=1 -Ipandas/src/klib -Ipandas/src -I/usr/local/Cellar/python/2.7.6/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/include -I/usr/local/Cellar/python/2.7.6/Frameworks/Python.framework/Versions/2.7/include/python2.7 -c pandas/msgpack.cpp -o build/temp.macosx-10.7-x86_64-2.7/pandas/msgpack.o
In file included from pandas/msgpack.cpp:316:
In file included from /usr/local/Cellar/python/2.7.6/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4:
In file included from /usr/local/Cellar/python/2.7.6/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17:
In file included from /usr/local/Cellar/python/2.7.6/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1760:
/usr/local/Cellar/python/2.7.6/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-W#warnings]
#warning "Using deprecated NumPy API, disable it by " \
^
pandas/msgpack.cpp:835:3: error: unknown type name 'template_context'
template_context ctx;
^
pandas/msgpack.cpp:854:70: error: C++ requires a type specifier for all declarations
PyObject *(*_unpack)(struct __pyx_obj_6pandas_7msgpack_Unpacker *, execute_fn, PyObject *, struct __pyx_opt_args_6pandas_7msgpack_8Unpacker__unpack *__pyx_optional_args);
^~~~~~~~~~
pandas/msgpack.cpp:1355:66: error: unknown type name 'template_context'
static CYTHON_INLINE PyObject *__pyx_f_6pandas_7msgpack_init_ctx(template_context *, PyObject *, PyObject *, PyObject *, int, char *, char *); /*proto*/
^
pandas/msgpack.cpp:1854:20: error: no member named 'buf' in 'msgpack_packer'
__pyx_v_self->pk.buf = ((char *)malloc(__pyx_v_buf_size));
~~~~~~~~~~~~~~~~ ^
pandas/msgpack.cpp:1863:34: error: no member named 'buf' in 'msgpack_packer'
__pyx_t_1 = ((__pyx_v_self->pk.buf == NULL) != 0);
~~~~~~~~~~~~~~~~ ^
pandas/msgpack.cpp:1889:20: error: no member named 'buf_size' in 'msgpack_packer'
__pyx_v_self->pk.buf_size = __pyx_v_buf_size;
~~~~~~~~~~~~~~~~ ^
pandas/msgpack.cpp:1898:20: error: no member named 'length' in 'msgpack_packer'
__pyx_v_self->pk.length = 0;
~~~~~~~~~~~~~~~~ ^
(continue)
```
Note that this is a part of the whole error message.
The pip.log file is also available from https://dl.dropboxusercontent.com/u/25281592/pip-pandas.log
My environment is:
- Mac OS X Mavericks 10.9.1
- Python 2.7.6 (installed through homebrew)
- pip 1.4.1
- clang
- Apple LLVM version 5.0 (clang-500.2.79) (based on LLVM 3.3svn)
- Target: x86_64-apple-darwin13.0.0
- Thread model: posix
The previous version (pandas 0.12.0) can be successfully installed in this environment.
Does anyone have any suggestions, or am I doing something wrong?
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} | 70 | 2014-01-26T13:01:26Z | 2014-02-09T22:43:39Z | 2014-02-03T05:17:08Z | NONE | null | We're due to release 0.13.1 in a few days (or as soon as we clear the queue),
as a bugfix release stabilising 0.13.0, allowing us to start on the 3 month release cycle for 0.14.
Anything that isn't in 0.13.1 will have to wait until then to hit pypi.
Any objections, warnings or notes?
cc @jseabold , @yarikoptic, @juliantaylor, @takluyver
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} | 0 | 2014-01-26T15:09:30Z | 2014-07-16T08:49:12Z | 2014-01-26T16:11:50Z | CONTRIBUTOR | null | close #6047
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} | 1 | 2014-01-26T15:58:54Z | 2016-10-12T23:04:33Z | 2014-01-27T03:11:54Z | NONE | null | if you call this method using only the month, an UnboundedLocalError exception is trown
```
goog_opt.get_options_data(month=3)
```
```
/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/io/data.pyc in _get_option_data(self, month, year, expiry, table_loc, name)
639
640 if month:
--> 641 name += m1 + str(year)[-2:]
642 setattr(self, name, option_data)
643 return option_data
UnboundLocalError: local variable 'm1' referenced before assignment
```
Apparently the issue is in here: https://github.com/pydata/pandas/blob/master/pandas/io/data.py#L639
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``` python
>>> import pandas
>>> pandas.Series.str
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "properties.pyx", line 25, in pandas.lib.cache_readonly.__get__ (pandas/lib.c:29334)
AttributeError: 'NoneType' object has no attribute '_cache'
```
Expected: a more clear message like:
``` python
>>> pandas.Series.bjdksalfjak
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: type object 'Series' has no attribute 'bjdksalfjak'
```
The current behaviour sends the wrong message, as I've discovered with some difficulty trying to debug https://github.com/pydata/pandas/pull/5530#issuecomment-33304367
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} | 0 | 2014-01-26T18:25:01Z | 2014-06-23T13:10:25Z | 2014-01-26T18:25:09Z | CONTRIBUTOR | null | closes #6106
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closes #5855
closes #5845
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outliers are getting in the way.
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} | 3 | 2014-01-26T22:20:36Z | 2014-06-13T01:11:38Z | 2014-01-28T03:13:56Z | CONTRIBUTOR | null | Network decorator was ignoring test failures due to API changes in external
services.
Closes #5816.
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} | 5 | 2014-01-27T02:54:32Z | 2015-09-24T11:32:00Z | 2015-09-24T11:32:00Z | CONTRIBUTOR | null | For read_table, Would it be possible to make "header=None" and "header=False" have the same behavior? Interpreting "False" as saying that the first line is the header has tricked me a couple times...
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} | 0 | 2014-01-27T03:51:17Z | 2014-07-16T08:49:30Z | 2014-01-27T03:51:24Z | NONE | null | new test added in #5583 does not skip_xlrd() and caused failures on sparc.
Noting that we don't seem to have min-dep coverage in travis.
cc @Jaydyou
wow. [that](https://github.com/pydata/pandas/pull/5995#issuecomment-33333749) sure payed itself off right quick.
cc @yarikoptic
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are... not my favorite kind.
```
Did something change in pandas recently that would effect
df.groupby('col')['date'].min()? Returning int64 instead of datetime64
```
cc @jreback
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} | 0 | 2014-01-27T08:11:41Z | 2014-07-16T08:49:31Z | 2014-01-27T11:08:14Z | CONTRIBUTOR | null | Added a test to verify that #4172 (failed read from an Excel `xlsm` file) is no longer an issue.
The test extends 3 of the existing Excel reader tests to also read from an `xlsm` (macro) file.
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} | 1 | 2014-01-27T09:08:44Z | 2014-06-13T01:27:07Z | 2014-02-07T10:30:39Z | NONE | null | 
works for line plots, bar plots, and kde plots. scatter plots don't seem to have a legend.
As usual, the plotting code is a tangled mess, I have no idea if this breaks something,
testing seems like a herculean task and I'm scared to death that by being brave and merging
this, I'm setting myself up to touch the plotting code again in the future, at least once. quite
the deterrent.
#6014
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https://api.github.com/repos/pandas-dev/pandas/issues/6119 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6119/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6119/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6119/events | https://github.com/pandas-dev/pandas/issues/6119 | 26,345,534 | MDU6SXNzdWUyNjM0NTUzNA== | 6,119 | Numexpr 2.2.2: "could not operate with block values <...>" error | {
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} | 21 | 2014-01-27T11:07:41Z | 2014-01-28T10:19:13Z | 2014-01-27T19:35:43Z | CONTRIBUTOR | null | Under numexpr 2.2.2 (and for some reason that's the latest version available in the anaconda distribution at the moment), I get the following kind of errors:
``` python
>>> data.shape
(2, 219, 104, 6)
>>> data.dtype
dtype('float64')
>>> df = pd.Panel(data[0]).to_frame().reset_index()
>>> df[0].dtype
dtype('float64')
>>> df[0].count()
711
>>> df[0] + 1
TypeError: Could not operate 1 with block values using `oa_ndim == 0` when `op_axes` is NULL is
deprecated. Use `oa_ndim == -1` or the MultiNew iterator for NumPy <1.8 compatibility
```
Looks like the same as [here](https://github.com/PyTables/PyTables/issues/259) and [here](https://groups.google.com/forum/#!topic/pydata/h4ww3t2hnCQ).
Upgrading to numexpr 2.3 solves it. Should v2.3 be then strictly required by pandas?
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https://api.github.com/repos/pandas-dev/pandas/issues/6120 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6120/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6120/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6120/events | https://github.com/pandas-dev/pandas/issues/6120 | 26,345,637 | MDU6SXNzdWUyNjM0NTYzNw== | 6,120 | Assign to df with repeated column fails | {
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} | 3 | 2014-01-27T11:09:40Z | 2014-01-27T13:04:29Z | 2014-01-27T12:57:15Z | CONTRIBUTOR | null | If you have a DataFrame with a repeated or non-unique column, then some assignments fail.
``` python
df = pd.DataFrame(np.random.randn(10,2), columns=['that', 'that'])
df2
Out[10]:
that that
0 1 1
1 1 1
2 1 1
3 1 1
4 1 1
5 1 1
6 1 1
7 1 1
8 1 1
9 1 1
[10 rows x 2 columns]
```
This is float data and the following works:
``` python
df['that'] = 1.0
```
However, this fails with an error and breaks the dataframe (e.g. a subsequent repr will also fail.)
``` python
df2['that'] = 1
Traceback (most recent call last):
File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/ipython-1.1.0_1_ahl1-py2.7.egg/IPython/core/interactiveshell.py", line 2830, in run_code
exec code_obj in self.user_global_ns, self.user_ns
File "<ipython-input-11-8701f5b0efe4>", line 1, in <module>
df2['that'] = 1
File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.0_292_g4dcecb0-py2.7-linux-x86_64.egg/pandas/core/frame.py", line 1879, in __setitem__
self._set_item(key, value)
File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.0_292_g4dcecb0-py2.7-linux-x86_64.egg/pandas/core/frame.py", line 1960, in _set_item
NDFrame._set_item(self, key, value)
File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.0_292_g4dcecb0-py2.7-linux-x86_64.egg/pandas/core/generic.py", line 1057, in _set_item
self._data.set(key, value)
File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.0_292_g4dcecb0-py2.7-linux-x86_64.egg/pandas/core/internals.py", line 2968, in set
_set_item(item, arr[None, :])
File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.0_292_g4dcecb0-py2.7-linux-x86_64.egg/pandas/core/internals.py", line 2927, in _set_item
self._add_new_block(item, arr, loc=None)
File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.0_292_g4dcecb0-py2.7-linux-x86_64.egg/pandas/core/internals.py", line 3108, in _add_new_block
new_block = make_block(value, self.items[loc:loc + 1].copy(),
TypeError: unsupported operand type(s) for +: 'slice' and 'int'
```
I stepped through the code and it looked like most places handle repeated columns ok except the code that reallocates arrays when the dtype changes.
I've tested this against pandas 0.13.0 and the latest master. Here's the output of installed versions when running on the master:
commit: None
python: 2.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.18-308.el5
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB
pandas: 0.13.0-292-g4dcecb0
Cython: 0.16
numpy: 1.7.1
scipy: 0.9.0
statsmodels: None
patsy: None
scikits.timeseries: None
dateutil: 1.5
pytz: None
bottleneck: 0.6.0
tables: 2.3.1-1
numexpr: 2.0.1
matplotlib: 1.1.1
openpyxl: None
xlrd: 0.8.0
xlwt: None
xlsxwriter: None
sqlalchemy: None
lxml: 2.3.6
bs4: None
html5lib: None
bq: None
apiclient: None
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https://api.github.com/repos/pandas-dev/pandas/issues/6121 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6121/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6121/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6121/events | https://github.com/pandas-dev/pandas/issues/6121 | 26,348,530 | MDU6SXNzdWUyNjM0ODUzMA== | 6,121 | Slice by column then by index fails if columns/rows are repeated. | {
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} | 4 | 2014-01-27T12:10:08Z | 2014-01-28T11:00:46Z | 2014-01-27T14:17:59Z | CONTRIBUTOR | null | We've found a problem where repeating a row and a column in a DataFrame fails with a "Cannot create BlockManager._ref_locs" assertion error.
The dataframe is very simple:
``` python
df = pd.DataFrame(np.arange(25.).reshape(5,5),
index=['a', 'b', 'c', 'd', 'e'],
columns=['a', 'b', 'c', 'd', 'e'])
```
And we pull the data out like this:
``` python
z = df[['a', 'c', 'a']]
z.ix[['a', 'c', 'a']]
Traceback (most recent call last):
File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/ipython-1.1.0_1_ahl1-py2.7.egg/IPython/core/interactiveshell.py", line 2830, in run_code
exec code_obj in self.user_global_ns, self.user_ns
File "<ipython-input-87-3bdc0aacc4b5>", line 1, in <module>
z.ix[['a', 'c', 'a']]
File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.0_292_g4dcecb0-py2.7-linux-x86_64.egg/pandas/core/indexing.py", line 56, in __getitem__
return self._getitem_axis(key, axis=0)
File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.0_292_g4dcecb0-py2.7-linux-x86_64.egg/pandas/core/indexing.py", line 744, in _getitem_axis
return self._getitem_iterable(key, axis=axis)
File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.0_292_g4dcecb0-py2.7-linux-x86_64.egg/pandas/core/indexing.py", line 816, in _getitem_iterable
convert=False)
File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.0_292_g4dcecb0-py2.7-linux-x86_64.egg/pandas/core/generic.py", line 1164, in take
new_data = self._data.take(indices, axis=baxis)
File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.0_292_g4dcecb0-py2.7-linux-x86_64.egg/pandas/core/internals.py", line 3366, in take
ref_items=new_axes[0], axis=axis)
File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.0_292_g4dcecb0-py2.7-linux-x86_64.egg/pandas/core/internals.py", line 2337, in apply
do_integrity_check=do_integrity_check)
File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.0_292_g4dcecb0-py2.7-linux-x86_64.egg/pandas/core/internals.py", line 1990, in __init__
self._set_ref_locs(do_refs=True)
File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.0_292_g4dcecb0-py2.7-linux-x86_64.egg/pandas/core/internals.py", line 2130, in _set_ref_locs
'have _ref_locs set' % (block, labels))
AssertionError: Cannot create BlockManager._ref_locs because block [FloatBlock: [a], 1 x 3, dtype: float64] with duplicate items [Index([u'a', u'c', u'a'], dtype='object')] does not have _ref_locs set
```
If instead we take a copy of the intermediate step, then it works:
``` python
z = df[['a', 'c', 'a']].copy()
z.ix[['a', 'c', 'a']]
Out[89]:
a c a
a 0 2 0
c 10 12 10
a 0 2 0
[3 rows x 3 columns]
```
This means that if you several functions which each do a part of the data processing, you need to know the history of an object to know whether what you're doing works. I think .ix should _always_ succeed on a DataFrame or Series, regardless of how it was constructed.
(I've read the discussion at https://github.com/pydata/pandas/issues/6056 about chained operations - but it's not something you can avoid if you have a pipeline of small steps instead of one big step).
This wasn't an issue in 0.11.0 but is failing in 0.13.0 and the latest master. Here's the output of installed versions when running on the master:
commit: None
python: 2.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.18-308.el5
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB
pandas: 0.13.0-292-g4dcecb0
Cython: 0.16
numpy: 1.7.1
scipy: 0.9.0
statsmodels: None
patsy: None
scikits.timeseries: None
dateutil: 1.5
pytz: None
bottleneck: 0.6.0
tables: 2.3.1-1
numexpr: 2.0.1
matplotlib: 1.1.1
openpyxl: None
xlrd: 0.8.0
xlwt: None
xlsxwriter: None
sqlalchemy: None
lxml: 2.3.6
bs4: None
html5lib: None
bq: None
apiclient: None
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} | 0 | 2014-01-27T13:54:50Z | 2014-06-22T13:28:39Z | 2014-01-27T14:17:59Z | CONTRIBUTOR | null | closes #6121
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} | 6 | 2014-01-27T14:33:39Z | 2014-03-05T21:58:54Z | 2014-03-05T21:58:54Z | CONTRIBUTOR | null | See #6068
## Use case
Facilitate DataFrame group/apply transformations when using a function that returns a Series. Right now, if we perform the following:
```
import pandas
df = pandas.DataFrame(
{'a': [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2],
'b': [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1],
'c': [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
'd': [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],
})
def count_values(df):
return pandas.Series({'count': df['b'].sum(), 'mean': df['c'].mean()}, name='metrics')
result = df.groupby('a').apply(count_values)
print result.stack().reset_index()
```
We get the following output:
```
a level_1 0
0 0 count 2.0
1 0 mean 0.5
2 1 count 2.0
3 1 mean 0.5
4 2 count 2.0
5 2 mean 0.5
[6 rows x 3 columns]
```
Ideally, the series name should be preserved and propagated through these operations such that we get the following output:
```
a metrics 0
0 0 count 2.0
1 0 mean 0.5
2 1 count 2.0
3 1 mean 0.5
4 2 count 2.0
5 2 mean 0.5
[6 rows x 3 columns]
```
The only way to achieve this (currently) is:
```
result = df.groupby('a').apply(count_values)
result.columns.name = 'metrics'
print result.stack().reset_index()
```
However, the key issue here is 1) this adds an extra line of code and 2) the name of the series created in the applied function may not be known in the outside block (so we can't properly fix the result.columns.name attribute).
The other work-around is to name the index of the series:
```
def count_values(df):
series = pandas.Series({'count': df['b'].sum(), 'mean': df['c'].mean()})
series.index.name = 'metrics'
return series
```
During the group/apply operation, one approach is to check to see whether series.index has the name attribute set. If the name attribute is not set, it will set the index.name attribute to the name of the series (thus ensuring the name propagates).
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} | 2 | 2014-01-27T15:19:18Z | 2014-01-28T10:55:22Z | 2014-01-27T16:22:38Z | CONTRIBUTOR | null | When you use apply on a DataFrame with datetimes in, the result is unexpected. This is a dataframe with just integers and strings and the result is that we get the market names back out.
``` python
positions = pd.DataFrame([[1, 'ABC', 50], [1, 'YUM', 20],
[1, 'DEF', 20], [2, 'ABC', 50],
[2, 'YUM', 20], [2, 'DEF', 20]],
columns=['a', 'market', 'position'])
positions.apply(lambda r: r['market'], axis=1)
Out[210]:
0 ABC
1 YUM
2 DEF
3 ABC
4 YUM
5 DEF
dtype: object
```
If we replace the data in column 'a' with datetimes, then we get the wrong result - the first value in the market column is repeated:
``` python
import datetime
positions = pd.DataFrame([[datetime.datetime(2013, 1, 1), 'ABC', 50],
[datetime.datetime(2013, 1, 1), 'YUM', 20],
[datetime.datetime(2013, 1, 1), 'DEF', 20],
[datetime.datetime(2013, 1, 2), 'ABC', 50],
[datetime.datetime(2013, 1, 2), 'YUM', 20],
[datetime.datetime(2013, 1, 2), 'DEF', 20]],
columns=['a', 'market', 'position'])
positions.apply(lambda r: r['market'], axis=1)
Out[213]:
0 ABC
1 ABC
2 ABC
3 ABC
4 ABC
5 ABC
dtype: object
```
If you replace the lambda function with a function which prints the object passed in, then you can see that you only ever receive the first row of the dataframe:
``` python
def print_input(r):
print r
return 1
positions.apply(print_input, axis=1)
a 2013-01-01 00:00:00
market ABC
position 50
Name: 0, dtype: object
a 2013-01-01 00:00:00
market ABC
position 50
Name: 1, dtype: object
a 2013-01-01 00:00:00
market ABC
position 50
Name: 2, dtype: object
a 2013-01-01 00:00:00
market ABC
position 50
Name: 3, dtype: object
a 2013-01-01 00:00:00
market ABC
position 50
Name: 4, dtype: object
a 2013-01-01 00:00:00
market ABC
position 50
Name: 5, dtype: object
Out[215]:
0 1
1 1
2 1
3 1
4 1
5 1
dtype: int64
```
This is new in the master, I didn't see it in pandas 0.11.0 or 0.13.0.
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} | 0 | 2014-01-27T15:34:23Z | 2014-06-23T20:08:10Z | 2014-01-27T16:22:38Z | CONTRIBUTOR | null | closes #6125
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https://api.github.com/repos/pandas-dev/pandas/issues/6127 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6127/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6127/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6127/events | https://github.com/pandas-dev/pandas/issues/6127 | 26,363,751 | MDU6SXNzdWUyNjM2Mzc1MQ== | 6,127 | Since 0.13: passing pandas DataFrame/Series like numpy array breaks | {
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} | [] | closed | false | null | [] | null | 19 | 2014-01-27T16:16:52Z | 2014-06-12T21:35:37Z | 2014-01-30T01:28:26Z | CONTRIBUTOR | null | As discussed in #6063:
I noticed that that numpy-style access breaks sometimes under 0.13. While I haven't been able to pin-point the issue, calls like `pylab.hist(-df.ix[row, col_name])` fail with some `x[0]` index error and I have to use `pylab.hist(-df.ix[row, col_name]).values`.
Here is a csv file for which this happens: https://gist.github.com/8651509
``` python
plt.hist(pd.read_csv('debug.csv'))
```
produces:
``` python
--------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-203-b20a1ff5d1db> in <module>()
----> 1 hist(pd.load('debug.pickle'))
/home/ipython/envs/ipynb/local/lib/python2.7/site-packages/matplotlib/pyplot.pyc in hist(x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, hold, **kwargs)
2825 histtype=histtype, align=align, orientation=orientation,
2826 rwidth=rwidth, log=log, color=color, label=label,
-> 2827 stacked=stacked, **kwargs)
2828 draw_if_interactive()
2829 finally:
/home/ipython/envs/ipynb/local/lib/python2.7/site-packages/matplotlib/axes.pyc in hist(self, x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)
8247 # Massage 'x' for processing.
8248 # NOTE: Be sure any changes here is also done below to 'weights'
-> 8249 if isinstance(x, np.ndarray) or not iterable(x[0]):
8250 # TODO: support masked arrays;
8251 x = np.asarray(x)
/home/ipython/envs/ipynb/local/lib/python2.7/site-packages/pandas/core/series.pyc in __getitem__(self, key)
482 def __getitem__(self, key):
483 try:
--> 484 result = self.index.get_value(self, key)
485 if isinstance(result, np.ndarray):
486 return self._constructor(result,index=[key]*len(result)).__finalize__(self)
/home/ipython/envs/ipynb/local/lib/python2.7/site-packages/pandas/core/index.pyc in get_value(self, series, key)
1030
1031 try:
-> 1032 return self._engine.get_value(s, k)
1033 except KeyError as e1:
1034 if len(self) > 0 and self.inferred_type == 'integer':
/home/ipython/envs/ipynb/local/lib/python2.7/site-packages/pandas/index.so in pandas.index.IndexEngine.get_value (pandas/index.c:2890)()
/home/ipython/envs/ipynb/local/lib/python2.7/site-packages/pandas/index.so in pandas.index.IndexEngine.get_value (pandas/index.c:2702)()
/home/ipython/envs/ipynb/local/lib/python2.7/site-packages/pandas/index.so in pandas.index.IndexEngine.get_loc (pandas/index.c:3440)()
/home/ipython/envs/ipynb/local/lib/python2.7/site-packages/pandas/hashtable.so in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:6595)()
/home/ipython/envs/ipynb/local/lib/python2.7/site-packages/pandas/hashtable.so in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:6536)()
KeyError: 0
```
While passing `.values` works.
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} | [] | closed | false | null | [] | null | 7 | 2014-01-27T16:26:15Z | 2014-01-27T17:22:42Z | 2014-01-27T17:18:27Z | NONE | null | Building from the current master, I receive an ImportError from `__init__.py` related to `SNDArray` on import of Pandas.
```
cannot import name SNDArray
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-1-d6ac987968b6> in <module>()
----> 1 import pandas
/Users/fonnescj/.virtualenvs/pymc2/lib/python2.7/site-packages/pandas-0.13.0_393_g1112cb7-py2.7-macosx-10.9-intel.egg/pandas/__init__.py in <module>()
4
5 try:
----> 6 from . import hashtable, tslib, lib
7 except Exception: # pragma: no cover
8 import sys
/Users/fonnescj/reduce.pyx in init pandas.lib (pandas/lib.c:53124)()
ImportError: cannot import name SNDArray
```
I'm running Python 2.7.5 on OS X 10.9.1, and build Pandas and the rest of the scientific stack pretty regularly. This is the first time this has come up. My build script is as follows:
```
rm -rf build dist
git pull
yes "" | make clean
make tseries
make sparse
python setup.py bdist_egg
easy_install -Z -N dist/*egg
```
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} | 1 | 2014-01-27T17:24:19Z | 2014-01-27T17:58:18Z | 2014-01-27T17:58:10Z | NONE | null | Appending a Series to a DataFrame does not create new columns in the DataFrame for unmatched labels in the Series.
```
df = pd.DataFrame({'a': {'x': 1, 'y': 2}, 'b': {'x': 3, 'y': 4}})
row = pd.Series([5, 6, 7], index=['a', 'b', 'c'], name='z')
df = df.append(row)
print(df.to_string())
```
Result:
```
a b
x 1 3
y 2 4
z 5 6
```
This behavior is contrary to the documentation for append().
## INSTALLED VERSIONS
Python: 3.3.0.final.0
OS: Windows
Release: 7
Processor: x86 Family 6 Model 37 Stepping 5, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.13.0
Cython: Not installed
Numpy: 1.7.0rc1
Scipy: Not installed
statsmodels: Not installed
patsy: Not installed
scikits.timeseries: Not installed
dateutil: 2.2
pytz: 2013.9
bottleneck: Not installed
PyTables: Not Installed
numexpr: Not Installed
matplotlib: 1.3.0
openpyxl: Not installed
xlrd: Not installed
xlwt: Not installed
xlsxwriter: Not installed
sqlalchemy: Not installed
lxml: Not installed
bs4: Not installed
html5lib: Not installed
bigquery: Not installed
apiclient: Not installed
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} | 8 | 2014-01-27T17:44:18Z | 2018-09-09T17:11:29Z | 2018-09-09T17:11:29Z | NONE | null | ``` python
import pandas
df = pandas.DataFrame([
{'a': 'aa', 'b': 'ba', 'c': 'ca', 'd': 1},
{'a': 'ab', 'b': 'bb', 'c': 'cc', 'd': 2},
{'a': 'ac', 'b': 'bc', 'c': 'cc', 'd': 3}
])
df.pivot_table(cols=['a'], rows=['b', 'c']).to_html(max_cols=2)
```
This renders like this:
while this:
``` python
df.pivot_table(cols=['a'], rows=['b']).to_html(max_cols=2)
```
renders like this:
As you can see in the first table, there are no ellipsis cells like in the second one.
I inspected the code to find the reason for this bug but couldn't find…
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} | 12 | 2014-01-27T21:04:44Z | 2014-07-10T09:36:33Z | 2014-01-28T00:52:46Z | CONTRIBUTOR | null | fixes #3695.
```
In [8]: s = pd.Series(['a|b', 'a|c', np.nan], list('ABC'))
In [9]: s.str.get_dummies('|')
Out[9]:
a b c
A 1 1 0
B 1 0 1
C 0 0 0
```
_iterates over each tag filling in with str.contains._
Edit: This works pretty fast with "few" tags (since I'm iterating over each tag), which I think is the main usecase, but I'm sure some more perf can be eeked out:
```
In [6]: %timeit genres.str.get_dummies('|')
10 loops, best of 3: 20.9 ms per loop
In [7]: %timeit genres.str.split('|').apply(lambda x: pd.Series(1., x)).fillna(0)
1 loops, best of 3: 623 ms per loop
```
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} | 2 | 2014-01-27T22:59:24Z | 2014-07-16T08:49:48Z | 2014-01-28T01:05:02Z | MEMBER | null | closes #5435. Clarified that ExcelFile is not deprecated but just moved. I don't use Excel myself, so can somebody check if it makes sense what I wrote? (@
For now, I didn't add ExcelFile to api.rst (but ExcelFile.parse is already in there, so it is already in some way mentioned), as I am not sure if all it's methods (`mro`?) should be public (this has possibly to be cleaned up first)
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} | 50 | 2014-01-27T23:12:15Z | 2014-06-13T23:55:57Z | 2014-02-04T13:28:55Z | NONE | null | Selection only. Did not check (nor care) about performance at this point.
Full Tests, vbenches and docs to follow in due course.
Please hold off on PEP8 comments or suggestions on how this could be made more complex.
I'd like to get something functional, solid and understandable in master fairly early in 0.14.
@jreback, I don't often wade this deep into the gore. Would welcome your review and suggestions.
Example of setting usage:
http://stackoverflow.com/questions/21539260/assign-to-multiindex-slice/21539479#21539479
xref: #5641, #3057, #4036, and others
``` python
In [1]:
...: def mklbl(prefix,n):
...: return ["%s%s" % (prefix,i) for i in range(n)]
...:
...: ix =pd.MultiIndex.from_product([mklbl('A',5),mklbl('B',7),mklbl('C',4),mklbl('D',2)])
...: df=pd.DataFrame(range(len(ix.get_values())),index=ix)
In [5]: df
Out[5]:
0
A0 B0 C0 D0 0
D1 1
C1 D0 2
D1 3
C2 D0 4
D1 5
C3 D0 6
D1 7
B1 C0 D0 8
D1 9
C1 D0 10
D1 11
```
``` python
In [2]: df.loc[(slice('A1','A3'),slice(None), ['C1','C3']),:]
Out[2]:
0
A1 B0 C1 D0 58
D1 59
C3 D0 62
D1 63
B1 C1 D0 66
D1 67
C3 D0 70
D1 71
B2 C1 D0 74
D1 75
C3 D0 78
D1 79
B3 C1 D0 82
D1 83
C3 D0 86
D1 87
B4 C1 D0 90
D1 91
C3 D0 94
D1 95
B5 C1 D0 98
D1 99
C3 D0 102
D1 103
B6 C1 D0 106
D1 107
C3 D0 110
D1 111
A2 B0 C1 D0 114
D1 115
C3 D0 118
D1 119
B1 C1 D0 122
D1 123
C3 D0 126
D1 127
B2 C1 D0 130
D1 131
C3 D0 134
D1 135
...
[56 rows x 1 columns]
```
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} | 30 | 2014-01-28T06:01:06Z | 2014-06-12T13:29:23Z | 2014-05-02T12:51:32Z | CONTRIBUTOR | null | fixes #3984.
Still need to tweak the docstrings and stuff. But I think this is working.
cc @jtratner
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} | 5 | 2014-01-28T07:42:52Z | 2014-01-31T21:49:46Z | 2014-01-31T16:25:24Z | NONE | null | This has failed intermittently a couple of times in the last week or two.
```
=============================================================
=======================
FAILURE: test_how_lambda_functions (pandas.tseries.tests.test_resample.TestResample)
------------------------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Python27-AMD64\Lib\unittest\case.py", line 331, in run
testMethod()
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\27\pandas\tseries\tests\test_resample.py", line 640, in test_how_lambda_functions
tm.assert_series_equal(result['bar'], bar_exp)
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\27\pandas\util\testing.py", line 448, in assert_series_equal
assert_almost_equal(left.values, right.values, check_less_precise)
File "testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas\src\testing.c:2561)
File "testing.pyx", line 93, in pandas._testing.assert_almost_equal (pandas\src\testing.c:1803)
File "testing.pyx", line 107, in pandas._testing.assert_almost_equal (pandas\src\testing.c:2023)
AssertionError: First object is not null, second is null: inf != nan
```
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} | 3 | 2014-01-28T08:05:02Z | 2014-06-14T21:42:59Z | 2014-01-28T12:40:18Z | CONTRIBUTOR | null | This fixes the main JSON performance regression in v0.13 (closes #5765). The main bottleneck was the use of intermediate NumPy scalars (from PR #4498). I introduced code to avoid the use of NumPy scalars. Also:
- checks current locale so locale fudging is only done if necessary
- added numpy 1.6 preprocessor switches as it still requires the use of scalars due to its weird datetime handling
- reorgranised some of the if/else checks in `objToJSON.c` based on likelihood.
- added some JSON benchmarks to vbench to try and avoid future regressions.
0.12
``` python
In [1]: import pandas as pd, numpy as np
In [2]: df = pd.DataFrame(np.random.rand(100000,10))
In [3]: %timeit df.to_json(orient='split')
10 loops, best of 3: 119 ms per loop
In [4]: pd.__version__, np.__version__
Out[4]: ('0.12.0', '1.8.0')
```
This PR
``` python
In [1]: import pandas as pd, numpy as np
In [2]: df = pd.DataFrame(np.random.rand(100000,10))
In [3]: %timeit df.to_json(orient='split')
10 loops, best of 3: 119 ms per loop
In [4]: pd.__version__, np.__version__
Out[4]: ('0.13.0-406-ga18e5e6', '1.8.0')
```
While this solves the main performance issue, using vbench this PR still appears to be a bit slower than v0.12 (~ 2 - 5%) but it's unclear where the slowdown is coming from.
@jreback do you still have a windows box / vm that you could test on?
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} | [] | closed | false | null | [] | null | 3 | 2014-01-28T10:19:13Z | 2014-06-19T09:31:18Z | 2014-01-28T15:42:35Z | CONTRIBUTOR | null | - Anaconda currently provides `pandas` v0.13, `numpy` 1.7.1 and `numexpr` 2.2.2 as the latest versions (#6056).
- There seem to exist rare but evil lurking bugs when using `numexpr` <= 2.2.2 and `numpy` >= 1.8.0 (#6119).
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https://api.github.com/repos/pandas-dev/pandas/issues/6139 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6139/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6139/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6139/events | https://github.com/pandas-dev/pandas/issues/6139 | 26,428,936 | MDU6SXNzdWUyNjQyODkzNg== | 6,139 | dataframe.eval() can't work with 'cash' column ? | {
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>>> df
cash sales net_pft
STK_ID RPT_Date
600141 20120331 7.8427 21.0882 0.6560
20120630 7.5140 46.9515 1.5912
20120930 7.7667 74.0364 2.5173
20121231 17.2350 96.1204 2.9458
20130331 13.3643 25.0869 0.7267
20130630 16.0832 56.6674 0.8088
20130930 16.7898 86.3745 1.3081
[7 rows x 3 columns]
>>> df.dtypes
cash float64
sales float64
net_pft float64
dtype: object
>>> df.eval('net_pft + sales')
STK_ID RPT_Date
600141 20120331 21.7442
20120630 48.5427
20120930 76.5537
20121231 99.0662
20130331 25.8136
20130630 57.4762
20130930 87.6826
dtype: float64
>>> df.eval('net_pft + cash')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "D:\Python2.7\lib\site-packages\pandas\core\frame.py", line 1845, in eval
return _eval(expr, **kwargs)
File "D:\Python2.7\lib\site-packages\pandas\computation\eval.py", line 208, in eval
ret = eng_inst.evaluate()
File "D:\Python2.7\lib\site-packages\pandas\computation\engines.py", line 50, in evaluate
self.pre_evaluate()
File "D:\Python2.7\lib\site-packages\pandas\computation\engines.py", line 33, in pre_evaluate
self.expr.check_name_clashes()
File "D:\Python2.7\lib\site-packages\pandas\computation\expr.py", line 799, in check_name_clashes
_check_disjoint_resolver_names(res_keys, lcl_keys, gbl_keys)
File "D:\Python2.7\lib\site-packages\pandas\computation\expr.py", line 41, in _check_disjoint_resolver_names
raise NameResolutionError(msg)
pandas.computation.common.NameResolutionError: resolvers and locals overlap on names ['cash']
>>> df.eval('sales + cash')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "D:\Python2.7\lib\site-packages\pandas\core\frame.py", line 1845, in eval
return _eval(expr, **kwargs)
File "D:\Python2.7\lib\site-packages\pandas\computation\eval.py", line 208, in eval
ret = eng_inst.evaluate()
File "D:\Python2.7\lib\site-packages\pandas\computation\engines.py", line 50, in evaluate
self.pre_evaluate()
File "D:\Python2.7\lib\site-packages\pandas\computation\engines.py", line 33, in pre_evaluate
self.expr.check_name_clashes()
File "D:\Python2.7\lib\site-packages\pandas\computation\expr.py", line 799, in check_name_clashes
_check_disjoint_resolver_names(res_keys, lcl_keys, gbl_keys)
File "D:\Python2.7\lib\site-packages\pandas\computation\expr.py", line 41, in _check_disjoint_resolver_names
raise NameResolutionError(msg)
pandas.computation.common.NameResolutionError: resolvers and locals overlap on names ['cash']
>>> pd.__version__
'0.13.0-203-g4518630'
>>>
>>> df=df.rename(columns={'cash':'cas'})
>>> df
cas sales net_pft
STK_ID RPT_Date
600141 20120331 7.8427 21.0882 0.6560
20120630 7.5140 46.9515 1.5912
20120930 7.7667 74.0364 2.5173
20121231 17.2350 96.1204 2.9458
20130331 13.3643 25.0869 0.7267
20130630 16.0832 56.6674 0.8088
20130930 16.7898 86.3745 1.3081
[7 rows x 3 columns]
>>> df.eval('sales + cas')
STK_ID RPT_Date
600141 20120331 28.9309
20120630 54.4655
20120930 81.8031
20121231 113.3554
20130331 38.4512
20130630 72.7506
20130930 103.1643
dtype: float64
>>> df.eval('net_pft + cas')
STK_ID RPT_Date
600141 20120331 8.4987
20120630 9.1052
20120930 10.2840
20121231 20.1808
20130331 14.0910
20130630 16.8920
20130930 18.0979
dtype: float64
>>>
```
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https://api.github.com/repos/pandas-dev/pandas/issues/6140 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6140/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6140/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6140/events | https://github.com/pandas-dev/pandas/issues/6140 | 26,430,593 | MDU6SXNzdWUyNjQzMDU5Mw== | 6,140 | DataFrame.from_records doesn't handle missing dates (None) | {
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} | 3 | 2014-01-28T10:54:15Z | 2014-01-29T00:59:07Z | 2014-01-28T15:39:16Z | CONTRIBUTOR | null | When you construct a DataFrame from a numpy recarray with datetime data and None for missing dates you get an error.
``` python
arrdata = [np.array([datetime.datetime(2005, 3, 1, 0, 0), None])]
dtypes = [('EXPIRY', '<M8[m]')]
recarray = np.core.records.fromarrays(arrdata, dtype=dtypes)
df = pd.DataFrame.from_records(recarray)
Traceback (most recent call last):
File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/ipython-1.1.0_1_ahl1-py2.7.egg/IPython/core/interactiveshell.py", line 2830, in run_code
exec code_obj in self.user_global_ns, self.user_ns
File "<ipython-input-33-ae01f48c3b82>", line 1, in <module>
df = pd.DataFrame.from_records(recarray)
File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.0_408_g464c1f9-py2.7-linux-x86_64.egg/pandas/core/frame.py", line 841, in from_records
columns)
File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.0_408_g464c1f9-py2.7-linux-x86_64.egg/pandas/core/frame.py", line 4473, in _arrays_to_mgr
return create_block_manager_from_arrays(arrays, arr_names, axes)
File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.0_408_g464c1f9-py2.7-linux-x86_64.egg/pandas/core/internals.py", line 3748, in create_block_manager_from_arrays
construction_error(len(arrays), arrays[0].shape[1:], axes, e)
File "/users/is/dbew/pyenvs/timeseries/lib/python2.7/site-packages/pandas-0.13.0_408_g464c1f9-py2.7-linux-x86_64.egg/pandas/core/internals.py", line 3720, in construction_error
passed,implied))
ValueError: Shape of passed values is (1,), indices imply (1, 2)
```
This is a regression from 0.11.0. Stepping through the code it looks the initial error is raised in tslib.cast_to_nanoseconds and then caught and re-raised in create_block_manager_from_arrays
Incidentally, construction does work from a simple array instead of a recarray:
``` python
pd.DataFrame(np.array([datetime.datetime(2005, 3, 1, 0, 0), None]))
Out[36]:
0
0 2005-03-01
1 NaT
[2 rows x 1 columns]
```
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} | 0 | 2014-01-28T13:19:13Z | 2014-06-14T09:36:10Z | 2014-01-28T13:19:18Z | CONTRIBUTOR | null | {
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} | 4 | 2014-01-28T13:20:58Z | 2014-06-19T22:42:08Z | 2014-01-28T15:39:16Z | CONTRIBUTOR | null | closes #6140
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} | 4 | 2014-01-28T14:54:02Z | 2017-11-24T10:38:16Z | 2017-11-24T10:37:38Z | CONTRIBUTOR | null | http://pandas.pydata.org/pandas-docs/vbench/
ideally have this run say once a week
need a few more categories (to cover the newer stuff)
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} | 1 | 2014-01-28T16:36:00Z | 2016-07-24T13:52:46Z | 2016-07-24T13:52:46Z | CONTRIBUTOR | null | When being `describe`d, series of `timedelta64` resulted in misformatted output:
Test example:
```
xx = pd.DataFrame({'t1':pd.date_range('2010-01-01 00:00:00', freq='T', periods=10),
't2':pd.date_range('2010-01-01 00:00:00', freq='2T', periods=10)})
xx['dt'] = xx['t2']-xx['t1']
print xx
```
Then `xx.dt.describe()` returns:
```
>>> xx.dt.describe()
count 10
mean 0 00:04:30
dtype: timedelta64[ns]
std 1.81659e+11
min 0 00:00:00
dtype: timedelta64[ns]
25% 135000000000 nanoseconds
50% 0 00:04:30
dtype: timedelta64[ns]
75% 405000000000 nanoseconds
max 0 00:09:00
dtype: timedelta64[ns]
Name: dt, dtype: object
```
Describe of datetime64 works fine:
```
>>> xx.t1.describe()
count 10
unique 10
first 2010-01-01 00:00:00
last 2010-01-01 00:09:00
top 2010-01-01 00:00:00
freq 1
Name: t1, dtype: object
```
Issue was found both in version 0.13.0 and master (0.13.0-417-g1ed5c3e).
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} | 1 | 2014-01-28T17:57:27Z | 2014-01-28T18:21:12Z | 2014-01-28T18:21:12Z | CONTRIBUTOR | null | The `unique` and `nunique` attributes are very useful in conjunction with series groupby operations. I used these extensively in previous versions of Pandas whenever I needed to get a list of unique values for each subgroup (or the number of unique values). This can be used, for example, to count the number of subjects in each treatment group (or get a list of the subject IDs for reporting):
```
data = pandas.DataFrame({
'subject_id': ('A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'),
'treatment': (0, 0, 0, 0, 0, 1, 1, 1, 0, 0),
})
print data.groupby('treatment').subject_id.apply(lambda x: x.nunique())
print data.groupby('treatment').subject_id.apply(lambda x: x.unique())
```
We'd get the following output:
```
treatment
0 7
1 3
dtype: int64
treatment
0 [A, B, C, D, E, I, J]
1 [F, G, H]
dtype: object
```
This is super-useful for generating summary statistics (e.g. N's) and debugging (e.g. tracking down which subjects are in which groups. In previous versions of Pandas, we could simply do:
```
print data.groupby('treatment').subject_id.nunique()
print data.groupby('treatment').subject_id.unique()
```
It would be nice to continue this. Is there a reason why `nunique` and `unique` can't be added to the whitelist?
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} | 3 | 2014-01-28T18:05:47Z | 2014-06-22T16:55:55Z | 2014-01-28T18:21:12Z | CONTRIBUTOR | null | Series.unique and Series.nunique are useful methods for querying the
unique elements (or number of unique elements) in each group when used
in conjunction with DataFrame.GroupBy. This commit addresses
closes pydata/pandas#6146.
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} | 5 | 2014-01-28T19:37:34Z | 2016-10-12T23:04:34Z | 2014-01-29T03:54:13Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/21414959/querying-a-single-row-dataframe-with-anded-conditionals/21415119#21415119
cc @cpcloud
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} | 6 | 2014-01-28T20:17:55Z | 2014-02-15T23:16:11Z | 2014-02-15T23:16:11Z | CONTRIBUTOR | null | see also: http://stackoverflow.com/questions/21415432/pandas-v0-13-0-setting-dataframe-values-of-type-datetime64ns
~~I believe this changed from 0.12.~~ was the same in 0.12
Boils down to if the row indexer is a null-slice (IOW all rows are selected), should
dtype conversion be done or not (as it current)
```
In [45]: df = pd.DataFrame({'date':pd.date_range('2000-01-01','2000-01-5'),'val' : np.arange(5)})
In [46]: df
Out[46]:
date val
0 2000-01-01 0
1 2000-01-02 1
2 2000-01-03 2
3 2000-01-04 3
4 2000-01-05 4
[5 rows x 2 columns]
In [47]: df['date'] = 0
In [48]: df
Out[48]:
date val
0 0 0
1 0 1
2 0 2
3 0 3
4 0 4
[5 rows x 2 columns]
In [49]: df = pd.DataFrame({'date':pd.date_range('2000-01-01','2000-01-5'),'val' : np.arange(5)})
In [53]: df.loc[:,'date'] = np.array([0])
In [54]: df
Out[54]:
date val
0 1970-01-01 0
1 1970-01-01 1
2 1970-01-01 2
3 1970-01-01 3
4 1970-01-01 4
[5 rows x 2 columns]
```
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} | 0 | 2014-01-28T21:23:53Z | 2014-01-29T00:48:24Z | 2014-01-29T00:48:24Z | CONTRIBUTOR | null | This seems to be significantly slower since 0.12:
```
import numpy as np; import pandas as pd; import datetime
s = pd.Series([np.random.choice(pd.date_range(datetime.datetime(1992,1,1,0,0,0),datetime.datetime(2014,1,1,0,0,0),freq='H')) for i in range(1200)])
# s is datetime64 column.
```
In 0.12:
```
In [12]: %timeit pd.DatetimeIndex(s)
10000 loops, best of 3: 15.4 µs per loop
```
pandas 0.13-dev:
```
In [13]: %timeit pd.DatetimeIndex(s)
100 loops, best of 3: 3.21 ms per loop
```
_Had originally thought this was normalize..._
cc @jreback
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We'll soon hit fundamental facts on errors in distributed systems.
#6144, hopefully.
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File "D:\Program Files\Miniconda3\lib\site-packages\pandas\tseries\offsets.py", line 13, in <module>
from pandas.tslib import Timestamp, OutOfBoundsDatetime
ImportError: cannot import name OutOfBoundsDatetime
## INSTALLED VERSIONS
Python: 3.3.3.final.0
OS: Windows
Release: 7
Processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.13.0
Cython: Not installed
Numpy: 1.7.1
Scipy: 0.13.2
statsmodels: Not installed
patsy: Not installed
scikits.timeseries: Not installed
dateutil: 2.1
pytz: 2013b
bottleneck: Not installed
PyTables: 3.0.0
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: Not installed
xlrd: 0.9.2
xlwt: Not installed
xlsxwriter: 0.5.2
sqlalchemy: Not installed
lxml: Not installed
bs4: Not installed
html5lib: Not installed
bigquery: Not installed
apiclient: Not installed
I've been really enjoying Pandas, by the way. Keep up the good work.
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} | 10 | 2014-01-29T00:24:40Z | 2014-01-29T06:19:54Z | 2014-01-29T03:24:59Z | NONE | null | Hi, it seems that string comparisons aren't supported in query() yet, so maybe this isn't a bug yet. Anyway, hopefully this behavior will be fixed for future editions of pandas.
``` python
import pandas as pd
import numexpr as ne
a = list('abcdef')
b = range(6)
df = pd.DataFrame({'X':pd.Series(a),'Y': pd.Series(b)})
df_Y = df.query('Y < 3') ## Works fine.
ne_works = ne.evaluate('"a" < "d"') ## ne_works == np.array([True])
df_X = df.query('X < "d"') ## RuntimeError: max recursion depth exceeded
```
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} | 2 | 2014-01-29T00:59:07Z | 2014-06-19T22:41:01Z | 2014-01-29T01:34:23Z | CONTRIBUTOR | null | related #6140, #6142
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https://api.github.com/repos/pandas-dev/pandas/issues/6157 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6157/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6157/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6157/events | https://github.com/pandas-dev/pandas/pull/6157 | 26,492,775 | MDExOlB1bGxSZXF1ZXN0MTE5NzUzMDU= | 6,157 | Updated docs to reflect a pagination bug that was fixed. Closes: issue #6068 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6158 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6158/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6158/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6158/events | https://github.com/pandas-dev/pandas/pull/6158 | 26,494,136 | MDExOlB1bGxSZXF1ZXN0MTE5NzYxMzM= | 6,158 | BUG: allow lex string comparisons | {
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} | 8 | 2014-01-29T02:25:33Z | 2014-06-16T01:40:36Z | 2014-01-29T03:24:59Z | MEMBER | null | closes #6155
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} | 2 | 2014-01-29T02:40:04Z | 2014-06-26T12:23:34Z | 2014-02-15T23:16:11Z | CONTRIBUTOR | null | previously the .loc would not necessarily coerce the dtype of the resultant series
closes #6149
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} | 4 | 2014-01-29T03:04:45Z | 2017-07-10T12:00:06Z | 2017-07-10T12:00:01Z | CONTRIBUTOR | null | When assigning to a column in a DataFrame, If I assign an existing column, the assignment works:
```
In [36]: df = pd.DataFrame({'a':[1,2,12], 'b':['a','b','a']})
In [37]: df.b = df.a
In [38]: print df
a b
0 1 1
1 2 2
2 12 12
[3 rows x 2 columns]
```
but assigning to a new column fails silently:
```
In [40]:df = pd.DataFrame({'a':[1,2,12], 'b':['a','b','a']})
In [41]:df.c = df.a
In [42]:print df
a b
0 1 a
1 2 b
2 12 a
[3 rows x 2 columns]
```
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} | 4 | 2014-01-29T03:11:27Z | 2018-07-06T20:21:03Z | 2017-07-10T21:09:42Z | CONTRIBUTOR | null | I would expect to be able to perform an `ewma` over each minor axis within each major axis:
```
In [52]:
wp = pd.Panel(np.random.randn(2, 4, 5), items=['Item1', 'Item2'],
minor_axis=pd.date_range('1/1/2000', periods=5),
major_axis=['A', 'B', 'C', 'D'])
In [57]:
wp
Out[57]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 4 (major_axis) x 5 (minor_axis)
Items axis: Item1 to Item2
Major_axis axis: A to D
Minor_axis axis: 2000-01-01 00:00:00 to 2000-01-05 00:00:00
In [56]:
pd.ewma(wp, com=2)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-56-bb436def494f> in <module>()
----> 1 pd.ewma(wp, com=2)
/usr/local/lib/python2.7/dist-packages/pandas-0.13.0_285_gfcfaa7d-py2.7-linux-x86_64.egg/pandas/stats/moments.pyc in ewma(arg, com, span, halflife, min_periods, freq, time_rule, adjust)
373 return result
374
--> 375 return_hook, values = _process_data_structure(arg)
376 output = np.apply_along_axis(_ewma, 0, values)
377 return return_hook(output)
/usr/local/lib/python2.7/dist-packages/pandas-0.13.0_285_gfcfaa7d-py2.7-linux-x86_64.egg/pandas/stats/moments.pyc in _process_data_structure(arg, kill_inf)
329 values = arg
330
--> 331 if not issubclass(values.dtype.type, float):
332 values = values.astype(float)
333
/usr/local/lib/python2.7/dist-packages/pandas-0.13.0_285_gfcfaa7d-py2.7-linux-x86_64.egg/pandas/core/generic.pyc in __getattr__(self, name)
1802 return self[name]
1803 raise AttributeError("'%s' object has no attribute '%s'" %
-> 1804 (type(self).__name__, name))
1805
1806 def __setattr__(self, name, value):
AttributeError: 'Panel' object has no attribute 'dtype'
```
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] | closed | false | null | [] | null | 5 | 2014-01-29T03:21:59Z | 2014-03-22T21:27:12Z | 2014-03-22T21:27:05Z | CONTRIBUTOR | null | See: http://pandas.pydata.org/pandas-docs/dev/indexing.html#advanced-indexing-with-hierarchical-index, where it states:
"The following does not work, and it’s not clear if it should or not:"
```
>>> df.ix[['bar', 'qux']]
```
That does work. See for example:
```
In [65]:
wp = pd.Panel(np.random.randn(2, 4, 5), items=['Item1', 'Item2'],
minor_axis=pd.date_range('1/1/2000', periods=5),
major_axis=['A', 'B', 'C', 'D'])
print wp.to_frame().ix[["A", "B"]]
Item1 Item2
major minor
A 2000-01-01 -1.938712 0.404485
2000-01-02 1.180799 0.124615
2000-01-03 -0.304632 -1.238341
2000-01-04 1.215016 1.117624
2000-01-05 -1.274482 0.348929
B 2000-01-01 0.622100 0.506472
2000-01-02 0.108105 1.562588
2000-01-03 -0.277282 -1.766601
2000-01-04 -0.313015 0.004508
2000-01-05 -0.553452 -0.600046
[10 rows x 2 columns]
```
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} | 6 | 2014-01-29T03:26:59Z | 2014-06-21T19:28:05Z | 2014-01-29T03:54:13Z | MEMBER | null | closes #6148
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} | 11 | 2014-01-29T06:49:35Z | 2014-06-20T19:03:14Z | 2014-01-29T20:26:50Z | CONTRIBUTOR | null | fixes #4110 #4304
#4304 rebased and passes travis.
cc @y-p
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} | [] | closed | false | null | [] | null | 1 | 2014-01-29T08:04:29Z | 2014-01-29T11:31:44Z | 2014-01-29T11:31:44Z | NONE | null | `.ix[]` doesn't handle MultiIndex very well, for example here is the questions from stackoverflow:
http://stackoverflow.com/questions/15520408/how-to-index-into-a-pandas-multindex-with-ix
http://stackoverflow.com/questions/21234279/selection-with-hierarchical-index-getting-subset-of-the-dataframe
They want to select rows that match some criteria for every level in the index. Can we add an indexer for this, for example, here is my rough implement:
```
import pandas as pd
import numpy as np
from pandas.util.testing import choice
N = 40
np.random.seed(0)
level0 = choice(["A","B","C","D","E"], N)
level1 = choice(["test%d" % i for i in range(1, 4)], N)
level2 = choice(range(5), N)
labels = zip(level0, level1, level2)
values = np.arange(len(labels)*2).reshape(-1, 2)
class MultiIndexIndexer(object):
def __init__(self, obj, name):
self.obj = obj
self.ndim = obj.ndim
self.name = name
def __getitem__(self, key):
idx = self.obj.index
mask = np.ones(len(idx), dtype=bool)
for i, values in enumerate(key):
level = idx.levels[i]
label = idx.labels[i]
if values is None:
continue
if isinstance(values, slice):
if values == slice(None):
continue
level = idx.levels[i]
if values.start is not None:
start = level.get_loc(values.start)
else:
start = 0
if values.stop is not None:
stop = level.get_loc(values.stop) + 1
else:
stop = None
values = level[start:stop:values.step]
if np.isscalar(values):
values = [values]
mask[mask] &= level.isin(values).take(np.compress(mask, label))
return self.obj.ix[mask]
pd.core.generic.NDFrame._create_indexer("ms", MultiIndexIndexer)
print df.ms[["A","C"], "test2":"test3", :2]
```
here is the output:
```
x y
A test2 0 34 35
1 52 53
test3 2 42 43
2 46 47
C test2 0 14 15
[5 rows x 2 columns]
```
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} | 15 | 2014-01-29T08:45:05Z | 2014-06-24T10:39:07Z | 2014-04-06T16:59:32Z | CONTRIBUTOR | null | When storing long strings in hdf5, they get truncated at 64bytes when converted back to strings. The fix computes the itemsize of the strings and uses it before converting to strings. It would be possible to use the attribute information of the table, but that would require changes in the calling code.
Test that failed without the patch is included.
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} | 2 | 2014-01-29T10:59:31Z | 2014-06-16T09:44:30Z | 2014-01-29T15:39:38Z | CONTRIBUTOR | null | Appending to an existing table with a multiindex failed as data_columns is set to [] but compared with None
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] | closed | false | null | [] | null | 7 | 2014-01-29T13:42:18Z | 2014-09-18T17:53:08Z | 2014-01-29T14:36:31Z | NONE | null | Previously (version 0.11) it was possible to generate a boolean Series, and use that to index a numpy array. Version 0.13.0 breaks this behaviour and raises "IndexError: unsupported iterator index"
``` python
import numpy as np
import pandas as pd
rng = np.arange(5)
rng[rng > 2] # works as expected
>>> array([3, 4])
b = pd.Series(rng > 2)
rng[b] # doesn't work anymore
>>> IndexError: unsupported iterator index
```
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} | 6 | 2014-01-29T15:24:50Z | 2014-01-31T13:32:00Z | 2014-01-31T13:32:00Z | CONTRIBUTOR | null | I'm running in what seems to be a bug.
I'm using pandas version '0.13.0rc1-29-ga0a527b' from github, python 3.3 on a linux Mint 15 64 bits.
Here's a minimal example that fails:
``` python
import numpy as np
import pandas as pd
index = pd.MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']],
labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['foo_name', 'bar_name'])
df_mi = pd.DataFrame(np.random.randn(10, 3), index=index,
columns=['A', 'B', 'C'])
with pd.get_store('minimal_io.h5') as store:
store.put('df_mi', df_mi, format='table')
with pd.get_store('minimal_io.h5') as store:
ixs = store.select('df_mi', "columns=['A']")
```
And here is the error message:
``` python
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-32-005cf4e724e0> in <module>()
17
18 with pd.get_store('minimal_io.h5') as store:
---> 19 ixs = store.select('df_mi', "columns=['A']")
/home/guillaume/python3/lib/python3.3/site-packages/pandas-0.13.0rc1_29_ga0a527b-py3.3-linux-x86_64.egg/pandas/io/pytables.py in select(self, key, where, start, stop, columns, iterator, chunksize, auto_close, **kwargs)
622
623 return TableIterator(self, func, nrows=s.nrows, start=start, stop=stop,
--> 624 auto_close=auto_close).get_values()
625
626 def select_as_coordinates(
/home/guillaume/python3/lib/python3.3/site-packages/pandas-0.13.0rc1_29_ga0a527b-py3.3-linux-x86_64.egg/pandas/io/pytables.py in get_values(self)
1252
1253 def get_values(self):
-> 1254 results = self.func(self.start, self.stop)
1255 self.close()
1256 return results
/home/guillaume/python3/lib/python3.3/site-packages/pandas-0.13.0rc1_29_ga0a527b-py3.3-linux-x86_64.egg/pandas/io/pytables.py in func(_start, _stop)
611 def func(_start, _stop):
612 return s.read(where=where, start=_start, stop=_stop,
--> 613 columns=columns, **kwargs)
614
615 if iterator or chunksize is not None:
/home/guillaume/python3/lib/python3.3/site-packages/pandas-0.13.0rc1_29_ga0a527b-py3.3-linux-x86_64.egg/pandas/io/pytables.py in read(self, columns, **kwargs)
3796 df = super(AppendableMultiFrameTable, self).read(
3797 columns=columns, **kwargs)
-> 3798 df = df.set_index(self.levels)
3799
3800 # remove names for 'level_%d'
/home/guillaume/python3/lib/python3.3/site-packages/pandas-0.13.0rc1_29_ga0a527b-py3.3-linux-x86_64.egg/pandas/core/frame.py in set_index(self, keys, drop, append, inplace, verify_integrity)
2327 names.append(None)
2328 else:
-> 2329 level = frame[col].values
2330 names.append(col)
2331 if drop:
/home/guillaume/python3/lib/python3.3/site-packages/pandas-0.13.0rc1_29_ga0a527b-py3.3-linux-x86_64.egg/pandas/core/frame.py in __getitem__(self, key)
1626 return self._getitem_multilevel(key)
1627 else:
-> 1628 return self._getitem_column(key)
1629
1630 def _getitem_column(self, key):
/home/guillaume/python3/lib/python3.3/site-packages/pandas-0.13.0rc1_29_ga0a527b-py3.3-linux-x86_64.egg/pandas/core/frame.py in _getitem_column(self, key)
1633 # get column
1634 if self.columns.is_unique:
-> 1635 return self._get_item_cache(key)
1636
1637 # duplicate columns & possible reduce dimensionaility
/home/guillaume/python3/lib/python3.3/site-packages/pandas-0.13.0rc1_29_ga0a527b-py3.3-linux-x86_64.egg/pandas/core/generic.py in _get_item_cache(self, item)
976 res = cache.get(item)
977 if res is None:
--> 978 values = self._data.get(item)
979 res = self._box_item_values(item, values)
980 cache[item] = res
/home/guillaume/python3/lib/python3.3/site-packages/pandas-0.13.0rc1_29_ga0a527b-py3.3-linux-x86_64.egg/pandas/core/internals.py in get(self, item)
2738 return self.get_for_nan_indexer(indexer)
2739
-> 2740 _, block = self._find_block(item)
2741 return block.get(item)
2742 else:
/home/guillaume/python3/lib/python3.3/site-packages/pandas-0.13.0rc1_29_ga0a527b-py3.3-linux-x86_64.egg/pandas/core/internals.py in _find_block(self, item)
3049
3050 def _find_block(self, item):
-> 3051 self._check_have(item)
3052 for i, block in enumerate(self.blocks):
3053 if item in block:
/home/guillaume/python3/lib/python3.3/site-packages/pandas-0.13.0rc1_29_ga0a527b-py3.3-linux-x86_64.egg/pandas/core/internals.py in _check_have(self, item)
3056 def _check_have(self, item):
3057 if item not in self.items:
-> 3058 raise KeyError('no item named %s' % com.pprint_thing(item))
3059
3060 def reindex_axis(self, new_axis, indexer=None, method=None, axis=0,
KeyError: 'no item named foo_name'
> /home/guillaume/python3/lib/python3.3/site-packages/pandas-0.13.0rc1_29_ga0a527b-py3.3-linux-x86_64.egg/pandas/core/internals.py(3058)_check_have()
3057 if item not in self.items:
-> 3058 raise KeyError('no item named %s' % com.pprint_thing(item))
3059
```
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} | 26 | 2014-01-29T15:47:23Z | 2017-07-24T04:02:34Z | 2015-01-18T21:40:11Z | CONTRIBUTOR | null | closes #6051
Bug: `mangle_dupe_cols` cannot work while the header is a list.
Originally, `has_mi_columns` will be set as 1 while the header is a list. And the `mangle_dupe_cols` things would not work when `has_mi_columns` == 1.
This pull request is to
1. for the list with single element, for example [0], the `has_mi_columns` will be set as 0, and the header will do the original `mangle_dupe_cols` things as the header is a single integer.
2. for the list with more than one element, append the sequence number in the last element of the duplicated multi-column
For example
```
Male,Male,Male,Male,Male,Female,Female
A,B,B,B,C,D,D
1,2,3,4,5,6,7
1,2,3,4,5,6,7
1,2,3,4,5,6,7
1,2,3,4,5,6,7
1,2,3,4,5,6,7
1,2,3,4,5,6,7
```
would be converted to
```
In [2]: pd.read_csv('woo.csv', header=[0,1])
Out[2]:
Male Female
A B B.1 B.2 C D D.1
0 1 2 3 4 5 6 7
1 1 2 3 4 5 6 7
2 1 2 3 4 5 6 7
3 1 2 3 4 5 6 7
4 1 2 3 4 5 6 7
5 1 2 3 4 5 6 7
[6 rows x 7 columns]
```
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} | 20 | 2014-01-29T15:56:50Z | 2014-01-29T19:48:08Z | 2014-01-29T17:23:04Z | CONTRIBUTOR | null | Why would assigning an entire column to an array of values work differently with numbers vs strings?
**Assigning numeric values:**
``` python
>>> df = pd.DataFrame(columns=['x', 'y'])
>>> df['x'] = [1, 2]
>>> df
x y
0 1 NaN
1 2 NaN
```
**Assigning string values:**
``` python
>>> df = pd.DataFrame(columns=['x', 'y'])
>>> df['x'] = ['1', '2']
ValueError: could not broadcast input array from shape (2) into shape (0)
```
Btw according to latest docs `.loc` can append, but can it append more than one value at once?
**Setting multiple via `.loc`:**
``` python
>>> df = pd.DataFrame(columns=['x', 'y'])
>>> df.loc[:, 'x'] = [1, 2]
>>> df
Empty DataFrame
Columns: [x, y]
Index: []
>>> df.loc[[0, 1], 'x'] = [1, 2]
>>> df
Empty DataFrame
Columns: [x, y]
Index: []
>>> df.loc[0:2, 'x'] = [1, 2]
>>> df
Empty DataFrame
Columns: [x, y]
Index: []
```
**Setting single via `.loc`:** (this works ofc)
``` python
>>> df = pd.DataFrame(columns=['x', 'y'])
>>> df.loc[0, 'x'] = 1
>>> df
x y
0 1 NaN
```
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} | 0 | 2014-01-29T16:48:25Z | 2014-06-24T03:11:45Z | 2014-01-29T17:23:04Z | CONTRIBUTOR | null | closes #6171
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} | 2 | 2014-01-29T17:09:52Z | 2018-11-23T02:38:12Z | 2018-11-23T02:38:12Z | CONTRIBUTOR | null | According to the latest v0.13 docs `.loc` can append, but can it (or rather, should it?) append more than one value at once?
**Setting multiple via .loc:**
``` python
>>> df = pd.DataFrame(columns=['x', 'y'])
>>> df.loc[:, 'x'] = [1, 2]
>>> df
Empty DataFrame
Columns: [x, y]
Index: []
>>> df.loc[[0, 1], 'x'] = [1, 2]
>>> df
Empty DataFrame
Columns: [x, y]
Index: []
>>> df.loc[0:2, 'x'] = [1, 2]
>>> df
Empty DataFrame
Columns: [x, y]
Index: []
```
**Setting single via .loc:** (works as intended)
``` python
>>> df = pd.DataFrame(columns=['x', 'y'])
>>> df.loc[0, 'x'] = 1
>>> df
x y
0 1 NaN
```
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} | 0 | 2014-01-29T17:31:14Z | 2014-06-22T03:44:52Z | 2014-01-29T17:31:38Z | NONE | null | More fixes to @network to handle the incredibly unreliable yahoo servers.
This should do it.
The mind boggles that a billion-dollar company can have a QoS this bad.
N.B. the default socket timeout setting.
It's not required for this because I believe travis has some firewall rules that
timeout net connections, but it's nicer when running locally where connections
can hang for much longer. 15 seconds is very generous so I hope it's not disruptive.
cc @jreback, @jtratner
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} | 11 | 2014-01-29T17:43:36Z | 2014-03-09T16:07:06Z | 2014-03-09T16:06:03Z | NONE | null | An example of an exception that uses `assert_`:
```
============================================================================
FAILURE: test_class_ops (pandas.tseries.tests.test_timeseries.TestTimestamp)
----------------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Python33-AMD64\Lib\unittest\case.py", line 384, in _executeTestPart
function()
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\33\pandas\tseries\tests\test_timeseries.py", line 2513, in test_class_ops
compare(Timestamp.now('UTC'),datetime.now(pytz.timezone('UTC')))
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\33\pandas\tseries\tests\test_timeseries.py", line 2510, in compare
self.assert_(int(Timestamp(x).value/1e9) == int(Timestamp(y).value/1e9))
File "c:\Python33-AMD64\Lib\unittest\case.py", line 1140, in deprecated_func
return original_func(*args, **kwargs)
File "c:\Python33-AMD64\Lib\unittest\case.py", line 520, in assertTrue
raise self.failureException(msg)
AssertionError: False is not true
```
note the line:
```
self.assert_(int(Timestamp(x).value/1e9) == int(Timestamp(y).value/1e9))
```
if this had been `assertEqual`, we'd have seen what the value were
and been able to debug easier (now fixed). (though, It should be obvious why that test is unreliable).
There are lots of these across the tests.
If someone steps up to do this, do a little bit at a time.
a PR that touches 200 tests will defy review and be stuck forever
in PR limbo.
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} | 1 | 2014-01-29T20:10:38Z | 2014-06-18T17:57:58Z | 2014-02-04T08:52:23Z | NONE | null | cython 20 is out, and complains about syntax.
Holding off till 0.14, on general principle.
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} | 10 | 2014-01-29T20:47:11Z | 2014-06-13T12:19:31Z | 2014-02-09T13:31:35Z | CONTRIBUTOR | null | select_as_multiple/column/coordinate and remove behaves strange on combinations of where clauses and start/stop keyword arguments so I started fixing this.
I changed select_as_multiple to select coordinates inside the TableIterator, so it will work on large tables. Moreover select_as_multiple always throws a KeyError when either a key or the selector is not available in the file.
Closes #4835.
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} | 2 | 2014-01-29T23:15:41Z | 2016-10-12T23:04:34Z | 2014-02-15T22:05:03Z | CONTRIBUTOR | null | #6149
exposed this bug; going to update the tests (and i'll link there that show it)
initially only showed up on py2.6...but it is real
the signs of the inf under integer division are wrong.
for the most part the np.nan are preserved, not sure if this is right or not
```
In [1]: Series([np.nan,1,-1])/0
Out[1]:
0 inf
1 inf
2 inf
dtype: float64
In [2]: Series([np.nan,1,-1])/0.
Out[2]:
0 NaN
1 inf
2 -inf
dtype: float64
```
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~/src/pandas/doc/ λ ./make.py clean ;time ./make.py ;
993.28s
```
```
~/src/pandas/doc/ λ ./make.py clean ;time ./make.py --no-api ;
255.12s
```
```
~/src/pandas/doc/ λ ./make.py clean ;time ./make.py --single indexing ;
8.08s
```
@jorisvandenbossche , please review.
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} | 0 | 2014-01-30T01:38:26Z | 2014-06-16T19:14:53Z | 2014-01-30T01:38:33Z | NONE | null | I wrote it, so I guess others might find it useful as well.
It's actually a single script for setting up a full py2 venv for pandas
with all deps as well.
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} | [] | closed | false | null | [] | null | 6 | 2014-01-30T01:59:23Z | 2014-04-15T18:02:00Z | 2014-01-30T02:04:55Z | CONTRIBUTOR | null | Is there a simple way to make the `Index` in a `DataFrame` into columns in a while still keeping the index?
Currently I do the following and am wondering if this is the best I can do:
```
df.reset_index().set_index(df.index.names, drop=False)
```
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} | [] | closed | false | null | [] | null | 1 | 2014-01-30T02:02:04Z | 2014-01-30T02:03:13Z | 2014-01-30T02:03:13Z | CONTRIBUTOR | null | If I have a DataFrame with a MultiIndex, is there any way to slice on the second level?
If I only want one value, I can use `xs`.
For example:
```
wp = pd.Panel(np.random.randn(2, 4, 5), items=['Item1', 'Item2'],
minor_axis=pd.date_range('1/1/2000', periods=5),
major_axis=['A', 'B', 'C', 'D'])
ff= wp.to_frame()
```
```
ff.xs("2000-01-01", level="minor", drop_level=False)
```
But what if I want to get all values after "2000-01-02"?
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} | [] | closed | false | null | [] | null | 1 | 2014-01-30T03:32:01Z | 2014-01-30T14:30:36Z | 2014-01-30T14:30:31Z | CONTRIBUTOR | null | I can't find this reported anywhere (and I can't reproduce on my dev versions). Is this known? Currently affects canopy users.
https://stackoverflow.com/questions/20564536/error-when-plotting-dataframe-containing-nan-with-pandas-0-12-0-and-matplotlib-1
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} | 1 | 2014-01-30T04:44:14Z | 2014-07-16T08:50:50Z | 2014-01-31T14:53:15Z | CONTRIBUTOR | null | Fixes #6081 issues in test_reshape.py -- most of `test_custom_var_and_value_name` wasn't being executed (hiding some bugs in the test code too) because it was being shadowed by another method of the same name.
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https://api.github.com/repos/pandas-dev/pandas/issues/6185 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6185/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6185/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6185/events | https://github.com/pandas-dev/pandas/pull/6185 | 26,575,063 | MDExOlB1bGxSZXF1ZXN0MTIwMjMxNDI= | 6,185 | BLD: ipython_directive, handle non-ascii execution results | {
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} | 9 | 2014-01-30T04:57:14Z | 2014-07-16T08:50:51Z | 2014-01-31T02:02:18Z | NONE | null | @jorisvandenbossche I think this is a clean solution to the non-utf8
output problem we discussed, specifically the example in io.rst.
This PR should produce the right output on windows and be more amenable
to py3 compat work too. If you confirm that it's good I feel this is clean enough
to try and get upstream, completing the upstreaming effort.
related #5530, https://github.com/pydata/pandas/pull/5925#issuecomment-32407859
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} | 7 | 2014-01-30T09:26:35Z | 2014-04-21T05:45:10Z | 2014-04-21T05:45:10Z | NONE | null | In the following snippet the two to_csv calls differs only in the index argument. Although the output header in the first case is (index), 'X', 'Y' but in the second case is 'A', 'B'. The documentation didn't suggest that sort of difference.
```
import pandas as pd
df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
df.to_csv('testtocsv1.csv', header=['X', 'Y'])
df.to_csv('testtocsv2.csv', index=False, header=['X', 'Y'])
```
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} | 13 | 2014-01-30T13:40:58Z | 2014-03-14T11:38:01Z | 2014-03-14T11:38:01Z | CONTRIBUTOR | null | One of the projects in my workplace that uses `pandas` a lot has hit a performance regression (~50%) when I was trying to upgrade from `0.12.0` to `0.13.0`. I've double-checked with
``` python
In [1]: pd.__version__
Out[1]: '0.13.0-470-gf0c4953'
```
and the regression is still there. I've skimmed through recent tickets but didn't find anything open about performance, so I'm filing a new one. `0.12.0` package was provided by `conda`. We don't do heavy math with `pandas`, just usual data aggregation/filtering, but I've included `bottleneck` & `numexpr` just to be sure. `0.13.0+` was built into a clone of `0.12.0` environment with `python setup.py develop`.
Here's a list of top pandas modules by total time spent as reported by `runsnake`:
```
filename 0.12 0.13 diff
tseries/index.py 11.7 13.4 14.53%
core/common.py 14.6 25 71.23%
core/frame.py 15.4 15.2 -1.30%
core/series.py 22.5 35.7 58.67%
core/index.py 28.3 44.9 58.66%
core/internals.py 29.6 53 79.05%
core/generic.py 5.5 24 336.36%
```
I've put profiles restricted to `pandas` modules to gdrive [here (0.12.0)](https://drive.google.com/file/d/0B_ZtkLy4UXstRk1ZalNROTZsOU0/edit?usp=sharing) and [here (0.13.0+)](https://drive.google.com/file/d/0B_ZtkLy4UXstcG42akoxdHcybTQ/edit?usp=sharing).
While most of this is explicable by, for example, `Series` having changed to subclass `NDArray` instead of `np.ndarray`, a performance hit like this makes it virtually impossible to talk my colleagues into upgrading.
Cheers
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} | 14 | 2014-01-30T14:54:52Z | 2014-07-16T08:50:53Z | 2014-02-18T20:07:16Z | CONTRIBUTOR | null | Unfortunately I won't be able to use this in production until 2020 when we require 0.14, but this would make some things a bit easier for projects that use pandas as a library.
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} | 5 | 2014-01-30T15:17:49Z | 2017-08-27T06:21:55Z | 2017-07-11T10:35:28Z | CONTRIBUTOR | null | Shouldn't this work?
```
>>> pd.version.version
'0.13.0-472-g9109812'
>>> df = pd.DataFrame(np.random.random((10,4)))
>>> df = df.drop(df[0] > .5)
>>> df.drop(df[0] < .2)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-23-5df7f56de272> in <module>()
----> 1 df.drop(df[0] > .5)
/usr/local/lib/python2.7/dist-packages/pandas-0.13.0_472_g9109812-py2.7-linux-x86_64.egg/pandas/core/generic.pyc in drop(self, labels, axis, level, inplace, **kwargs)
1397 new_axis = axis.drop(labels, level=level)
1398 else:
-> 1399 new_axis = axis.drop(labels)
1400 dropped = self.reindex(**{axis_name: new_axis})
1401 try:
/usr/local/lib/python2.7/dist-packages/pandas-0.13.0_472_g9109812-py2.7-linux-x86_64.egg/pandas/core/index.pyc in drop(self, labels)
1621 mask = indexer == -1
1622 if mask.any():
-> 1623 raise ValueError('labels %s not contained in axis' % labels[mask])
1624 return self.delete(indexer)
1625
ValueError: labels [False False True True True True True False] not contained in axis
```
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} | 5 | 2014-01-30T16:25:49Z | 2014-01-31T02:05:23Z | 2014-01-31T02:05:16Z | CONTRIBUTOR | null | The link to the 0.13 documentation on pandas.pydata.org points to [whatsnew](http://pandas.pydata.org/pandas-docs/stable/whatsnew.html) instead of the [docs](http://pandas.pydata.org/pandas-docs/stable/index.html)
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https://api.github.com/repos/pandas-dev/pandas/issues/6192 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6192/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6192/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6192/events | https://github.com/pandas-dev/pandas/pull/6192 | 26,637,457 | MDExOlB1bGxSZXF1ZXN0MTIwNTYzNjk= | 6,192 | BLD/TST: @network, inspect formatted expection rather then str(e) | {
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} | [] | closed | false | null | [] | null | 2 | 2014-01-30T21:46:29Z | 2014-06-26T02:15:16Z | 2014-01-30T23:40:28Z | NONE | null | @jreback, here's the next attempt.
I guess the output we see from pdb and str(e) are not the same thing,
so this compares the strings against a properly formatted exception
(I ommited the traceback, shouldn't be relevent).
I'l rerun this on travis 15 times or so before merging.
https://github.com/pydata/pandas/issues/6144
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https://api.github.com/repos/pandas-dev/pandas/issues/6193 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6193/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6193/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6193/events | https://github.com/pandas-dev/pandas/pull/6193 | 26,638,012 | MDExOlB1bGxSZXF1ZXN0MTIwNTY3MTM= | 6,193 | DOC: contributing.rst, document fast doc building | {
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} | [] | closed | false | null | [] | null | 2 | 2014-01-30T21:54:28Z | 2014-06-26T23:16:21Z | 2014-01-30T21:54:38Z | NONE | null | @jreback, don't say you didn't know about this feature later.
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} | 5 | 2014-01-30T21:54:32Z | 2016-07-29T00:08:33Z | 2016-07-29T00:08:33Z | CONTRIBUTOR | null | ``` python
AttributeError: 'Float64Index' object has no attribute 'dropna'
```
Any reason why it could not have a dropna()?
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} | [] | closed | false | null | [] | null | 1 | 2014-01-30T22:55:57Z | 2014-01-31T00:47:11Z | 2014-01-31T00:47:11Z | NONE | null | This is similar to the question at http://stackoverflow.com/questions/18071222/working-with-set-index-in-pandas-dataframe, but I think this one may actually be a bug.
```
import pandas as pd
import numpy as np
d = {'ID': pd.Series(['x']*3 + ['y']*3,index = range(6)),
'Count': pd.Series([1,2,1,1,1,1], index = range(6)),
'Value_type': pd.Series(['foo','foo','bar','foo','bar','baz'], index = range(6)),
'Value': pd.Series(range(1,7),index = range(6))}
df = pd.DataFrame(d)
stacked = df.set_index(['ID','Count','Value_type'])
print stacked
```
Running this code yields the following:
```
Value
ID Count Value_type
x 1 foo 1
2 foo 2
1 bar 3
y 1 foo 4
bar 5
baz 6
```
However, I thought every level of the row index except for the last should be grouped, which would yield this.
```
Value
ID Count Value_type
x 1 foo 1
bar 3
2 foo 2
y 1 foo 4
bar 5
baz 6
```
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} | [] | closed | false | null | [] | null | 3 | 2014-01-31T11:21:08Z | 2014-01-31T13:33:55Z | 2014-01-31T13:07:29Z | CONTRIBUTOR | null | Raise a detailed error when a `columns` argument is passed through 'where' to
select a multiIndexed Dataframe from an HDF store. Wrote a test
showcasing the bug
```
modified: pandas/io/pytables.py
modified: pandas/io/tests/test_pytables.py
```
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