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In [24]: ts2.index
Out[24]:
<class 'pandas.tseries.index.DatetimeIndex'>
freq: <1 Day>, timezone: US/Central
[1990-01-01 00:00:00-06:00, ..., 2000-01-01 00:00:00-06:00]
length: 3653
In [25]: ts2.take(range(5)).index
Out[25]:
DatetimeIndex([1990-01-01 00:00:00, 1990-01-02 00:00:00, 1990-01-03 00:00:00,
1990-01-04 00:00:00, 1990-01-05 00:00:00], dtype=datetime64[us])
```
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} | 4 | "2012-04-22T11:56:12Z" | "2012-04-22T14:59:43Z" | "2012-04-22T14:48:56Z" | NONE | null | > > > from pandas import *
> > > Traceback (most recent call last):
> > > File "<stdin>", line 1, in <module>
> > > File "/usr/local/lib/python2.7/dist-packages/pandas-0.8.0.dev_be5cd06-py2.7-linux-x86_64.egg/pandas/**init**.py", line 22, in <module>
> > > from pandas.core.api import *
> > > File "/usr/local/lib/python2.7/dist-packages/pandas-0.8.0.dev_be5cd06-py2.7-linux-x86_64.egg/pandas/core/api.py", line 6, in <module>
> > > from pandas.core.datetools import DateOffset, to_datetime
> > > File "/usr/local/lib/python2.7/dist-packages/pandas-0.8.0.dev_be5cd06-py2.7-linux-x86_64.egg/pandas/core/datetools.py", line 3, in <module>
> > > from pandas.tseries.tools import *
> > > ImportError: No module named tseries.tools
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} | 2 | "2012-04-22T20:52:00Z" | "2012-05-08T14:51:43Z" | "2012-05-08T14:51:43Z" | MEMBER | null | Zipping and leads to a "wrong number of columns" message
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https://api.github.com/repos/pandas-dev/pandas/issues/1106 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1106/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1106/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1106/events | https://github.com/pandas-dev/pandas/issues/1106 | 4,232,468 | MDU6SXNzdWU0MjMyNDY4 | 1,106 | Bug? numpy.dtype is not sortable. | {
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} | [] | closed | false | null | [] | null | 1 | "2012-04-22T22:25:06Z" | "2012-05-16T21:06:23Z" | "2012-05-16T21:06:23Z" | CONTRIBUTOR | null | I find this when I am writing the pull request #1098. As it is loosely related to the pull request, I am reporting here as a separate issue.
It looks like dtype does not support sorting:
``` python
>>> from numpy import dtype
>>> sorted([dtype('bool'), dtype('float64'), dtype('complex64'),
... dtype('float64'), dtype('object')])
[dtype('bool'),
dtype('float64'),
dtype('complex64'),
dtype('float64'),
dtype('object')]
```
`core.internals._consolidate` relies sortability of `dtype`. Probably it is better to sort using `dtype.name` as I did in bae81555be2c16cb191b5c9f2b9f9f305eea53d5?
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} | 4 | "2012-04-23T07:29:07Z" | "2012-04-23T15:59:59Z" | "2012-04-23T15:54:32Z" | CONTRIBUTOR | null | ```
(pandas)[1837][n] nosetests pandas.tseries.tests.test_resample
*** glibc detected *** ~/.virtualenvs/pandas/bin/python: double free or corruption (!prev): 0x0000000003d319f0 ***
======= Backtrace: =========
/lib64/libc.so.6[0x3d7047245f]
/lib64/libc.so.6(cfree+0x4b)[0x3d704728bb]
~/.virtualenvs/pandas/lib/python2.7/site-packages/numpy/core/multiarray.so[0x2adc0147126f]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0[0x2adbf9e40083]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyEval_EvalFrameEx+0x5ef9)[0x2adbf9eb00b9]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyEval_EvalFrameEx+0x5ed3)[0x2adbf9eb0093]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyEval_EvalCodeEx+0x8d2)[0x2adbf9eb1722]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyEval_EvalFrameEx+0x54f6)[0x2adbf9eaf6b6]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyEval_EvalCodeEx+0x8d2)[0x2adbf9eb1722]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0[0x2adbf9e41522]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyObject_Call+0x68)[0x2adbf9e1ad18]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyEval_EvalFrameEx+0xd29)[0x2adbf9eaaee9]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyEval_EvalCodeEx+0x8d2)[0x2adbf9eb1722]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyEval_EvalFrameEx+0x54f6)[0x2adbf9eaf6b6]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyEval_EvalFrameEx+0x5ed3)[0x2adbf9eb0093]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyEval_EvalCodeEx+0x8d2)[0x2adbf9eb1722]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0[0x2adbf9e41522]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyObject_Call+0x68)[0x2adbf9e1ad18]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyEval_EvalFrameEx+0xd29)[0x2adbf9eaaee9]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyEval_EvalCodeEx+0x8d2)[0x2adbf9eb1722]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0[0x2adbf9e41522]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyObject_Call+0x68)[0x2adbf9e1ad18]
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~/.virtualenvs/pandas/lib/libpython2.7.so.1.0[0x2adbf9e7115a]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyObject_Call+0x68)[0x2adbf9e1ad18]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyEval_EvalFrameEx+0x1268)[0x2adbf9eab428]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyEval_EvalFrameEx+0x5ed3)[0x2adbf9eb0093]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyEval_EvalCodeEx+0x8d2)[0x2adbf9eb1722]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0[0x2adbf9e41522]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyObject_Call+0x68)[0x2adbf9e1ad18]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyEval_EvalFrameEx+0xd29)[0x2adbf9eaaee9]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyEval_EvalCodeEx+0x8d2)[0x2adbf9eb1722]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0[0x2adbf9e41522]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyObject_Call+0x68)[0x2adbf9e1ad18]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0[0x2adbf9e2918d]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyObject_Call+0x68)[0x2adbf9e1ad18]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0[0x2adbf9e7115a]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyObject_Call+0x68)[0x2adbf9e1ad18]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyEval_EvalFrameEx+0x1268)[0x2adbf9eab428]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyEval_EvalCodeEx+0x8d2)[0x2adbf9eb1722]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0[0x2adbf9e41522]
~/.virtualenvs/pandas/lib/libpython2.7.so.1.0(PyObject_Call+0x68)[0x2adbf9e1ad18]
~/.virtualenvs/pandas/lib/libpyzsh: abort nosetests pandas.tseries.tests.test_resample
```
``` python
In [2]: pandas.__version__
Out[2]: '0.8.0.dev-ce8629a'
```
```
LSB Version: :core-4.0-amd64:core-4.0-ia32:core-4.0-noarch:graphics-4.0-amd64:graphics-4.0-ia32:graphics-4.0-noarch:printing-4.0-amd64:printing-4.0-ia32:printing-4.0-noarch
Distributor ID: RedHatEnterpriseServer
Description: Red Hat Enterprise Linux Server release 5.6 (Tikanga)
Release: 5.6
Codename: Tikanga
```
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see http://pandas.pydata.org/pandas-docs/stable/visualization.html for an example.
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} | 0 | "2012-04-23T17:51:22Z" | "2012-04-28T20:09:31Z" | "2012-04-28T20:09:31Z" | MEMBER | null | ```
In [6]: ts = ts.resample('M', kind='period')
In [7]: ts
Out[7]:
Jan-1990 -0.008004
Feb-1990 -0.117806
Mar-1990 0.291086
Apr-1990 -0.298024
May-1990 -0.306769
Jun-1990 0.173145
Jul-1990 0.144821
Aug-1990 -0.238503
Sep-1990 0.102926
Oct-1990 -0.023604
Nov-1990 0.043893
Dec-1990 -0.229339
Jan-1991 -0.026023
Feb-1991 0.133059
Mar-1991 -0.006739
Apr-1991 0.138948
May-1991 0.150813
Jun-1991 0.208500
Jul-1991 -0.015446
Aug-1991 0.040145
Sep-1991 -0.252116
Oct-1991 0.378951
Nov-1991 0.333716
Dec-1991 -0.067042
Jan-1992 -0.066540
Feb-1992 0.271326
Mar-1992 0.075388
Apr-1992 0.014510
May-1992 0.008858
Jun-1992 -0.082096
Jul-1992 0.157964
Aug-1992 -0.060613
Sep-1992 0.093209
In [8]: ts.resample('A')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/Users/wesm/code/pandas/<ipython-input-8-d20eb06885e9> in <module>()
----> 1 ts.resample('A')
/Users/wesm/code/pandas/pandas/core/generic.pyc in resample(self, rule, how, axis, as_index, fill_method, closed, label, kind)
158 idx = self._get_axis(axis)
159 if not isinstance(idx, DatetimeIndex):
--> 160 raise ValueError("Cannot call resample with non-DatetimeIndex")
161
162 grouper = TimeGrouper(rule, label=label, closed=closed,
ValueError: Cannot call resample with non-DatetimeIndex
```
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"due_on": "2012-06-12T07:00:00Z",
"html_url": "https://github.com/pandas-dev/pandas/milestone/2",
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(Pdb) rng
<class 'pandas.tseries.index.DatetimeIndex'>
freq: <1 Day>, timezone: None
[2000-01-01 00:00:00, ..., 2000-01-31 00:00:00]
length: 31
(Pdb) rng2 = rng.repeat(5)
(Pdb) rng2
<class 'pandas.tseries.index.DatetimeIndex'>
freq: <1 Day>, timezone: None
[2000-01-01 00:00:00, ..., 2000-01-31 00:00:00]
length: 155
```
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} | 0 | "2012-04-23T18:17:49Z" | "2012-05-03T01:27:37Z" | "2012-05-03T01:27:37Z" | MEMBER | null | A bit tedious to have to specify the own frequency, e.g.:
`ts / ts.shift(freq='B')`
If freq is already `B` then you would like to just type: `ts / ts.tshift(1)`, this should raise an exception if no frequency set
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In [14]: rng
Out[14]:
<class 'pandas.tseries.index.DatetimeIndex'>
freq: <1 Day>, timezone: None
[1800-01-01 00:00:00, ..., 2000-01-01 00:00:00]
length: 73049
In [15]: timeit rng.weekofyear
1 loops, best of 3: 501 ms per loop
In [16]: timeit rng.year
100 loops, best of 3: 3.48 ms per loop
```
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|
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} | 1 | "2012-04-23T19:46:12Z" | "2012-05-12T17:01:55Z" | "2012-05-12T17:01:55Z" | MEMBER | null | scikits.timeseries notions of fill_missing_dates, duplicates possible, etc. Little to no testing in this regard
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} | 0 | "2012-04-23T19:52:57Z" | "2012-04-26T22:58:14Z" | "2012-04-26T22:58:14Z" | MEMBER | null | Should conform with new PeriodIndex.to_timestamp, add lots of test for annual, quarterly, other frequencies
```
def start_time(self):
return self.to_timestamp(which_end='S')
def end_time(self):
return self.to_timestamp(which_end='E')
def to_timestamp(self, which_end='S'):
"""
Return the Timestamp at the start/end of the period
Parameters
----------
which_end: str, default 'S' (start)
'S', 'E'. Can be aliased as case insensitive
'Start', 'Finish', 'Begin', 'End'
Returns
-------
Timestamp
"""
which_end = _validate_end_alias(which_end)
new_val = self.asfreq('S', which_end)
base, mult = _gfc(new_val.freq)
return Timestamp(lib.period_ordinal_to_dt64(new_val.ordinal, base, mult))
```
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} | 0 | "2012-04-23T20:12:05Z" | "2012-04-26T19:34:51Z" | "2012-04-26T19:34:51Z" | MEMBER | null | ```
In [5]: rng
Out[5]:
<class 'pandas.tseries.period.PeriodIndex'>
freq: B
[28-Dec-1999, ..., 02-Jan-2001]
length: 54
In [6]: rng.take(np.random.permutation(len(rng)))
Out[6]:
Int64Index([521672, 521592, 521572, 521587, 521732, 521712, 521512, 521627,
521552, 521582, 521717, 521752, 521737, 521777, 521647, 521517,
521597, 521677, 521722, 521622, 521542, 521652, 521682, 521707,
521632, 521612, 521577, 521772, 521637, 521547, 521557, 521532,
521667, 521617, 521747, 521527, 521742, 521562, 521607, 521522,
521642, 521657, 521537, 521662, 521727, 521692, 521702, 521567,
521697, 521767, 521757, 521602, 521687, 521762])
```
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} | 0 | "2012-04-23T20:19:39Z" | "2012-04-27T17:48:37Z" | "2012-04-27T17:48:37Z" | MEMBER | null | EDIT: actually just need a better error message
```
In [10]: PeriodIndex(val, periods=20)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/Users/wesm/code/pandas/<ipython-input-10-e39b42220b85> in <module>()
----> 1 PeriodIndex(val, periods=20)
/Users/wesm/code/pandas/pandas/tseries/period.pyc in __new__(cls, data, freq, start, end, periods, copy, name)
568
569 if freq is None:
--> 570 raise ValueError('freq cannot be none')
571
572 data = _period_unbox_array(data, check=freq)
ValueError: freq cannot be none
In [11]: val
Out[11]: Period('02-Apr-2012', 'B')
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1119 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1119/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1119/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1119/events | https://github.com/pandas-dev/pandas/issues/1119 | 4,248,378 | MDU6SXNzdWU0MjQ4Mzc4 | 1,119 | Support for different "bases" with multiples of deltas | {
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} | 2 | "2012-04-23T20:21:17Z" | "2012-05-15T01:01:05Z" | "2012-05-15T01:01:05Z" | MEMBER | null | Want a sane way to be able to do like
`ts.resample('5min', base=2)`
base=0 should be the default (as it is now)
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https://api.github.com/repos/pandas-dev/pandas/issues/1120 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1120/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1120/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1120/events | https://github.com/pandas-dev/pandas/issues/1120 | 4,248,395 | MDU6SXNzdWU0MjQ4Mzk1 | 1,120 | Parsing mixed time strings? | {
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} | 0 | "2012-04-23T20:22:32Z" | "2012-04-25T20:21:05Z" | "2012-04-25T20:21:05Z" | MEMBER | null | Like `2hr30min`. Can use regular expressions
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} | 7 | "2012-04-23T20:43:06Z" | "2012-04-26T21:47:47Z" | "2012-04-26T21:38:36Z" | CONTRIBUTOR | null | ```
creating build/lib.linux-x86_64-2.6/pandas/tseries/tests/data
copying pandas/tseries/tests/data/series.pickle -> build/lib.linux-x86_64-2.6/pandas/tseries/tests/data
copying pandas/tseries/tests/data/frame.pickle -> build/lib.linux-x86_64-2.6/pandas/tseries/tests/data
running build_ext
cythoning pandas/src/tseries.pyx to pandas/src/tseries.c
Error compiling Cython file:
------------------------------------------------------------
...
from numpy cimport int32_t, int64_t, import_array, ndarray
from cpython cimport *
from libc.stdlib cimport malloc, free, abs
from libc.math cimport floor
^
------------------------------------------------------------
pandas/pandas_test/pandas/src/datetime.pyx:10:0: 'floor.pxd' not found
Error compiling Cython file:
------------------------------------------------------------
...
from numpy cimport int32_t, int64_t, import_array, ndarray
from cpython cimport *
from libc.stdlib cimport malloc, free, abs
from libc.math cimport floor
^
------------------------------------------------------------
pandas/pandas_test/pandas/src/datetime.pyx:10:23: Name 'floor' not declared in module 'libc.math'
Error compiling Cython file:
------------------------------------------------------------
...
def roll_median_c(ndarray[float64_t] arg, int win, int minp):
cdef double val, res, prev
cdef:
int ret=0
skiplist_t *sl
^
------------------------------------------------------------
pandas/pandas_test/pandas/src/moments.pyx:448:8: 'skiplist_t' is not a type identifier
Error compiling Cython file:
------------------------------------------------------------
...
cdef Py_ssize_t N = len(arg)
cdef ndarray[double_t] output = np.empty(N, dtype=float)
sl = skiplist_init(win)
^
------------------------------------------------------------
pandas/pandas_test/pandas/src/moments.pyx:455:22: undeclared name not builtin: skiplist_init
Error compiling Cython file:
------------------------------------------------------------
...
val = arg[i]
# Not NaN
if val == val:
nobs += 1
skiplist_insert(sl, val)
^
------------------------------------------------------------
pandas/pandas_test/pandas/src/moments.pyx:465:27: undeclared name not builtin: skiplist_insert
Error compiling Cython file:
------------------------------------------------------------
...
if i > win - 1:
prev = arg[i - win]
if prev == prev:
skiplist_remove(sl, prev)
^
------------------------------------------------------------
pandas/pandas_test/pandas/src/moments.pyx:476:31: undeclared name not builtin: skiplist_remove
Error compiling Cython file:
------------------------------------------------------------
...
skiplist_remove(sl, prev)
nobs -= 1
if val == val:
nobs += 1
skiplist_insert(sl, val)
^
------------------------------------------------------------
pandas/pandas_test/pandas/src/moments.pyx:481:27: undeclared name not builtin: skiplist_insert
Error compiling Cython file:
------------------------------------------------------------
...
skiplist_insert(sl, val)
if nobs >= minp:
midpoint = nobs / 2
if nobs % 2:
res = skiplist_get(sl, midpoint, &ret)
^
------------------------------------------------------------
pandas/pandas_test/pandas/src/moments.pyx:486:34: undeclared name not builtin: skiplist_get
Error compiling Cython file:
------------------------------------------------------------
...
if nobs >= minp:
midpoint = nobs / 2
if nobs % 2:
res = skiplist_get(sl, midpoint, &ret)
else:
res = (skiplist_get(sl, midpoint, &ret) +
^
------------------------------------------------------------
pandas/pandas_test/pandas/src/moments.pyx:488:35: undeclared name not builtin: skiplist_get
Error compiling Cython file:
------------------------------------------------------------
...
midpoint = nobs / 2
if nobs % 2:
res = skiplist_get(sl, midpoint, &ret)
else:
res = (skiplist_get(sl, midpoint, &ret) +
skiplist_get(sl, (midpoint - 1), &ret)) / 2
^
------------------------------------------------------------
pandas/pandas_test/pandas/src/moments.pyx:489:35: undeclared name not builtin: skiplist_get
Error compiling Cython file:
------------------------------------------------------------
...
else:
res = NaN
output[i] = res
skiplist_destroy(sl)
^
------------------------------------------------------------
pandas/pandas_test/pandas/src/moments.pyx:495:20: undeclared name not builtin: skiplist_destroy
building 'pandas._tseries' extension
creating build/temp.linux-x86_64-2.6
creating build/temp.linux-x86_64-2.6/pandas
creating build/temp.linux-x86_64-2.6/pandas/src
gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -g -O2 -fPIC -I/usr/lib/pymodules/python2.6/numpy/core/include -I/usr/include/python2.6 -c pandas/src/tseries.c -o build/temp.linux-x86_64-2.6/pandas/src/tseries.o
pandas/src/tseries.c:1:2: Fehler: #error Do not use this file, it is the result of a failed Cython compilation.
error: command 'gcc' failed with exit status 1
make[1]: *** [python-install2.6] Fehler 1
make[1]: Verlasse Verzeichnis 'pandas/pandas_test'
make: *** [binary] Fehler 2
dpkg-buildpackage: Fehler: Fehler-Exitstatus von fakeroot debian/rules binary war 2
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1122 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1122/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1122/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1122/events | https://github.com/pandas-dev/pandas/issues/1122 | 4,249,648 | MDU6SXNzdWU0MjQ5NjQ4 | 1,122 | NaN group causes all groups to be NaN in groupby.transform with shift | {
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} | [] | closed | false | null | [] | null | 1 | "2012-04-23T21:34:04Z" | "2012-04-25T09:42:26Z" | "2012-04-25T09:42:26Z" | NONE | null | df = DataFrame( [(1,1,3), (1,2,4),(2,1,np.nan),(2,2,np.nan)])
print df.groupby([0,1]).transform(lambda x: x)
```
2
```
0 3
1 4
2 NaN
3 NaN
print df.groupby([0,1]).transform(lambda x: x.shift(1))
2
0 NaN
1 NaN
2 NaN
3 NaN
expected:
2
0 NaN
1 3
2 NaN
3 NaN
use case would be group wise changes of another column (i.e., group by dimension and week, then week over week change in a new column by transform(lambda x: x/x.shift(1)-1)
edit:
apparently:
df = DataFrame( [(1,1,3), (1,2,4),(2,1,1),(2,2,np.nan)])
has the same problem...so it's sufficient to have an operation that results in a full NaN group.
edit2:
print df.groupby([0,1]).transform(lambda x: 1/x) (and the first df in this issue) does not result in a full NaN result.
so the issue seems to be with Series.shift()
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https://api.github.com/repos/pandas-dev/pandas/issues/1123 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1123/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1123/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1123/events | https://github.com/pandas-dev/pandas/issues/1123 | 4,250,962 | MDU6SXNzdWU0MjUwOTYy | 1,123 | Integer overflow with period_asfreq on 32-bit system | {
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] | closed | false | null | [] | null | 3 | "2012-04-23T23:08:34Z" | "2012-04-24T11:13:37Z" | "2012-04-24T11:13:37Z" | CONTRIBUTOR | null | This causes a lot of test failures on my system.
```
In [4]: ival_H = Period(freq='H', year=2007, month=1, day=1, hour=0)
In [5]: ival_H.asfreq('S','S')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/thomas/Code/<ipython-input-5-1380e902ce36> in <module>()
----> 1 ival_H.asfreq('S','S')
/home/thomas/Code/virtualenvs/pandas-py2/local/lib/python2.7/site-packages/pandas-0.8.0.dev_4bf61b5-py2.7-linux-i686.egg/pandas/tseries/period.pyc in asfreq(self, freq, how)
168 base2, mult2, py3compat.str_to_bytes(how))
169
--> 170 return Period(new_ordinal, (base2, mult2))
171
172 def start_time(self):
/home/thomas/Code/virtualenvs/pandas-py2/local/lib/python2.7/site-packages/pandas-0.8.0.dev_4bf61b5-py2.7-linux-i686.egg/pandas/tseries/period.pyc in __init__(self, value, freq, year, month, quarter, day, hour, minute, second)
112 elif isinstance(value, (int, long, np.integer)):
113 if value <= 0:
--> 114 raise ValueError("Value must be positive")
115 self.ordinal = value
116 if freq is None:
ValueError: Value must be positive
```
The C code is ultimately doing `ival_H.ordinal * 60 * 60`, and trying to store the result in a `long` - which according to [this table](http://en.wikipedia.org/wiki/Integer_%28computer_science%29#Common_long_integer_sizes) will be 32 bits on my system, and therefore isn't enough to hold it. From the debugger, `lib.period_asfreq()` returns -1121299440, which is equal to `(ival_H.ordinal*60*60 & sys.maxsize) - sys.maxsize - 1`.
I've tried substituing `long long` or `int64_t`, but the problem persisted, so I must be missing something.
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https://api.github.com/repos/pandas-dev/pandas/issues/1124 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1124/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1124/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1124/events | https://github.com/pandas-dev/pandas/issues/1124 | 4,255,796 | MDU6SXNzdWU0MjU1Nzk2 | 1,124 | sharex and sharey do not turn off redundant tick labeling | {
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] | closed | false | null | [] | null | 1 | "2012-04-24T08:45:52Z" | "2012-04-29T19:16:50Z" | "2012-04-29T19:16:50Z" | CONTRIBUTOR | null | sharex and sharey do not turn off redundant tick labeling, is this an intended deviation from matplotlib `subplots()` behavior?
[pandas.tools.plotting._subplots()](https://github.com/pydata/pandas/blob/master/pandas/tools/plotting.py#L923) is a copy from matplotlib, but the code to remove the redundant tick labeling when using _sharex=True_ or _sharey=True_ has been left out.
This is the code present in matplotlib `subplots()` but not in [pandas.tools.plotting._subplots()](https://github.com/pydata/pandas/blob/master/pandas/tools/plotting.py#L923)
``` python
# turn off redundant tick labeling
if sharex and nrows>1:
# turn off all but the bottom row
for ax in axarr[:-1,:].flat:
for label in ax.get_xticklabels():
label.set_visible(False)
if sharey and ncols>1:
# turn off all but the first column
for ax in axarr[:,1:].flat:
for label in ax.get_yticklabels():
label.set_visible(False)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1125 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1125/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1125/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1125/events | https://github.com/pandas-dev/pandas/issues/1125 | 4,256,643 | MDU6SXNzdWU0MjU2NjQz | 1,125 | failing unittests on 32b linux machine | {
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} | 2 | "2012-04-24T09:51:41Z" | "2012-04-26T21:39:35Z" | "2012-04-26T21:39:35Z" | CONTRIBUTOR | null | Just did a fresh install of pandas on a 32bits linux machine (ubuntu), i suppose this is a supported platform.
Running nosetests shows many failures (one example at the bottom).
numpy 1.6.1
Cython 0.16
pandas from master
A lot of them are related to numpy issue:
``` python
In [11]: values = np.random.randn(100000, 5)
In [12]: values.take(np.arange(10, dtype='int32'))
Out[12]:
array([-0.49410797, -0.24884862, 0.76527299, -0.96366541, -2.64132367,
0.21082825, -0.09124413, -0.89438207, -0.16012725, 0.20569685])
In [13]: values.take(np.arange(10, dtype='int64'))
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/home/lodagro/projects/pandas/<ipython-input-13-6ad7fa396ff4> in <module>()
----> 1 values.take(np.arange(10, dtype='int64'))
TypeError: array cannot be safely cast to required type
In [14]:
```
```
======================================================================
ERROR: test_append (pandas.sparse.tests.test_sparse.TestSparseDataFrame)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/lodagro/.virtualenvs/pandas/lib/python2.7/site-packages/pandas-0.8.0.dev_789300e-py2.7-linux-i686.egg/pandas/sparse/tests/test_sparse.py", line 1014, in test_append
a = self.frame[:5]
File "/home/lodagro/.virtualenvs/pandas/lib/python2.7/site-packages/pandas-0.8.0.dev_789300e-py2.7-linux-i686.egg/pandas/sparse/frame.py", line 318, in __getitem__
return self.reindex(date_rng)
File "/home/lodagro/.virtualenvs/pandas/lib/python2.7/site-packages/pandas-0.8.0.dev_789300e-py2.7-linux-i686.egg/pandas/core/frame.py", line 1916, in reindex
fill_value, limit)
File "/home/lodagro/.virtualenvs/pandas/lib/python2.7/site-packages/pandas-0.8.0.dev_789300e-py2.7-linux-i686.egg/pandas/sparse/frame.py", line 511, in _reindex_index
new = values.take(indexer)
TypeError: array cannot be safely cast to required type
```
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} | 1 | "2012-04-24T15:17:49Z" | "2012-05-01T20:31:05Z" | "2012-05-01T20:31:05Z" | MEMBER | null | Lost it during the merge and the plot refactor. All that code (pandas/tseries/plotting.py) needs to be cleaned up
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} | 0 | "2012-04-24T15:20:57Z" | "2012-04-27T15:47:51Z" | "2012-04-27T15:47:51Z" | MEMBER | null | Suppose we're doing 5 minute data and want to get 9:34:59 instead of 9:35:00 for the resampled labels. want to do like:
```
ts.resample('5min', closed='left', label='right', loffset=timedelta(seconds=-1))
```
Straightforward to convert the timedelta into microseconds and adjust the datetime64 values
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https://api.github.com/repos/pandas-dev/pandas/issues/1128 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1128/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1128/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1128/events | https://github.com/pandas-dev/pandas/issues/1128 | 4,264,476 | MDU6SXNzdWU0MjY0NDc2 | 1,128 | Add level option to rank() for decile, quartile analysis | {
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} | 2 | "2012-04-24T17:06:44Z" | "2012-07-13T01:38:34Z" | "2012-07-13T01:38:34Z" | MEMBER | null | What do you think about a kwarg to rank, like "levels=None"? If None, it does the default, which is the 1...n rank. If levels=k, then it does the 1..k, eg 1..10. Then you could do
df.groupby(df.A.rank(levels=10)).mean()
which is a hell of a lot prettier than (df.A.rank()/float(len(df))*10.).astype(int)
Actually, what I am after is a bit more than this. I have some tile summary code that I use all the time that takes as input x and y and returns a record array of the tile label, the min and max for x, and descriptive statistics for y in each tile. If looks like this in use:
In [14]: rt = nansafe.tile_summary(df.A, df.B, levels=np.arange(10, 101, 10))
In [15]: print mlab.rec2txt(rt)
label xmin xmax qcount qmean qmedian qmin qmax qcimin qcimax
tile 0 -3.466 -1.259 100 -0.067 -0.105 -2.424 2.308 -0.165 0.030
tile 1 -1.258 -0.821 100 0.031 0.014 -3.017 2.016 -0.067 0.129
tile 2 -0.820 -0.533 100 -0.013 0.024 -2.924 2.476 -0.117 0.091
tile 3 -0.531 -0.258 100 0.190 0.236 -2.449 2.454 0.091 0.289
tile 4 -0.252 0.016 100 0.008 -0.063 -1.717 1.789 -0.073 0.090
tile 5 0.025 0.240 100 0.014 -0.018 -2.116 2.513 -0.081 0.109
tile 6 0.243 0.494 100 0.050 0.006 -1.957 1.618 -0.030 0.130
tile 7 0.494 0.820 100 0.170 -0.011 -1.924 2.327 0.082 0.257
tile 8 0.826 1.244 100 -0.036 -0.131 -2.317 2.360 -0.137 0.065
tile 9 1.246 3.055 100 -0.153 -0.123 -3.225 1.845 -0.249 -0.058
tile ALL -3.466 3.055 1000 0.019 -0.020 -3.225 2.513 -0.011 0.049
and the output y columns are customizable::
Definition: nansafe.tile_summary(x, y, stats=(<function count at 0x98cebc4>, <function mean at 0x98ce994>, <function median at 0x98ce9cc>, <function min at 0x98ceca4>, <function max at 0x98cecdc>, <function cimin at 0x98cec34>, <function cimax at 0x98cec6c>), levels=(20.0, 40.0, 60.0, 80.0, 100.0), breakpoints=None, tilelabel='tile', names=None, catd=None, verbose_output=False, force_categorical=False)
Do you think something like this has a place in pandas?
cc @jdh2358
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} | 0 | "2012-04-24T17:57:36Z" | "2012-04-27T15:38:42Z" | "2012-04-27T15:38:42Z" | MEMBER | null | This is not a surprise but needs to be fixed
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```
gcc-4.2 -DNDEBUG -g -fwrapv -Os -Wall -Wstrict-prototypes -pipe -arch x86_64 -I/Library/Python/2.7/site-packages/numpy-1.7.0.dev_3cb783e_20120425-py2.7-macosx-10.7-x86_64.egg/numpy/core/include -I/System/Library/Frameworks/Python.framework/Versions/2.7/include/python2.7 -c pandas/src/np_datetime_strings.c -o build/temp.macosx-10.7-x86_64-2.7/pandas/src/np_datetime_strings.o
In file included from /Library/Python/2.7/site-packages/numpy-1.7.0.dev_3cb783e_20120425-py2.7-macosx-10.7-x86_64.egg/numpy/core/include/numpy/ndarraytypes.h:1974,
from /Library/Python/2.7/site-packages/numpy-1.7.0.dev_3cb783e_20120425-py2.7-macosx-10.7-x86_64.egg/numpy/core/include/numpy/ndarrayobject.h:17,
from /Library/Python/2.7/site-packages/numpy-1.7.0.dev_3cb783e_20120425-py2.7-macosx-10.7-x86_64.egg/numpy/core/include/numpy/arrayobject.h:15,
from pandas/src/np_datetime_strings.c:17:
/Library/Python/2.7/site-packages/numpy-1.7.0.dev_3cb783e_20120425-py2.7-macosx-10.7-x86_64.egg/numpy/core/include/numpy/npy_deprecated_api.h:11:2: warning: #warning "Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION"
pandas/src/np_datetime_strings.c: In function ‘get_datetime_iso_8601_strlen’:
pandas/src/np_datetime_strings.c:911: error: ‘NPY_FR_B’ undeclared (first use in this function)
pandas/src/np_datetime_strings.c:911: error: (Each undeclared identifier is reported only once
pandas/src/np_datetime_strings.c:911: error: for each function it appears in.)
pandas/src/np_datetime_strings.c:888: warning: enumeration value ‘NPY_FR_GENERIC’ not handled in switch
error: command 'gcc-4.2' failed with exit status 1
```
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} | 1 | "2012-04-25T21:48:00Z" | "2016-09-29T22:44:19Z" | "2016-09-29T22:13:35Z" | MEMBER | null | The idea being that a lot of filling and other things would happen automatically. Goes against "explicit is better than implicit" but could really simplify the logic in a number of places
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} | 14 | "2012-04-25T23:59:00Z" | "2016-08-25T10:20:52Z" | "2016-08-25T10:20:52Z" | CONTRIBUTOR | null | Something seems to be wrong with s1 == s2 when s1 and s2 don't have the same index. Here is a snippet example:
``` python
import operator
import pandas
s1 = pandas.Series([1,2], ['a','b'])
s2 = pandas.Series([2,3], ['b','c'])
s1 == s2
s2 == s1
```
with the output:
```
InIn [5]: s1 == s2
Out[5]:
a False
b False
In [6]: s2 == s1
Out[6]:
b False
c False
```
On the other hand using combine works fine:
```
In [7]: s1.combine(s2, operator.eq)
Out[7]:
a 0
b 1
c 0
In [8]: s2.combine(s1, operator.eq)
Out[8]:
a 0
b 1
c 0
```
I guess you can first align s1 and s2 and then compare them, but is there a good reason why this couldn't work out of the box?
There doesn't seem to be any tests for pandas.Series. __eq__ for two series with a different index in pandas/pandas/tests/test_series.py. I have a patch lying around to add such a test and I could commit it if that's useful.
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} | 7 | "2012-04-26T07:52:16Z" | "2020-01-31T04:06:39Z" | "2020-01-31T04:06:39Z" | MEMBER | null | When you are working with a large array, it is not printed out in its entirety in the console, but when you have grouped them with `groupby('key')`, the groups are all printed out. Would it be possible to also restrict the output of `groupby('key').groups` to eg the first and last groups?
I was working with a rather large dataframe (around 80000 rows), and first it took a long time to print it all, and second the console got stuck for a while (but that could also be an issue with spyder).
I know it is not very useful to print out the groups, but I was curious to see what it would look like, but it was not a very good idea with such a large array.
```python
>>> import numpy as np
>>> import pandas as pd
>>> df = pd.DataFrame(np.random.randn(100000, 4), columns=list('abcd'))
>>> df['g'] = np.random.randint(0, 100, 100000)
>>> df.groupby('g').groups # this repr should be truncated
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/1136 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1136/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1136/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1136/events | https://github.com/pandas-dev/pandas/issues/1136 | 4,302,040 | MDU6SXNzdWU0MzAyMDQw | 1,136 | pandas and pyinstaller | {
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} | [] | closed | false | null | [] | null | 1 | "2012-04-26T13:47:27Z" | "2012-04-26T14:55:57Z" | "2012-04-26T14:55:54Z" | NONE | null | Dear pandas dev,
thanks a lot for this great tools.
I try to make a one file distribuable under window with pyinstaller.
This do not work becasue from what I anderstand : "pandas do hidden import" and need a hook-pandas in pyinstaller.
So question : does anyone have tried to use pyinstaller with pandas ?
Thanks a lot.
Samuel
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https://api.github.com/repos/pandas-dev/pandas/issues/1137 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1137/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1137/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1137/events | https://github.com/pandas-dev/pandas/issues/1137 | 4,308,448 | MDU6SXNzdWU0MzA4NDQ4 | 1,137 | Different types returned for numpy 1.5.1 and 1.6.1 | {
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} | 1 | "2012-04-26T18:14:37Z" | "2012-06-21T22:44:55Z" | "2012-06-21T22:44:55Z" | NONE | null | I noticed that certain `numpy` functions return `pandas.Series` in 1.5.1 and `numpy.array` in 1.6.1. This makes it hard to write code that works for both versions when the index is not `0..n`, but something else like timestamps.
For example on Mac OS X 10.7.3 with numpy 1.5.1:
``` python
Python 2.7.1 (r271:86832, Jun 25 2011, 05:09:01)
[GCC 4.2.1 (Based on Apple Inc. build 5658) (LLVM build 2335.15.00)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy
>>> numpy.__version__
'1.5.1'
>>> import pandas
>>> pandas.__version__
'0.7.3'
>>> a = numpy.array(range(10))
>>> s = pandas.Series(a)
>>> numpy.where(~numpy.isnan(s))
(0 0
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9,)
>>> type(numpy.where(~numpy.isnan(s))[0])
<class 'pandas.core.series.Series'>
```
versus same system with numpy 1.6.1
``` python
Python 2.7.1 (r271:86832, Jun 25 2011, 05:09:01)
[GCC 4.2.1 (Based on Apple Inc. build 5658) (LLVM build 2335.15.00)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy
>>> numpy.__version__
'1.6.1'
>>> import pandas
>>> pandas.__version__
'0.7.3'
>>> a = numpy.array(range(10))
>>> s = pandas.Series(a)
>>> numpy.where(~numpy.isnan(s))
(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),)
>>> type(numpy.where(~numpy.isnan(s))[0])
<type 'numpy.ndarray'>
```
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} | 0 | "2012-04-26T19:43:25Z" | "2012-04-27T21:52:56Z" | "2012-04-27T21:52:56Z" | MEMBER | null | ```
In [5]: idx
Out[5]:
<class 'pandas.tseries.period.PeriodIndex'>
freq: D
[01-Jan-2000, ..., 20-Jan-2000]
length: 20
In [6]: idx.join(idx[:-5], how='inner')
Out[6]:
Int64Index([730120, 730121, 730122, 730123, 730124, 730125, 730126, 730127,
730128, 730129, 730130, 730131, 730132, 730133, 730134])
In [7]: idx.union(idx[:-5])
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/wesm/code/pandas/<ipython-input-7-e8f9b3613a4d> in <module>()
----> 1 idx.union(idx[:-5])
/home/wesm/code/pandas/pandas/core/index.pyc in union(self, other)
496
497 # for subclasses
--> 498 return self._wrap_union_result(other, result)
499
500 def _wrap_union_result(self, other, result):
/home/wesm/code/pandas/pandas/core/index.pyc in _wrap_union_result(self, other, result)
500 def _wrap_union_result(self, other, result):
501 name = self.name if self.name == other.name else None
--> 502 return type(self)(data=result, name=name)
503
504 def intersection(self, other):
/home/wesm/code/pandas/pandas/tseries/period.pyc in __new__(cls, data, freq, start, end, periods, copy, name)
518 else:
519 if freq is None:
--> 520 raise ValueError('freq cannot be none')
521
522 if data.dtype == np.datetime64:
ValueError: freq cannot be none
```
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} | 4 | "2012-04-26T21:09:47Z" | "2012-04-26T21:47:07Z" | "2012-04-26T21:37:44Z" | CONTRIBUTOR | null | ```
pandas/src/tseries.c: In Funktion »__pyx_pf_6pandas_8_tseries_236join_sorter«:
pandas/src/tseries.c:127783:14: Warnung: Variable »__pyx_bshape_0_where« gesetzt, aber nicht verwendet [-Wunused-but-set-variable]
pandas/src/tseries.c:127780:14: Warnung: Variable »__pyx_bshape_0_counts« gesetzt, aber nicht verwendet [-Wunused-but-set-variable]
pandas/src/tseries.c:127777:14: Warnung: Variable »__pyx_bshape_0_result« gesetzt, aber nicht verwendet [-Wunused-but-set-variable]
pandas/src/tseries.c:127774:14: Warnung: Variable »__pyx_bshape_0_index« gesetzt, aber nicht verwendet [-Wunused-but-set-variable]
pandas/src/tseries.c: In Funktion »is_datetime64_object«:
pandas/src/numpy_helper.h:74:1: Warnung: Kontrollfluss erreicht Ende von Nicht-void-Funktion [-Wreturn-type]
pandas/src/tseries.c: In Funktion »infer_type«:
pandas/src/numpy_helper.h:43:1: Warnung: Kontrollfluss erreicht Ende von Nicht-void-Funktion [-Wreturn-type]
error: command 'gcc' failed with exit status 1
make[1]: *** [python-install2.6] Fehler 1
make[1]: Verlasse Verzeichnis 'pandas/pandas_test'
make: *** [binary] Fehler 2
dpkg-buildpackage: Fehler: Fehler-Exitstatus von fakeroot debian/rules binary war 2
```
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} | 9 | "2012-04-27T01:49:47Z" | "2012-05-07T15:43:33Z" | "2012-05-07T15:36:15Z" | CONTRIBUTOR | null | Hi,
I was wondering if there was a good reason why this works:
``` python
dat.ix[:,2] + 2
```
but not this:
``` python
dat.ix[:,2] = dat.ix[:,2] + 2
```
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} | 0 | "2012-04-27T19:45:00Z" | "2012-04-27T20:38:12Z" | "2012-04-27T20:38:12Z" | CONTRIBUTOR | null | In [18]: a = DatetimeIndex(start=datetime(2000, 1, 1), freq='D', periods=3)
In [19]: a
Out[19]:
<class 'pandas.tseries.index.DatetimeIndex'>
freq: D, timezone: None
[2000-01-01 00:00:00, ..., 2000-01-03 00:00:00]
length: 3
In [20]: DatetimeIndex(a)
Out[20]: DatetimeIndex([2000-01-01 00:00:00, 2000-01-02 00:00:00, 2000-01-03 00:00:00], dtype=datetime64[us])
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df2 = pd.DataFrame(columns=['A', 'B', 'C'], index=[1,2,3])
In [38]: df2
Out[38]:
A B C
1 NaN NaN NaN
2 NaN NaN NaN
3 NaN NaN NaN
In [40]: x = df2.ix[1] # create reference to index 1
In [42]: x['A'] = 3 # this changes the value in original df2
In [43]: df2
Out[43]:
A B C
1 3.0 NaN NaN
2 NaN NaN NaN
3 NaN NaN NaN
In [44]: x.A = 4 # this doesn't change df2, while they should behave the same (intuitively)
In [45]: df2
Out[45]:
A B C
1 3.0 NaN NaN
2 NaN NaN NaN
3 NaN NaN NaN
```
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} | 0 | "2012-04-27T23:16:29Z" | "2012-05-03T01:20:37Z" | "2012-05-03T01:20:37Z" | MEMBER | null | There are no tests for these
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} | 0 | "2012-04-27T23:25:27Z" | "2012-05-01T18:09:21Z" | "2012-05-01T18:09:21Z" | MEMBER | null | I don't think this roundtrips work right now
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Right now it's either aggregate whole chunks or 1D arrays, maybe should allow frame-by-frame
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} | 1 | "2012-04-28T22:43:53Z" | "2016-10-12T23:02:56Z" | "2013-09-20T18:09:22Z" | NONE | null | Hi,
thanks for all the awesome work with Pandas. I just started working on it, and I ran into two distinct bugs:
- The HDFStore function won't accept sparse_dataframes as objects - doing so raises a KeyError.
- If you pickle a sparse data frame with a hierarchical index, then reload the matrix, the resulting matrix loses all hierarchical indexing information and all the indexes are collapsed as tuples.
I am using the latest version of pandas on Ubuntu 12.04 with the latest version of EPD.
Federico
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} | 0 | "2012-04-29T00:17:09Z" | "2012-05-05T19:26:42Z" | "2012-05-05T19:26:42Z" | MEMBER | null | Each timezone in pytz has a `_tzinfos` dict:
In [3]: type(pytz.timezone('US/Eastern'))
Out[3]: pytz.tzfile.US/Eastern
In [4]: pytz.timezone('US/Eastern')._tzinfos
Out[4]:
{(datetime.timedelta(-1, 68400),
datetime.timedelta(0),
'EST'): <DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>,
(datetime.timedelta(-1, 72000),
datetime.timedelta(0, 3600),
'EDT'): <DstTzInfo 'US/Eastern' EDT-1 day, 20:00:00 DST>,
(datetime.timedelta(-1, 72000),
datetime.timedelta(0, 3600),
'EPT'): <DstTzInfo 'US/Eastern' EPT-1 day, 20:00:00 DST>,
(datetime.timedelta(-1, 72000),
datetime.timedelta(0, 3600),
'EWT'): <DstTzInfo 'US/Eastern' EWT-1 day, 20:00:00 DST>}
Need some kind of sane (read: performant) way to retrieve the right one of these to attach when boxing a timestamp. Is not as trivial as I thought
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} | 3 | "2012-04-29T14:54:24Z" | "2012-05-05T20:42:26Z" | "2012-05-05T20:42:26Z" | MEMBER | null | Perhaps should shift like PeriodIndex if frequency is set
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} | 2 | "2012-04-29T15:10:33Z" | "2018-07-06T20:20:59Z" | "2018-07-06T19:34:27Z" | MEMBER | null | This might be quite a bit of work, maybe leave for post 0.8.0
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} | 0 | "2012-04-29T15:23:12Z" | "2012-05-15T00:48:13Z" | "2012-05-15T00:48:13Z" | MEMBER | null | In very large time series, a first call to
```
ts[stamp]
```
will cause a hash table population to look up the location of `stamp`. If the index has, say, 10mm timestamps, the allocated hash table will take up at minimum 160 megabytes ((timestamp, location) pairs, each 8 bytes) probably rounded up to 256 megabytes. I would say either always use binary search or don't create the hash table when the index exceeds a certain length.
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} | 4 | "2012-04-29T15:45:01Z" | "2019-01-11T11:55:18Z" | "2019-01-11T11:55:18Z" | NONE | null | It looks like the time series branch already includes increased functionality for high performance time resampling between time frequencies. Will there be support for converting from higher frequency data to other grouping methods, such as tick data or 1s bars to 1000 volume bars? Perhaps this doesn't fall into true time series functionality.
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https://api.github.com/repos/pandas-dev/pandas/issues/1162 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1162/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1162/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1162/events | https://github.com/pandas-dev/pandas/issues/1162 | 4,342,059 | MDU6SXNzdWU0MzQyMDU5 | 1,162 | Negative numbers not handled correctly in frequency strings | {
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} | 0 | "2012-04-29T17:33:45Z" | "2012-05-01T16:54:55Z" | "2012-05-01T16:54:55Z" | MEMBER | null | ```
In [2]: datetools.to_offset('-1S')
Out[2]: <1 Second>
```
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} | 0 | "2012-04-29T17:39:46Z" | "2012-04-29T21:43:57Z" | "2012-04-29T21:43:57Z" | MEMBER | null | ```
In [7]: ts
Out[7]:
2000-01-01 00:00:00 -1.135382
2000-01-01 00:01:00 1.295421
2000-01-01 00:02:00 0.625706
2000-01-01 00:03:00 0.235525
2000-01-01 00:04:00 0.254634
2000-01-01 00:05:00 -0.393107
2000-01-01 00:06:00 -1.881196
2000-01-01 00:07:00 -2.447050
2000-01-01 00:08:00 1.055716
2000-01-01 00:09:00 1.156710
2000-01-01 00:10:00 -1.108877
2000-01-01 00:11:00 -0.710064
Freq: T
In [8]: ts.resample('5min', loffset=datetools.Second(-1))
Out[8]:
1999-12-31 23:59:59 -1.135382
2000-01-01 00:04:59 0.403636
2000-01-01 00:09:59 -0.644939
2000-01-01 00:14:59 -0.710064
Freq: 5T
In [9]: ts.resample('5min', loffset='1min')
Out[9]:
2000-01-01 00:00:00 -1.135382
2000-01-01 00:05:00 0.403636
2000-01-01 00:10:00 -0.644939
2000-01-01 00:15:00 -0.710064
Freq: 5T
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1164 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1164/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1164/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1164/events | https://github.com/pandas-dev/pandas/issues/1164 | 4,342,121 | MDU6SXNzdWU0MzQyMTIx | 1,164 | Add column name option to Series.reset_index | {
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} | 0 | "2012-04-29T17:48:18Z" | "2012-05-01T08:21:04Z" | "2012-05-01T08:21:04Z" | MEMBER | null | Currently the column name in the result DataFrame will either be 0 or the `name` attribute (if any):
```
In [31]: df1.groupby( [ "Name", "City"] ).size()
Out[31]:
Name City
Alice Seattle 1
Bob Seattle 2
Mallory Portland 2
Seattle 1
In [32]: df1.groupby( [ "Name", "City"] ).size().reset_index()
Out[32]:
Name City 0
0 Alice Seattle 1
1 Bob Seattle 2
2 Mallory Portland 2
3 Mallory Seattle 1
```
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```
In [44]: ts.resample('5min')
Out[44]:
2000-01-01 00:00:00 -0.744244
2000-01-01 00:05:00 -0.934083
2000-01-01 00:10:00 0.198829
2000-01-01 00:15:00 1.786013
Freq: 5T
In [45]: ts[2:].resample('5min')
Out[45]:
2000-01-01 00:02:00 -0.903645
2000-01-01 00:07:00 -0.496863
2000-01-01 00:12:00 0.461970
2000-01-01 00:17:00 NaN
Freq: 5T
```
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} | 0 | "2012-04-29T22:15:43Z" | "2012-05-03T01:20:06Z" | "2012-05-03T01:20:06Z" | MEMBER | null | ```
In [3]: Period(200701, freq='M')
Out[3]: Period('Jan-16726', 'M')
In [4]: Period('200701', freq='M')
Out[4]: Period('Jul-2020', 'M')
```
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} | 2 | "2012-04-29T23:24:42Z" | "2012-05-05T19:33:18Z" | "2012-05-05T19:33:18Z" | MEMBER | null | Currently the code calls `offset.onOffset` on every date. This is pretty weak; might want to do something more intelligent. Add vbenchmarks too
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} | 0 | "2012-04-30T16:36:22Z" | "2012-05-05T20:18:16Z" | "2012-05-05T20:18:16Z" | MEMBER | null | The time zone only affects what the user "sees"; will make set ops and operations between different-timezone time series much more straightforward
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In [209]: a = DataFrame(np.asarray([1,2,3,4,5]).T)
In [210]: a
Out[210]:
0
0 1
1 2
2 3
3 4
4 5
In [211]: a.hist()
TypeError Traceback (most recent call last)
H:<ipython-input-211-351515a79fe3> in <module>()
----> 1 a.hist()
C:\Python27\lib\site-packages\pandas\core\frame.pyc in hist(self, grid, *_kwds)
3923
3924 for i, col in enumerate(_try_sort(self.columns)):
-> 3925 ax = axes[i / k][i % k]
3926 ax.hist(self[col].dropna().values, *_kwds)
3927 ax.set_title(col)
TypeError: 'AxesSubplot' object does not support indexing
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} | 1 | "2012-05-01T18:32:03Z" | "2012-05-07T16:26:04Z" | "2012-05-07T16:26:04Z" | MEMBER | null | Want to disable attribute setting outside of name and index
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} | 8 | "2012-05-02T15:14:41Z" | "2014-06-26T11:02:15Z" | "2012-07-15T22:49:43Z" | CONTRIBUTOR | null | ...ries class, performing element-wise regex matching (Series.str_find) and substitution (Series.string_sub).
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Following
https://groups.google.com/forum/?fromgroups#!topic/pydata/pZjQMX_avmY
and
https://github.com/pydata/pandas/issues/854
I would suggest to insert:
https://github.com/pydata/pandas/tree/master/pandas/tseries/converters.py
Where users could contribute/share typical converters for getting their date and times parsed into a pandas object from input.
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} | 10 | "2012-05-02T17:39:01Z" | "2012-05-09T17:54:02Z" | "2012-05-08T18:27:05Z" | MEMBER | null | from @rkern on the pydata mailing list:
```
Hello! I am using the pivot table functionality to good effect. One
issue I am running to, however, is that the columns end up being
roughly a full Cartesian product of the column pivots. That is, there
are blocks of all-NA columns. The row pivots appear to be pruned
already; all-NA rows don't show up. For pivot values that are roughly
tree-structured (i.e. the children of one node mostly don't show up as
children of a neighboring node), this can create pivot tables with
very many columns. Currently, I post-process the pivot tables using
.dropna(axis=1, how='all'), but I have just run into a case there the
intermediate table is too large for my 32-bit machine. Would there be
a good way to change the pivot table computation to get appropriately
sparse trees for both the rows and the columns? I am happy to look
into it myself, but I did want to check to see if it was on anyone's
radar or if anyone had suggestions.
```
to my response
```
That's actually a good point and it's basically an oversight in the pivot_table implementation. If you look at the code, it's basically a convenience function that uses groupby and calls unstack on the aggregated result. I suspect the problem with unstack is that it's creating lots of empty columns;
```
```
```
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} | 0 | "2012-05-02T18:38:53Z" | "2012-05-07T18:23:41Z" | "2012-05-07T18:23:41Z" | MEMBER | null | ```
In [2]: s = pandas.Series([1,2])
In [3]: s
Out[3]:
0 1
1 2
In [4]: s.apply(lambda x: (x, x+1))
Out[4]:
0 0 1
1 2
1 0 2
1 3
```
But:
```
In [8]: s = pandas.Series([1,2,3])
In [9]: s
Out[9]:
0 1
1 2
2 3
In [10]: s.apply(lambda x: (x, x+1))
Out[10]:
0 (1, 2)
1 (2, 3)
2 (3, 4)
```
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} | 0 | "2012-05-02T19:45:11Z" | "2012-06-25T13:51:14Z" | "2012-06-25T13:51:14Z" | MEMBER | null | ```
In [1]: date_range('1/1/2000', periods=10, freq='1B30min')
Out[1]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2000-01-03 00:30:00, ..., 2000-01-13 04:30:00]
Length: 9, Freq: B, Timezone: None
In [2]: date_range('1/1/2000', periods=10, freq='1B30min').offset
Out[2]: <1 BusinessDay: offset=datetime.timedelta(0, 1800)>
```
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} | 3 | "2012-05-02T19:54:43Z" | "2015-04-13T19:09:10Z" | "2015-04-13T19:06:09Z" | MEMBER | null | `WeekOfMonth` can't readily generate, say, US Thanksgivings which are the last Thursday of November each year
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} | 1 | "2012-05-03T12:10:01Z" | "2012-05-08T19:29:13Z" | "2012-05-08T19:29:13Z" | CONTRIBUTOR | null | from pystatsmodels mailing list:
I've created a DataFrame using read_clipboard() (Pandas built from Git trunk) and am having problems reindexing unless I select rows using the [:] notation. IIRC the index was defined using 'set_index("Timestamp")'.
In [47]:
df
Out[47]:
<class 'pandas.core.frame.DataFrame'>
Index: 61507 entries, 2010-01-31 15:30:00 to 2011-11-03 09:15:00
Data columns:
Q 61507 non-null values
NTU 61507 non-null values
dtypes: float64(2)
In [50]:
df[:50000000]['Q'] < 14
Out[50]:
time
2010-01-31 15:30:00 True
2010-01-31 15:45:00 True
2010-01-31 16:00:00 True
2010-01-31 16:15:00 False
2010-01-31 16:30:00 False
<<snip; all fine here>>
In [51]:
## df['Q'] < 14
AttributeError Traceback (most recent call last)
/home/will/Dropbox/PhD/AI_general/Semblance/<ipython-input-51-2f53055c9488> in <module>()
----> 1 q_and_turb_df['Q'] < 14
/usr/lib/python2.7/dist-packages/IPython/core/displayhook.pyc in **call**(self, result)
236 self.start_displayhook()
237 self.write_output_prompt()
--> 238 format_dict = self.compute_format_data(result)
239 self.write_format_data(format_dict)
240 self.update_user_ns(result)
/usr/lib/python2.7/dist-packages/IPython/core/displayhook.pyc in compute_format_data(self, result)
148 MIME type representation of the object.
149 """
--> 150 return self.shell.display_formatter.format(result)
151
152 def write_format_data(self, format_dict):
/usr/lib/python2.7/dist-packages/IPython/core/formatters.pyc in format(self, obj, include, exclude)
124 continue
125 try:
--> 126 data = formatter(obj)
127 except:
128 # FIXME: log the exception
/usr/lib/python2.7/dist-packages/IPython/core/formatters.pyc in **call**(self, obj)
445 type_pprinters=self.type_printers,
446 deferred_pprinters=self.deferred_printers)
--> 447 printer.pretty(obj)
448 printer.flush()
449 return stream.getvalue()
/usr/lib/python2.7/dist-packages/IPython/lib/pretty.pyc in pretty(self, obj)
352 if callable(obj_class._repr_pretty_):
353 return obj_class._repr_pretty_(obj, self, cycle)
--> 354 return _default_pprint(obj, self, cycle)
355 finally:
356 self.end_group()
/usr/lib/python2.7/dist-packages/IPython/lib/pretty.pyc in _default_pprint(obj, p, cycle)
472 if getattr(klass, '__repr__', None) not in _baseclass_reprs:
473 # A user-provided repr.
--> 474 p.text(repr(obj))
475 return
476 p.begin_group(1, '<')
/usr/local/lib/python2.7/dist-packages/pandas-0.8.0.dev_1ea766c-py2.7-linux-x86_64.egg/pandas/core/series.pyc in **repr**(self)
710 else fmt.print_config.max_rows)
711 if len(self.index) > max_rows:
--> 712 result = self._tidy_repr(min(30, max_rows - 4))
713 elif len(self.index) > 0:
714 result = self._get_repr(print_header=True,
/usr/local/lib/python2.7/dist-packages/pandas-0.8.0.dev_1ea766c-py2.7-linux-x86_64.egg/pandas/core/series.pyc in _tidy_repr(self, max_vals)
728 name=False)
729 result = head + '\n...\n' + tail
--> 730 return '%s\n%s' % (result, self._repr_footer())
731
732 def _repr_footer(self):
/usr/local/lib/python2.7/dist-packages/pandas-0.8.0.dev_1ea766c-py2.7-linux-x86_64.egg/pandas/core/series.pyc in _repr_footer(self)
2551
2552 def _repr_footer(self):
-> 2553 if self.index.freq is not None:
2554 freqstr = 'Freq: %s, ' % self.index.freqstr
2555 else:
AttributeError: 'Index' object has no attribute 'freq'
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https://api.github.com/repos/pandas-dev/pandas/issues/1188 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1188/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1188/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1188/events | https://github.com/pandas-dev/pandas/issues/1188 | 4,404,381 | MDU6SXNzdWU0NDA0Mzgx | 1,188 | Maybe add use_inferred_type option to Index constructor | {
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} | 1 | "2012-05-03T12:54:54Z" | "2012-05-03T13:02:41Z" | "2012-05-03T13:02:41Z" | CONTRIBUTOR | null | use inferred type info of input argument to try and be more clever about returning the right subclass, esp timeseries Index classes.
related to #1187
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} | 1 | "2012-05-03T13:59:29Z" | "2012-07-13T03:12:51Z" | "2012-07-13T03:12:51Z" | CONTRIBUTOR | null | from pydata mailing list:
"""
The problem I'm hitting is in getting setup.py to run either with build_ext --inplace or develop, so I can run 'nosetests pandas'
cc1.exe: error: unrecognized command line option '-mno-cygwin'
error: command 'gcc' failed with exit status 1
Which looks like a possible case of http://bugs.python.org/issue12641.
Some details:
I'm running python 2.7.2 on Windows 64.
mingw installed per http://wiki.cython.org/InstallingOnWindows:
C:\Users\US\pandas>gcc --version
gcc (GCC) 4.6.2
Copyright (C) 2011 Free Software Foundation, Inc.
"""
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"""
The problem I'm hitting is in getting setup.py to run either with build_ext --inplace or develop, so I can run 'nosetests pandas'
cc1.exe: error: unrecognized command line option '-mno-cygwin'
error: command 'gcc' failed with exit status 1
Which looks like a possible case of http://bugs.python.org/issue12641.
Some details:
I'm running python 2.7.2 on Windows 64.
mingw installed per http://wiki.cython.org/InstallingOnWindows:
C:\Users\US\pandas>gcc --version
gcc (GCC) 4.6.2
Copyright (C) 2011 Free Software Foundation, Inc.
"""
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} | 0 | "2012-05-03T16:25:56Z" | "2012-05-07T23:38:20Z" | "2012-05-07T23:38:20Z" | MEMBER | null | {
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https://api.github.com/repos/pandas-dev/pandas/issues/1192 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1192/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1192/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1192/events | https://github.com/pandas-dev/pandas/issues/1192 | 4,409,646 | MDU6SXNzdWU0NDA5NjQ2 | 1,192 | Think about adding Period slicing | {
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} | 1 | "2012-05-03T17:24:31Z" | "2019-12-12T14:40:44Z" | "2019-12-12T14:40:37Z" | CONTRIBUTOR | null | Get subperiods via getitem
p = Period( 2001)
p[0, 'D'] => Period('01-Jan-2001', 'D')
p[0] => Period('2001Q1', 'Q-DEC')
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} | 0 | "2012-05-04T13:12:51Z" | "2012-05-29T02:03:00Z" | "2012-05-29T02:03:00Z" | CONTRIBUTOR | null | yes the caps are intentional
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} | 0 | "2012-05-04T13:25:02Z" | "2012-05-04T14:33:21Z" | "2012-05-04T14:33:21Z" | CONTRIBUTOR | null | In [24]: df.take([1,2])
/pandas/pandas/core/common.pyc in _ensure_int32(arr)
687
688 def _ensure_int32(arr):
--> 689 if arr.dtype != np.int32:
690 arr = arr.astype(np.int32)
691 return arr
AttributeError: 'list' object has no attribute 'dtype'
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} | 0 | "2012-05-04T16:33:52Z" | "2012-05-07T14:47:10Z" | "2012-05-07T14:47:10Z" | CONTRIBUTOR | null | {
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} | [] | closed | false | null | [] | null | 3 | "2012-05-04T23:05:09Z" | "2012-05-08T01:45:05Z" | "2012-05-08T01:45:05Z" | NONE | null | Hey Wes, I think there's a bug in your implementation of standard deviation in 0.8.0.dev-7085b94. In an arbitrary dataset, if I use df.groupby(df.col).std(), I get NaNs in some records. If I use groupby([df.col]).apply(lambda x: np.std(x)), the records are correct (0.000000).
Haven't had a chance to dig into it deeper, but prima facie this seems like a bug. Let me know what other info you need.
Cheers,
Peter
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https://api.github.com/repos/pandas-dev/pandas/issues/1199 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1199/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1199/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1199/events | https://github.com/pandas-dev/pandas/issues/1199 | 4,448,196 | MDU6SXNzdWU0NDQ4MTk2 | 1,199 | Remove period multipliers | {
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} | 0 | "2012-05-07T02:37:39Z" | "2012-05-12T16:03:01Z" | "2012-05-12T16:03:01Z" | MEMBER | null | After some thought I don't think I want period multipliers. They're rigid and inflexible as they are, probably likely to lead to more trouble than they're worth right now
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} | 1 | "2012-05-07T12:32:10Z" | "2013-12-04T00:43:23Z" | "2012-05-18T19:24:50Z" | NONE | null | I'm running on Windows 7 32-bit, with Python 2.7.2. Dependencies installed, pandas cloned and built.
Here is the output when I run "nosetests pandas" :
................SS.........S...............S.................C:\Python27\lib\site-packages\scikits\statsmodels\tools\tools.py:256: FutureWarning: The default of `prepend` will be changed to True in the next release, use explicit prepend
"next release, use explicit prepend", FutureWarning)
...........S...................EEEEEE...............SS.....................C:\Users\candre4\pandas\pandas\core\index.py:441: RuntimeWarning: tp_compare didn't return -1 or -2 for exception
return self.view(np.ndarray).argsort(_args, *_kwargs)
# .........S.................................S.......................S......
## ERROR: test_line_plot_datetime_frame (pandas.tseries.tests.test_plotting.TestTSPlot)
Traceback (most recent call last):
File "C:\Users\candre4\pandas\pandas\tseries\tests\test_plotting.py", line 82, in test_line_plot_datetime_frame
_check_plot_works(df.plot, freq)
File "C:\Users\candre4\pandas\pandas\tseries\tests\test_plotting.py", line 118, in _check_plot_works
plt.savefig(PNG_PATH)
File "C:\Python27\lib\site-packages\matplotlib\pyplot.py", line 363, in savefig
return fig.savefig(_args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\figure.py", line 1084, in savefig
self.canvas.print_figure(_args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\backend_bases.py", line 1923, in print_figure
*_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\backends\backend_agg.py", line 438, in print_png
FigureCanvasAgg.draw(self)
File "C:\Python27\lib\site-packages\matplotlib\backends\backend_agg.py", line 394, in draw
self.figure.draw(self.renderer)
File "C:\Python27\lib\site-packages\matplotlib\artist.py", line 55, in draw_wrapper
draw(artist, renderer, *args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\figure.py", line 798, in draw
func(_args)
File "C:\Python27\lib\site-packages\matplotlib\artist.py", line 55, in draw_wrapper
draw(artist, renderer, *args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\axes.py", line 1946, in draw
a.draw(renderer)
File "C:\Python27\lib\site-packages\matplotlib\artist.py", line 55, in draw_wrapper
draw(artist, renderer, _args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\axis.py", line 971, in draw
tick_tups = [ t for t in self.iter_ticks()]
File "C:\Python27\lib\site-packages\matplotlib\axis.py", line 907, in iter_ticks
majorLabels = [self.major.formatter(val, i) for i, val in enumerate(majorLocs)]
File "C:\Users\candre4\pandas\pandas\tseries\plotting.py", line 812, in __call__
return Period(ordinal=int(x), freq=self.freq).strftime(fmt)
File "C:\Users\candre4\pandas\pandas\tseries\period.py", line 101, in **init**
raise ValueError("Ordinal must be positive")
ValueError: Ordinal must be positive
#
## ERROR: test_line_plot_datetime_series (pandas.tseries.tests.test_plotting.TestTSPlot)
Traceback (most recent call last):
File "C:\Users\candre4\pandas\pandas\tseries\tests\test_plotting.py", line 71, in test_line_plot_datetime_series
_check_plot_works(s.plot, s.index.freq.rule_code)
File "C:\Users\candre4\pandas\pandas\tseries\tests\test_plotting.py", line 118, in _check_plot_works
plt.savefig(PNG_PATH)
File "C:\Python27\lib\site-packages\matplotlib\pyplot.py", line 363, in savefig
return fig.savefig(_args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\figure.py", line 1084, in savefig
self.canvas.print_figure(_args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\backend_bases.py", line 1923, in print_figure
*_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\backends\backend_agg.py", line 438, in print_png
FigureCanvasAgg.draw(self)
File "C:\Python27\lib\site-packages\matplotlib\backends\backend_agg.py", line 394, in draw
self.figure.draw(self.renderer)
File "C:\Python27\lib\site-packages\matplotlib\artist.py", line 55, in draw_wrapper
draw(artist, renderer, *args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\figure.py", line 798, in draw
func(_args)
File "C:\Python27\lib\site-packages\matplotlib\artist.py", line 55, in draw_wrapper
draw(artist, renderer, *args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\axes.py", line 1946, in draw
a.draw(renderer)
File "C:\Python27\lib\site-packages\matplotlib\artist.py", line 55, in draw_wrapper
draw(artist, renderer, _args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\axis.py", line 971, in draw
tick_tups = [ t for t in self.iter_ticks()]
File "C:\Python27\lib\site-packages\matplotlib\axis.py", line 907, in iter_ticks
majorLabels = [self.major.formatter(val, i) for i, val in enumerate(majorLocs)]
File "C:\Users\candre4\pandas\pandas\tseries\plotting.py", line 812, in __call__
return Period(ordinal=int(x), freq=self.freq).strftime(fmt)
File "C:\Users\candre4\pandas\pandas\tseries\period.py", line 101, in **init**
raise ValueError("Ordinal must be positive")
ValueError: Ordinal must be positive
#
## ERROR: test_line_plot_inferred_freq (pandas.tseries.tests.test_plotting.TestTSPlot)
Traceback (most recent call last):
File "C:\Users\candre4\pandas\pandas\tseries\tests\test_plotting.py", line 88, in test_line_plot_inferred_freq
_check_plot_works(ser.plot, ser.index.inferred_freq)
File "C:\Users\candre4\pandas\pandas\tseries\tests\test_plotting.py", line 118, in _check_plot_works
plt.savefig(PNG_PATH)
File "C:\Python27\lib\site-packages\matplotlib\pyplot.py", line 363, in savefig
return fig.savefig(_args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\figure.py", line 1084, in savefig
self.canvas.print_figure(_args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\backend_bases.py", line 1923, in print_figure
*_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\backends\backend_agg.py", line 438, in print_png
FigureCanvasAgg.draw(self)
File "C:\Python27\lib\site-packages\matplotlib\backends\backend_agg.py", line 394, in draw
self.figure.draw(self.renderer)
File "C:\Python27\lib\site-packages\matplotlib\artist.py", line 55, in draw_wrapper
draw(artist, renderer, *args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\figure.py", line 798, in draw
func(_args)
File "C:\Python27\lib\site-packages\matplotlib\artist.py", line 55, in draw_wrapper
draw(artist, renderer, *args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\axes.py", line 1946, in draw
a.draw(renderer)
File "C:\Python27\lib\site-packages\matplotlib\artist.py", line 55, in draw_wrapper
draw(artist, renderer, _args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\axis.py", line 971, in draw
tick_tups = [ t for t in self.iter_ticks()]
File "C:\Python27\lib\site-packages\matplotlib\axis.py", line 907, in iter_ticks
majorLabels = [self.major.formatter(val, i) for i, val in enumerate(majorLocs)]
File "C:\Users\candre4\pandas\pandas\tseries\plotting.py", line 812, in __call__
return Period(ordinal=int(x), freq=self.freq).strftime(fmt)
File "C:\Users\candre4\pandas\pandas\tseries\period.py", line 101, in **init**
raise ValueError("Ordinal must be positive")
ValueError: Ordinal must be positive
#
## ERROR: test_line_plot_period_frame (pandas.tseries.tests.test_plotting.TestTSPlot)
Traceback (most recent call last):
File "C:\Users\candre4\pandas\pandas\tseries\tests\test_plotting.py", line 76, in test_line_plot_period_frame
_check_plot_works(df.plot, df.index.freq)
File "C:\Users\candre4\pandas\pandas\tseries\tests\test_plotting.py", line 118, in _check_plot_works
plt.savefig(PNG_PATH)
File "C:\Python27\lib\site-packages\matplotlib\pyplot.py", line 363, in savefig
return fig.savefig(_args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\figure.py", line 1084, in savefig
self.canvas.print_figure(_args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\backend_bases.py", line 1923, in print_figure
*_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\backends\backend_agg.py", line 438, in print_png
FigureCanvasAgg.draw(self)
File "C:\Python27\lib\site-packages\matplotlib\backends\backend_agg.py", line 394, in draw
self.figure.draw(self.renderer)
File "C:\Python27\lib\site-packages\matplotlib\artist.py", line 55, in draw_wrapper
draw(artist, renderer, *args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\figure.py", line 798, in draw
func(_args)
File "C:\Python27\lib\site-packages\matplotlib\artist.py", line 55, in draw_wrapper
draw(artist, renderer, *args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\axes.py", line 1946, in draw
a.draw(renderer)
File "C:\Python27\lib\site-packages\matplotlib\artist.py", line 55, in draw_wrapper
draw(artist, renderer, _args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\axis.py", line 971, in draw
tick_tups = [ t for t in self.iter_ticks()]
File "C:\Python27\lib\site-packages\matplotlib\axis.py", line 907, in iter_ticks
majorLabels = [self.major.formatter(val, i) for i, val in enumerate(majorLocs)]
File "C:\Users\candre4\pandas\pandas\tseries\plotting.py", line 812, in __call__
return Period(ordinal=int(x), freq=self.freq).strftime(fmt)
File "C:\Users\candre4\pandas\pandas\tseries\period.py", line 101, in **init**
raise ValueError("Ordinal must be positive")
ValueError: Ordinal must be positive
#
## ERROR: test_line_plot_period_series (pandas.tseries.tests.test_plotting.TestTSPlot)
Traceback (most recent call last):
File "C:\Users\candre4\pandas\pandas\tseries\tests\test_plotting.py", line 66, in test_line_plot_period_series
_check_plot_works(s.plot, s.index.freq)
File "C:\Users\candre4\pandas\pandas\tseries\tests\test_plotting.py", line 118, in _check_plot_works
plt.savefig(PNG_PATH)
File "C:\Python27\lib\site-packages\matplotlib\pyplot.py", line 363, in savefig
return fig.savefig(_args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\figure.py", line 1084, in savefig
self.canvas.print_figure(_args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\backend_bases.py", line 1923, in print_figure
*_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\backends\backend_agg.py", line 438, in print_png
FigureCanvasAgg.draw(self)
File "C:\Python27\lib\site-packages\matplotlib\backends\backend_agg.py", line 394, in draw
self.figure.draw(self.renderer)
File "C:\Python27\lib\site-packages\matplotlib\artist.py", line 55, in draw_wrapper
draw(artist, renderer, *args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\figure.py", line 798, in draw
func(_args)
File "C:\Python27\lib\site-packages\matplotlib\artist.py", line 55, in draw_wrapper
draw(artist, renderer, *args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\axes.py", line 1946, in draw
a.draw(renderer)
File "C:\Python27\lib\site-packages\matplotlib\artist.py", line 55, in draw_wrapper
draw(artist, renderer, _args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\axis.py", line 971, in draw
tick_tups = [ t for t in self.iter_ticks()]
File "C:\Python27\lib\site-packages\matplotlib\axis.py", line 907, in iter_ticks
majorLabels = [self.major.formatter(val, i) for i, val in enumerate(majorLocs)]
File "C:\Users\candre4\pandas\pandas\tseries\plotting.py", line 812, in __call__
return Period(ordinal=int(x), freq=self.freq).strftime(fmt)
File "C:\Users\candre4\pandas\pandas\tseries\period.py", line 101, in **init**
raise ValueError("Ordinal must be positive")
ValueError: Ordinal must be positive
#
## ERROR: test_tsplot (pandas.tseries.tests.test_plotting.TestTSPlot)
Traceback (most recent call last):
File "C:\Users\candre4\pandas\pandas\tseries\tests\test_plotting.py", line 59, in test_tsplot
_check_plot_works(f, s.index.freq, ax=ax, series=s)
File "C:\Users\candre4\pandas\pandas\tseries\tests\test_plotting.py", line 118, in _check_plot_works
plt.savefig(PNG_PATH)
File "C:\Python27\lib\site-packages\matplotlib\pyplot.py", line 363, in savefig
return fig.savefig(_args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\figure.py", line 1084, in savefig
self.canvas.print_figure(_args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\backend_bases.py", line 1923, in print_figure
*_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\backends\backend_agg.py", line 438, in print_png
FigureCanvasAgg.draw(self)
File "C:\Python27\lib\site-packages\matplotlib\backends\backend_agg.py", line 394, in draw
self.figure.draw(self.renderer)
File "C:\Python27\lib\site-packages\matplotlib\artist.py", line 55, in draw_wrapper
draw(artist, renderer, *args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\figure.py", line 798, in draw
func(_args)
File "C:\Python27\lib\site-packages\matplotlib\artist.py", line 55, in draw_wrapper
draw(artist, renderer, *args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\axes.py", line 1946, in draw
a.draw(renderer)
File "C:\Python27\lib\site-packages\matplotlib\artist.py", line 55, in draw_wrapper
draw(artist, renderer, _args, *_kwargs)
File "C:\Python27\lib\site-packages\matplotlib\axis.py", line 971, in draw
tick_tups = [ t for t in self.iter_ticks()]
File "C:\Python27\lib\site-packages\matplotlib\axis.py", line 907, in iter_ticks
majorLabels = [self.major.formatter(val, i) for i, val in enumerate(majorLocs)]
File "C:\Users\candre4\pandas\pandas\tseries\plotting.py", line 812, in __call__
return Period(ordinal=int(x), freq=self.freq).strftime(fmt)
File "C:\Users\candre4\pandas\pandas\tseries\period.py", line 101, in **init**
raise ValueError("Ordinal must be positive")
ValueError: Ordinal must be positive
---
Ran 1743 tests in 60.531s
FAILED (SKIP=10, errors=6)
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