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| null | 1 | 2015-08-25T10:06:07Z | 2015-08-26T00:51:11Z | 2015-08-26T00:51:01Z | NONE | null | Now it is not possible to create a `DateOffset` which would incorporate shifting by a certain number of _business_ days in addition to any other offset rules. In my view, it would be useful to include a `bdays` argument which would act similarly to `days` parameter.
NOTE: It is also impossible to create such a `DateOffset` indirectly via summation `DateOffset(hour=2) + BDay()` since `DateOffset`s are currently not summable as discussed in #10902.
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} | 1 | 2015-08-25T22:25:12Z | 2016-05-07T14:42:56Z | 2016-05-07T14:42:56Z | NONE | null | Noticed this in Pandas v0.16.1 when JSON serializing a large nested dictionary containing mixes of arrays, numpy arrays, and pandas series. Calling tolist() on numpy arrays allows the standard library JSON serializer to serialize them, but this is not the case for pandas.
numpy.array.tolist converts all of the array's contents to native Python datatypes. For compatibility with numpy, pandas.Series.tolist should also convert its contents to native Python datatypes. Currently, the list returned by pandas contains elements which have types like numpy.int64 or numpy.float64.
For example:
```
>>> from pandas
>>> import numpy
>>> np_array = numpy.array([1, 2, 3])
>>> pd_series = pandas.Series([1,2,3])
>>> np_list = np_array.tolist()
>>> pd_list = pd_series.tolist()
>>> type(np_list[0])
<class 'int'>
>>> type(pd_list[0])
<class 'numpy.int64'>
```
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| null | 2 | 2015-08-26T13:11:00Z | 2015-08-26T13:19:52Z | 2015-08-26T13:13:50Z | CONTRIBUTOR | null | In [#928](https://github.com/pydata/pandas/issues/928), support for list of boolean/binary values was added to control ascending/descending when sorting on multiple columns.
This doesn't work with `sortlevel`, but nor does it raise an error. This seems potentially "dangerous" to me.
```
In [23]: levels = list('ABC')
In [24]: length = 10
In [25]: df = pd.DataFrame(dict(alpha=np.random.choice(levels, length),
....: num=np.random.random(length), vals=np.random.random(length)))
In [26]: df = df.set_index(['alpha', 'num'])
In [27]: df
Out[27]:
vals
alpha num
C 0.101632 0.566144
0.334399 0.242917
A 0.741676 0.807468
C 0.406477 0.756441
A 0.225341 0.214034
C 0.623214 0.696784
A 0.576516 0.181786
0.509289 0.144184
B 0.510504 0.155965
A 0.093283 0.356627
In [28]: # Sort reverse alphabetically. But then `num` is backwards!
In [29]: df.sortlevel(['alpha', 'num'], ascending=False)
Out[29]:
vals
alpha num
C 0.623214 0.696784
0.406477 0.756441
0.334399 0.242917
0.101632 0.566144
B 0.510504 0.155965
A 0.741676 0.807468
0.576516 0.181786
0.509289 0.144184
0.225341 0.214034
0.093283 0.356627
In [30]: # Attempt to sort reverse alphabetically with `num` sorting forwards:
In [31]: df.sortlevel(['alpha', 'num'], ascending=[False, True])
Out[31]:
vals
alpha num
A 0.093283 0.356627
0.225341 0.214034
0.509289 0.144184
0.576516 0.181786
0.741676 0.807468
B 0.510504 0.155965
C 0.101632 0.566144
0.334399 0.242917
0.406477 0.756441
0.623214 0.696784
```
As you can see, this last `sortlevel` does not behave as expected. I'd expect to see `alpha` sorted descending (Z - A) and `num` sorted ascending (0 - 1).
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} | 5 | 2015-08-26T13:29:48Z | 2015-09-01T11:19:54Z | 2015-09-01T11:19:53Z | CONTRIBUTOR | null | skipping these tests as the link seems t have changed: https://github.com/pydata/pandas/commit/a3c4b59f1190035df98e70635291fce0dc04b91d
but would like to replace these with something new testing the same functionaility.
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} | 5 | 2015-08-26T14:23:21Z | 2015-09-01T11:02:13Z | 2015-09-01T11:02:13Z | NONE | null | The following code in pandas 0.16.2 demonstrates the problem:
``` python
>>> import pandas as pd
>>> df = pd.DataFrame({'y': pd.Series([2]), 'z': pd.Series([3])})
>>> df
y z
0 2 3
>>> df.insert(0, 'x', 1)
>>> df.diff(axis=1)
x y z
0 NaN NaN 1
```
The following workaround produces the expected result:
``` python
>>> df.T.diff().T
x y z
0 NaN 1 1
```
Versions:
``` python
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.18-400.1.1.el5
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: C
pandas: 0.16.2
nose: None
Cython: None
numpy: 1.9.2
sphinx: None
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.4
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 7 | 2015-08-26T20:10:34Z | 2015-11-18T20:17:47Z | 2015-11-18T20:17:47Z | NONE | null | closes #7879 by changing API (filter_observations=True now deprecated and replaced with dropna=False)
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} | 1 | 2015-08-27T02:26:36Z | 2015-08-27T11:19:39Z | 2015-08-27T03:16:19Z | CONTRIBUTOR | null | ``` -------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
groupby_ngroups_10000_value_counts | 11.4880 | 6698.5627 | 0.0017 |
groupby_ngroups_100_value_counts | 1.3397 | 69.8457 | 0.0192 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [d587be8] : PERF: imporves performance in SeriesGroupBy.value_counts
Base [a3c4b59] : TST: pythonxs link seems to have changed in test_html.py, skip tests
```
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} | 6 | 2015-08-27T09:50:17Z | 2015-11-10T15:29:29Z | 2015-11-10T15:29:21Z | CONTRIBUTOR | null | This is the result from
https://github.com/pydata/pandas/issues/10898
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} | 5 | 2015-08-27T10:31:02Z | 2016-04-22T15:20:13Z | 2016-04-22T15:20:13Z | NONE | null | Now I don't know if this is known or the desired behaviour but when I try to read certain rows from a large file that uses "~" (tilde) as a quotechar and use skiprows at the same time, the parser screws up as follows:
Note: I use "" in the output even though that isn't shown, if I didn't the markup would become messed up - sorry...
``` python
>>> pd.read_csv(StringIO.StringIO('a,b,c\r~a\n b~,~e\n d~,~f\n f~\r1,2,~12\n 13\n 14~'), quotechar="~", skiprows=range(1,2) )
a b c
"b~" "e\n d" "f\n f"
1 2 "12\n 13\n 14" NaN
```
while the output I wish to get would be in this artificial case:
```
a b c
0 1 2 "12\n 13\n 14"
```
it seems when skipping rows, the parser ignores custom quotation - which in this case is undesired from my point of view.
EDIT: It might well be that in the quoted texts newlines are not always \n but sometimes also \r.
EDIT2 (31.8.):
The lineterminator fix fails as far as I can see with the following example:
``` python
>>> a = StringIO.StringIO('Text,url\r~example\r sentence\r one~,url1\r~example\n sentence\n two~,url2')
>>> pd.read_csv(a, quotechar="~", skiprows=range(1,2), lineterminator='\r' )
Text url
0 sentence NaN
1 "one~" url1
2 "example\n sentence\n two" url2
```
The problem is that there is a "text"-column in the csv with html-formatted textblocks as content. However, there is no saying what kind of newline the creators of the html used originally and the textblocks stem from different sources.
I might also add that it respects the quoting perfectly if one does not use "skiprows".
versioninfo:
```
python: 2.7.10.final.0
python-bits: 64
OS: Windows
OS-release: 7
pandas: 0.16.2
nose: None
Cython: None
numpy: 1.9.2
scipy: 0.16.0
statsmodels: None
IPython: 4.0.0
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.4
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.0
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 1 | 2015-08-27T15:36:23Z | 2015-08-28T23:28:06Z | 2015-08-28T23:28:06Z | NONE | null | Hi, Thanks for all efforts for the great tool!
I've found a strange behavior like this...
``` python
In [1]: a = pd.DataFrame([[1,2,3],[4,5,6]], columns=['A','B','C'], index=['X','Y'])
In [2]: a
Out[2]:
A B C
X 1 2 3
Y 4 5 6
In [3]: b = a.pop('B')
In [4]: a
Out[4]:
A C
X 1 3
Y 4 6
In [5]: b
Out[5]:
X 2
Y 5
Name: B, dtype: int64
In [6]: b += 1
In [7]: a # Why does a popped column "B" appear again?
Out[7]:
A C B
X 1 3 3
Y 4 6 6
In [8]: b
Out[8]:
X 3
Y 6
Name: B, dtype: int64
```
Is it a bug of pandas?
This behavior can be avoided by using `b = b+1` instead of implace operations.
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| null | 1 | 2015-08-27T19:09:15Z | 2015-09-01T14:40:55Z | 2015-09-01T14:40:55Z | NONE | null | Pull request so solve issue #10897
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} | 4 | 2015-08-27T21:10:48Z | 2015-08-28T12:24:43Z | 2015-08-28T12:22:23Z | CONTRIBUTOR | null | Works on 0.16.2, fails on git master.
``` python
import pandas as pd
import numpy as np
index = pd.date_range(start='20120102', periods=24*60*3, freq='T').values
index = index[::2]
s = pd.Series(np.arange(len(index), dtype='f8'), index=pd.Index(index))
expected = s.resample('30T', how='nunique')
```
Traceback:
``` python
Traceback (most recent call last):
File "test.py", line 7, in <module>
expected = s.resample('30T', how='nunique')
File "/Users/jcrist/Code/pandas/pandas/core/generic.py", line 3368, in resample
return sampler.resample(self).__finalize__(self)
File "/Users/jcrist/Code/pandas/pandas/tseries/resample.py", line 82, in resample
rs = self._resample_timestamps()
File "/Users/jcrist/Code/pandas/pandas/tseries/resample.py", line 287, in _resample_timestamps
result = grouped.aggregate(self._agg_method)
File "/Users/jcrist/Code/pandas/pandas/core/groupby.py", line 2339, in aggregate
return getattr(self, func_or_funcs)(*args, **kwargs)
File "/Users/jcrist/Code/pandas/pandas/core/groupby.py", line 2564, in nunique
sorter = np.lexsort((val, ids))
ValueError: all keys need to be the same shape
```
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| null | 6 | 2015-08-28T01:51:58Z | 2015-08-29T02:05:06Z | 2015-08-29T02:04:51Z | NONE | null | Version : 0.16.2
OS : Mac
What I think is when use .str need first change compulsory change dtype to str, but in my situation, pandas change my number data to NA.
And when I use .astype('str') first, I got the output I need, but I think this may may somebody confused and make some mistake without .astype('str') .
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} | 1 | 2015-08-28T03:06:21Z | 2015-08-29T00:17:38Z | 2015-08-28T03:08:45Z | CONTRIBUTOR | null | This reverts commit d40627398cf78347f5a49fa060bad1c40514908a, reversing
changes made to bd804aa75ac9b7a55c00101dbea01571cbdfd068.
@jorisvandenbossche reverting so that the docs build
pls rebsumit when you can
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} | 0 | 2015-08-28T03:14:24Z | 2015-08-28T18:18:58Z | 2015-08-28T18:18:58Z | CONTRIBUTOR | null | no need to factorize integers when dropping duplicates.
master
```
In [1]: np.random.seed(1234)
In [2]: df = DataFrame({'A' : np.random.randint(0,10000,size=1000000)})
In [3]: %timeit df.drop_duplicates()
10 loops, best of 3: 36.9 ms per loop
```
PR
```
In [2]: df = DataFrame({'A' : np.random.randint(0,10000,size=1000000)})
In [3]: %timeit df.drop_duplicates()
10 loops, best of 3: 21.6 ms per loop
```
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} | 0 | 2015-08-28T03:24:08Z | 2015-08-29T12:39:08Z | 2015-08-28T12:22:23Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/10914
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} | 2 | 2015-08-28T13:34:45Z | 2015-08-29T00:10:30Z | 2015-08-29T00:03:43Z | MEMBER | null | Closes #10792.
Because using `deprecate_kwargs` hides original impl, used ugly workaround for `GroupBy`...
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| null | 7 | 2015-08-28T17:09:50Z | 2015-09-02T10:39:19Z | 2015-08-28T17:57:15Z | NONE | null | Hello,
Would it be possible to add complex function operation on DataFrame ?
Function like np.sin, np.abs works properly on Pandas DataFrame containing complex numbers and
returns a DataFrame (as expected).
However, np.real, np.imag, np.angle operation return an numpy.ndarray array instead of DataFrame...
``` python
import pandas as pd
import numpy as np
arr = pd.DataFrame(np.random.randn(2, 3) + 1j*np.random.randn(2, 3))
print(type(np.abs(arr))) # <class 'pandas.core.frame.DataFrame'>
print(type(np.angle(arr))) # <class 'numpy.ndarray'>
```
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} | 1 | 2015-08-28T18:36:05Z | 2015-08-28T23:28:06Z | 2015-08-28T23:28:06Z | CONTRIBUTOR | null | closes #10912
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| null | 4 | 2015-08-28T19:03:58Z | 2017-07-26T19:57:19Z | 2015-08-28T20:18:43Z | NONE | null | This is similar to #9697, which was fixed in 0.16.1. I give a (very) slightly modified example here to show some related behavior which is at least inconsistent and should probably be handled cleanly.
It's not entirely clear to me what the desired behavior is in this case; it's possible that transform should not work here at all, since it spits out unexpected values. But at minimum it seems like it should do the same thing no matter how I invoke it below.
Example:
``` python
import numpy as np
df = pd.DataFrame({'col1':[1,1,2,2], 'col2':[1,2,3,np.nan])
#Let's try grouping on 'col2', which contains a NaN.
# Works and gives arguably reasonable results, with one unpredictable value
df.groupby('col2').transform(sum)['col1']
# Throws an unhelpful error
df.groupby('col2')['col1'].transform(sum)
```
Error is similar to the one encountered in the previous issue:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-54-2d4b83df6487> in <module>()
----> 1 df.groupby('col2')['col1'].transform(sum)
/usr/local/lib/python2.7/dist-packages/pandas/core/groupby.pyc in transform(self, func, *args, **kwargs)
2442 cyfunc = _intercept_cython(func)
2443 if cyfunc and not args and not kwargs:
-> 2444 return self._transform_fast(cyfunc)
2445
2446 # reg transform
/usr/local/lib/python2.7/dist-packages/pandas/core/groupby.pyc in _transform_fast(self, func)
2488 values = self._try_cast(values, self._selected_obj)
2489
-> 2490 return self._set_result_index_ordered(Series(values))
2491
2492 def filter(self, func, dropna=True, *args, **kwargs):
/usr/local/lib/python2.7/dist-packages/pandas/core/groupby.pyc in _set_result_index_ordered(self, result)
503 result = result.sort_index()
504
--> 505 result.index = self.obj.index
506 return result
507
/usr/local/lib/python2.7/dist-packages/pandas/core/generic.pyc in __setattr__(self, name, value)
2159 try:
2160 object.__getattribute__(self, name)
-> 2161 return object.__setattr__(self, name, value)
2162 except AttributeError:
2163 pass
/usr/local/lib/python2.7/dist-packages/pandas/lib.so in pandas.lib.AxisProperty.__set__ (pandas/lib.c:42548)()
/usr/local/lib/python2.7/dist-packages/pandas/core/series.pyc in _set_axis(self, axis, labels, fastpath)
273 object.__setattr__(self, '_index', labels)
274 if not fastpath:
--> 275 self._data.set_axis(axis, labels)
276
277 def _set_subtyp(self, is_all_dates):
/usr/local/lib/python2.7/dist-packages/pandas/core/internals.pyc in set_axis(self, axis, new_labels)
2217 if new_len != old_len:
2218 raise ValueError('Length mismatch: Expected axis has %d elements, '
-> 2219 'new values have %d elements' % (old_len, new_len))
2220
2221 self.axes[axis] = new_labels
ValueError: Length mismatch: Expected axis has 3 elements, new values have 4 elements
```
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| null | 4 | 2015-08-28T20:23:45Z | 2015-08-28T21:52:58Z | 2015-08-28T20:33:58Z | CONTRIBUTOR | null | According to the documentation `pandas.Series.dot` returns a `pandas.Series` or a scalar value. Certainly, the output type is depending on the type and dimension of the first positional argument of the `pandas.Series.dot` function. The following example outputs a value of type `numpy.ndarray` using pandas (0.16.2):
``` python
In [0]: pd.Series(np.ones(3)).dot(np.eye(3))
out[0]: array([ 1., 1., 1.])
```
Even though, the result is plausible (array of scalars), the documentation is inconsistent on this. This only applies to `pandas.Series.dot`.
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} | 8 | 2015-08-29T00:18:09Z | 2016-02-12T17:40:30Z | 2016-01-07T01:35:44Z | MEMBER | null | Reverts pydata/pandas#10916
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} | 0 | 2015-08-29T02:03:26Z | 2015-08-29T13:12:39Z | 2015-08-29T13:12:39Z | CONTRIBUTOR | null | closes #9442
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} | 10 | 2015-08-29T06:37:53Z | 2015-09-03T19:14:47Z | 2015-09-02T11:49:29Z | CONTRIBUTOR | null | One tiny part of #10213
```
import timeit
setup_seq = '''
import pandas
import numpy
numpy.random.seed(1234)
x = numpy.random.randn(10000000)
a = x.copy()
b = x.copy()
def f(s):
pandas.algos.kth_smallest(s, 500000)
def seq():
f(a)
f(b)
'''
setup_parallel = '''
import pandas
import numpy
import threading
numpy.random.seed(1234)
x = numpy.random.randn(10000000)
a = x.copy()
b = x.copy()
def f(s):
pandas.algos.kth_smallest(s, 500000)
def parallel():
thread1 = threading.Thread(target=f, args=(a,))
thread2 = threading.Thread(target=f, args=(b,))
thread1.start()
thread2.start()
thread1.join()
thread2.join()
'''
print(min(timeit.repeat(stmt='seq()', setup=setup_seq, repeat=100, number=1)))
print(min(timeit.repeat(stmt='parallel()', setup=setup_parallel, repeat=100, number=1)))
```
On master
```
0.15268295999976544
0.15424678300041705
```
On branch
```
0.1544521670002723
0.08813380599985976
```
Testing of `nsmallest`/`nlargest`/`median` (I don't think `median` calls `kth_smallest` though)
```
import pandas, numpy
from pandas.util.testing import test_parallel
n = 1000000
k = 50000
numpy.random.seed(1234)
s = pandas.Series(numpy.random.randn(n))
def f():
s.nlargest(k)
def seq():
f()
f()
@test_parallel(num_threads=2)
def g():
f()
```
Master `nsmallest`
```
In [2]: %timeit f()
10 loops, best of 3: 42.4 ms per loop
In [3]: %timeit g()
10 loops, best of 3: 79.9 ms per loop
In [4]: %timeit seq()
10 loops, best of 3: 84.9 ms per loop
```
Branch `nsmallest`
```
In [10]: %timeit f()
10 loops, best of 3: 42.8 ms per loop
In [11]: %timeit g()
10 loops, best of 3: 68.6 ms per loop
In [12]: %timeit seq()
10 loops, best of 3: 91.2 ms per loop
```
Master `nlargest`
```
In [2]: %timeit f()
10 loops, best of 3: 47.5 ms per loop
In [3]: %timeit g()
10 loops, best of 3: 86 ms per loop
In [4]: %timeit seq()
```
Branch `nlargest`
```
In [10]: %timeit f()
10 loops, best of 3: 48.1 ms per loop
In [11]: %timeit g()
10 loops, best of 3: 71 ms per loop
In [12]: %timeit seq()
10 loops, best of 3: 95.7 ms per loop
```
Master `median`
```
In [2]: %timeit f()
100 loops, best of 3: 15.4 ms per loop
In [3]: %timeit g()
10 loops, best of 3: 20.7 ms per loop
In [4]: %timeit seq()
10 loops, best of 3: 30.7 ms per loop
```
Branch `median`
```
In [12]: %timeit f()
100 loops, best of 3: 15 ms per loop
In [13]: %timeit g()
10 loops, best of 3: 21.5 ms per loop
In [14]: %timeit seq()
10 loops, best of 3: 30 ms per loop
```
Results are pretty in line with expectations -- `nsmallest` does quite a bit more than `kth_smallest`, such as copying and indexing.
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} | 8 | 2015-08-29T08:40:42Z | 2015-09-01T19:25:40Z | 2015-08-29T22:42:36Z | CONTRIBUTOR | null | @jorisvandenbossche As you requested in #10849, here's the current state of affairs. The changes are:
- First patch:
- Fix the translation script so that functions defined in the vbench `setup` string get defined as classmethods and called appropriately. This fixes most failing tests.
- Some backwards-compatibility fixes around imports. Raising `NotImplementedError` inside `setup()` is equivalent to `SkipTest`, so I've tried to do that in a couple necessary cases (such as things using `test_parallel`)
- Speaking of `test_parallel`, this required a bit of hackery to support not-existing. I implemented a no-op decorator by that name so that the actual definition of test cases wouldn't fail. We simply raise `NotImplementedError` rather than test without it, however.
- Second patch:
- Support Python 3, the changes are essentially what I mentioned elsewhere.
- `from pandas_vb_common import *` -> `from .pandas_vb_common import *`
- Replace `xrange` with `range` and add `from pandas.compat import range` in `pandas_vb_common`
- Replace `string.uppercase` with `string.ascii_uppercase`
- Third patch
- `asv` instructions in `contributing.rst`, largely lifted from the vbench description. The current examples are minimalist, but most comparable to what vbench outputs.
- Fourth patch:
- Re-run the translation script. I think some other additions may be included in the first patch due to having run it a few times while making changes.
cc @jreback @TomAugspurger
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} | 8 | 2015-08-29T13:22:00Z | 2017-04-05T02:06:34Z | 2015-09-01T19:18:15Z | CONTRIBUTOR | null | WIP still
Closes https://github.com/pydata/pandas/issues/10748
I have to run for now, but will pick this up later today.
I think I'm missing a few in the tests, since the warning is showing up in a bunch of places (there's a way to convert those to errors for testing right?)
I had to refactor a couple function that were setting `._categories` directly instead of using `Categorical._set_categories`. I kept that in a separate commit. Could do a bit more refactoring with the `validate_categories` stuff, but that can be separate.
And I need to figure out the proper `stacklevel` for this warning. I think I used 3 for now.
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} | 25 | 2015-08-29T16:31:30Z | 2015-09-11T13:15:00Z | 2015-09-01T11:01:58Z | CONTRIBUTOR | null | Fixed issue #10907
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} | 6 | 2015-08-29T18:16:32Z | 2015-09-05T22:20:28Z | 2015-09-05T22:20:25Z | CONTRIBUTOR | null | xref #9651
closes #8588
Adds test to ensure the sort of a target object is preserved within `groupby()` groups, modifies docs to make it clear sort is preserved within groups.
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} | 5 | 2015-08-30T09:14:49Z | 2016-07-21T16:01:08Z | 2015-10-18T14:02:48Z | NONE | null | closes #10143
This implementation is inspired by os.walk and follows the interface as much as possible.
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| null | 2 | 2015-08-30T09:34:04Z | 2015-11-19T21:49:31Z | 2015-08-30T10:03:24Z | CONTRIBUTOR | null | I'm at the EuroSciPy 2015 sprint, I just found this issue when trying to update my Pandas development environment, I installed a fresh conda development environment and then got the following error.
> > > import pandas as pd
> > > Traceback (most recent call last):
> > > File "/Users/peadarcoyle/OpenSource/pandas/pandas/**init**.py", line 7, in <module>
> > > from pandas import hashtable, tslib, lib
> > > ImportError: dlopen(/Users/peadarcoyle/OpenSource/pandas/pandas/hashtable.so, 2): Symbol not found: _PyCObject_Type
> > > Referenced from: /Users/peadarcoyle/OpenSource/pandas/pandas/hashtable.so
> > > Expected in: dynamic lookup
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/peadarcoyle/OpenSource/pandas/pandas/**init**.py", line 13, in <module>
"extensions first.".format(module))
ImportError: C extension: dlopen(/Users/peadarcoyle/OpenSource/pandas/pandas/hashtable.so, 2): Symbol not found: _PyCObject_Type
Referenced from: /Users/peadarcoyle/OpenSource/pandas/pandas/hashtable.so
Expected in: dynamic lookup
not built. If you want to import pandas from the source directory, you may need to run 'python setup.py build_ext --inplace' to build the C extensions first.
I tried running the same python setup.py build_ext --inplace command.
My python version is Python 3.4.3 |Continuum Analytics, Inc.| (default, Mar 6 2015, 12:07:41)
[GCC 4.2.1 (Apple Inc. build 5577)] on darwin
Has anyone seen an error like this before?
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| null | 5 | 2015-08-30T10:32:25Z | 2015-09-01T08:55:40Z | 2015-09-01T08:55:40Z | CONTRIBUTOR | null | I made a few alterations to the consistent imports in the merging.rst file.
Does anyone have any objections to what I did?
I notice a few problems with the rebase though first - that's 12 commits rather than one.
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} | 5 | 2015-08-30T11:09:01Z | 2015-11-02T12:07:08Z | 2015-11-02T12:07:07Z | NONE | null | tm.equalContent does not raise an exception, it returns a Bool. Therefore these tests need wrapping in self.assertTrue()
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} | 2 | 2015-08-30T11:17:01Z | 2015-08-31T08:18:13Z | 2015-08-31T01:03:13Z | CONTRIBUTOR | null | closes #10886
I didn't notice warnings or errors associated with those modifications when building the docs.
No more `FutureWarning` either (except in a the v. 0.13 what's new, which is legit)...
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closes #10817
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| null | 1 | 2015-08-30T14:01:20Z | 2015-08-30T14:03:02Z | 2015-08-30T14:02:58Z | CONTRIBUTOR | null | I fixed my mess up from earlier.
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} | 4 | 2015-08-30T14:41:37Z | 2015-09-02T20:14:50Z | 2015-09-02T11:51:18Z | CONTRIBUTOR | null | Fixes [GH10856](https://github.com/pydata/pandas/issues/10856).
``` Python
>>> pd.DataFrame({'a':0.1}, columns=['b'])
ValueError: If using all scalar values, you must pass an index
```
Trying to raise this error was slightly trickier than I anticipated - this was the only way that didn't break existing tests. If no index is passed to the constructor, `extract_index` is called to check whether the dictionary contacts only scalar values (and raises the ValueError if so).
This check now happens _prior_ to preselecting any columns. If people are happy with this approach I can write tests for the PR.
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} | 6 | 2015-08-30T14:48:18Z | 2015-09-01T09:17:48Z | 2015-08-31T11:07:48Z | CONTRIBUTOR | null | closes #9402
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} | 2 | 2015-08-30T22:43:26Z | 2015-08-31T23:37:24Z | 2015-08-31T12:18:36Z | CONTRIBUTOR | null | Addresses #10875, when `Index` is converted via `to_datetime`, `to_timedelta`
part of #9862 master issue
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} | 10 | 2015-08-31T02:11:16Z | 2015-09-05T16:46:12Z | 2015-09-05T16:36:53Z | CONTRIBUTOR | null | BUG: closes bug in Series.count when index has nulls
``` python
In [4]: ts
Out[4]:
a 1 0
2 1
b 2 2
NaN 3
c 1 4
2 5
dtype: int64
In [5]: ts.count(level=1)
Out[5]:
1 2
2 4 # <<< BUG!
dtype: int64
In [6]: from string import ascii_lowercase
In [7]: np.random.seed(2718281)
In [8]: n = 1 << 21
In [9]: df = DataFrame({
...: '1st':np.random.choice(list(ascii_lowercase), n),
...: '2nd':np.random.randint(0, n // 100, n),
...: '3rd':np.random.randn(n).round(3)})
In [10]: df.loc[np.random.choice(n, n // 10), '3rd'] = np.nan
In [11]:
In [11]: gr = df.groupby(['1st', '2nd'])['3rd']
In [12]: %timeit gr.count()
The slowest run took 6.67 times longer than the fastest. This could mean that an intermediate result is being cached
1 loops, best of 3: 86.4 ms per loop
In [13]: %timeit gr.count()
10 loops, best of 3: 87 ms per loop
```
on branch:
``` python
In [5]: ts.count(level=1)
Out[5]:
1 2
2 3
NaN 1
dtype: int64
...
In [12]: %timeit gr.count()
The slowest run took 12.29 times longer than the fastest. This could mean that an intermediate result is being cached
1 loops, best of 3: 43.1 ms per loop
In [13]: %timeit gr.count()
10 loops, best of 3: 43.5 ms per loop
```
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| null | 2 | 2015-08-31T12:18:00Z | 2015-08-31T12:27:56Z | 2015-08-31T12:26:45Z | NONE | null | According to the [documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.pivot_table.html) of the `pivot_table` command, the `columns` attribute could be a grouper object ("columns : a column, Grouper, array which has the same length as data, or list of them")
However when I try to do so, I get an error.
```
x = pd.DataFrame({'key1': [1,1,2,2]*2, 'key2': ['a','a','a','b']*2, 'values': [1,2,3,4]*2, 'index': [1,1,1,1,2,2,2,2]})
x.pivot_table(columns=x.groupby(['key1','key2']), values='values', index='index')
```
see Error below.
Moreover, when I have a dataset of 10,000 rows with 800 unique combination of keys, the pivot_table doesn't return.
```
Traceback (most recent call last):
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 700, in mean
return self._cython_agg_general('mean')
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 2560, in _cython_agg_general
new_items, new_blocks = self._cython_agg_blocks(how, numeric_only=numeric_only)
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 2598, in _cython_agg_blocks
result, _ = self.grouper.aggregate(values, how, axis=agg_axis)
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 1497, in aggregate
out_shape = (self.ngroups,) + values.shape[1:]
File "pandas\src\properties.pyx", line 34, in pandas.lib.cache_readonly.__get__ (pandas\lib.c:41917)
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 1384, in ngroups
return len(self.result_index)
File "pandas\src\properties.pyx", line 34, in pandas.lib.cache_readonly.__get__ (pandas\lib.c:41917)
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 1398, in result_index
return MultiIndex(levels=[ping.group_index for ping in self.groupings],
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 1398, in <listcomp>
return MultiIndex(levels=[ping.group_index for ping in self.groupings],
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 2029, in group_index
self._make_labels()
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 2034, in _make_labels
labels, uniques = algos.factorize(self.grouper, sort=self.sort)
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\algorithms.py", line 135, in factorize
labels = table.get_labels(vals, uniques, 0, na_sentinel)
File "pandas\hashtable.pyx", line 815, in pandas.hashtable.PyObjectHashTable.get_labels (pandas\hashtable.c:14073)
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\generic.py", line 663, in __hash__
' hashed'.format(self.__class__.__name__))
TypeError: 'DataFrame' objects are mutable, thus they cannot be hashed
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3035, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-18-6e9f5e8775c2>", line 1, in <module>
x.pivot_table(columns=x.groupby(['key1','key2']), values='values', index='index')
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\tools\pivot.py", line 111, in pivot_table
agged = grouped.agg(aggfunc)
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 685, in agg
return self.aggregate(func, *args, **kwargs)
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 2637, in aggregate
return getattr(self, arg)(*args, **kwargs)
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 706, in mean
return self._python_agg_general(f)
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 1121, in _python_agg_general
return self._python_apply_general(f)
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 675, in _python_apply_general
self.axis)
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 1270, in apply
splitter = self._get_splitter(data, axis=axis)
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 1256, in _get_splitter
comp_ids, _, ngroups = self.group_info
File "pandas\src\properties.pyx", line 34, in pandas.lib.cache_readonly.__get__ (pandas\lib.c:41917)
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 1365, in group_info
comp_ids, obs_group_ids = self._get_compressed_labels()
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 1373, in _get_compressed_labels
all_labels = [ping.labels for ping in self.groupings]
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 1373, in <listcomp>
all_labels = [ping.labels for ping in self.groupings]
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 2023, in labels
self._make_labels()
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 2034, in _make_labels
labels, uniques = algos.factorize(self.grouper, sort=self.sort)
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\algorithms.py", line 135, in factorize
labels = table.get_labels(vals, uniques, 0, na_sentinel)
File "pandas\hashtable.pyx", line 815, in pandas.hashtable.PyObjectHashTable.get_labels (pandas\hashtable.c:14073)
File "C:\Users\hanans\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\generic.py", line 663, in __hash__
' hashed'.format(self.__class__.__name__))
TypeError: 'DataFrame' objects are mutable, thus they cannot be hashed
```
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| null | 2 | 2015-08-31T12:45:27Z | 2018-10-21T01:31:41Z | 2018-10-21T01:31:36Z | NONE | null | I would like to have the group index of each instance.
I managed to do something like:
```
labels = {idx:(groupKey) for groupKey,idxList in g.groups.items() for idx in idxList}
```
But I wish it would be part of pandas for other to use. How do I do that?
Maybe this function already exists and I am not aware of it?
I couldn't find any GroupBy object methods documentation online
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} | 8 | 2015-08-31T13:48:14Z | 2015-10-20T17:48:06Z | 2015-10-20T17:48:06Z | CONTRIBUTOR | null | I think `s.cat.density` is appropriate here
```
In [1]: s = Series(list('aabbcccc')).astype('category')
In [2]: 100*len(s.cat.categories)/len(s)
Out[2]: 37
```
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} | 1 | 2015-08-31T14:15:05Z | 2018-07-06T20:21:08Z | 2017-07-10T21:07:40Z | NONE | null | After running
```
import numpy as np
randn = np.random.randn
from pandas import *
```
the following code creates a panel:
```
## Create pseudo panel data ##
numindivs = 2 # Number of individuals
numwaves = 5 # Number of time periods (waves)
# Create panel object
wp = Panel(np.random.randint(12, size=(numindivs, numwaves, 4)), minor_axis=['background', 'lnH', 'F', 'T'])
# Generate random fixed background variable for each indiv: N(2,1)
wp.ix[:,:,'background'] = np.tile(randn(numindivs)+2,(numwaves,1))
# Generate lnF and lnT
wp.ix[:,:,'lnF'] = np.log(wp[:,:,'F']+1)
wp.ix[:,:,'lnT'] = np.log(wp[:,:,'T']+1)
```
Yet, the following code, which is identical except for changing the contents of the 'background' column in the penultimate section, generates an error ("ValueError: could not broadcast input array from shape (2) into shape (5)"):
```
## Create pseudo panel data ##
numindivs = 2 # Number of individuals
numwaves = 5 # Number of time periods (waves)
# Create panel object
wp = Panel(np.random.randint(12, size=(numindivs, numwaves, 4)), minor_axis=['background', 'lnH', 'F', 'T'])
# Generate lnF and lnT
wp.ix[:,:,'lnF'] = np.log(wp[:,:,'F']+1)
wp.ix[:,:,'lnT'] = np.log(wp[:,:,'T']+1)
```
This is extremely odd behavior. I am new to pandas; a contributor on StackOverflow suggested it "probably has something to do with how dtypes are getting coerced when you assign", hence the title of the issue. Thanks!
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} | 1 | 2015-08-31T14:29:06Z | 2015-09-01T12:22:24Z | 2015-09-01T11:05:56Z | MEMBER | null | Closes #10878.
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} | 1 | 2015-08-31T17:02:11Z | 2015-09-03T13:25:32Z | 2015-09-03T13:25:32Z | CONTRIBUTOR | null | NumPy array of complex64 is implicitly converted to complex128 when used as data for DataFrame:
``` python
>>> a=np.random.randn(10).astype(np.complex64)
>>> a.dtype
dtype('complex64')
>>> pd.DataFrame({'a': a}).a.dtype
dtype('complex128')
>>>
```
The problem does not affect Series:
``` python
In [7]: Series(a).dtype
Out[7]: dtype('complex64')
```
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} | 6 | 2015-08-31T17:20:37Z | 2015-09-05T22:26:15Z | 2015-09-05T22:26:10Z | CONTRIBUTOR | null | closes #4893
Extends the eval parser to accept calling math functions.
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| null | 40 | 2015-08-31T17:31:07Z | 2020-09-06T18:47:08Z | null | CONTRIBUTOR | null | As `pandas` approaches its 1.0 release, I would like to raise a concern about one aspect of the `pandas` architecture that I think is a threat to its widespread adoption: how `pandas` works with copies and views when setting values (what I will refer to here as the `SettingWithCopyWarning` issue).
The summary of my concern is the following:
```
1. SettingWithCopyWarning is a threat to data integrity
2. It is unreasonable to expect the average user to avoid a `SettingWithCopyWarning` issue, as doing
so requires keeping track of the plethora of factors that determine what generates a copy and what
generates a view.
2a. Views made sense in `numpy`, but not in `pandas`
2b. Chain-indexing is a much more subtle problem than suggested in the `pandas` docs.
3. Given (1) and (2), data integrity in `pandas` relies on users noticing a non-exception warning in the
flow of their output.
4. Even aside from the threat to data integrity, this behavior is unpythonic, and likely to frustrate
alienate lots of potential users of `pandas`.
5. I think solutions can be found that would have only limited effects on performance for the majority of
users
```
Taking each of these in turn:
(1) **`SettingWithCopyWarning` is a threat to data integrity**
The fact that assignment operations do different things depending on whether the target is a view or a copy has already been recognized as a threat to the predictability of `pandas`. Indeed, the reason a warning was added is because users were consistently asking why `pandas` was doing un-anticipated things when `SettingWithCopyWarning` came into play.
(2) **It is unreasonable to expect the average user to avoid a `SettingWithCopyWarning` issue, as doing so requires keeping track of the plethora of factors that determine what generates a copy and what generates a view.**
Figuring out when a function will return a copy and when it will return a view in `pandas` is not simple. Indeed, the `pandas` documentation doesn't even try to explain when each will occur (link http://pandas-docs.github.io/pandas-docs-travis/indexing.html?highlight=views#indexing-view-versus-copy):
> The reason for having the SettingWithCopy warning is this. Sometimes when you slice an array you will simply get a view back, which means you can set it no problem. However, even a single dtyped array can generate a copy if it is sliced in a particular way. A multi-dtyped DataFrame (meaning it has say float and object data), will almost always yield a copy. Whether a view is created is dependent on the memory layout of the array."
(2a) **Views made sense in `numpy`, but not in `pandas`**
Views entered the pandas lexicon via `numpy`. But the reason they were so useful in `numpy` is that they were _predictable_ because `numpy` arrays are always single-typed. In `pandas`, no such consistent, predictable behavior exists.
(2b) **Chain-indexing is a much more subtle problem than suggested in the `pandas` docs**
At first glance, the `pandas` docs suggest that the `SettingWithCopyWarning` is easily avoided by avoiding chain-indexing or using `.loc`. This, I fear, is misleading for two reasos. First, the canonical example of chain indexing in the docs (`dfmi['one']['second'] = value`) seems to suggest that one can avoid chain indexing by just not falling into the trap of this kind of double slicing. The problem, however, is that these slices need not appear near one another. I know I've had trouble with code of the form:
```
df2 = dfmi['one']
# Lots of intermediate code that doesn't change dfmi or df2
df2['second'] = 5
```
Moreover, using `.loc` only solves this problem if one notices the chained indexing and attempts to fix it in one place. Just consistently using `.loc[]` (for example, in both the first and second problematic slicings above) would not solve the problem.
(3) **Given (1) and (2), data integrity in `pandas` relies on users noticing a non-exception warning in the flow of their output.**
This seems really problematic. If a users is printing values as they go along (which CS developers may not do, but interactive casual users often do to monitor the progress of their code), these warnings are easy to miss. And that seems very dangerous.
(4) **Even aside from the threat to data integrity, this behavior is unpythonic, and likely to frustrate alienate lots of potential users of `pandas`**
I suspect I come to `pandas` from a different perspective than many developers. I am an economist and political scientist who has gotten deeper and deeper into computer science over the past several years for instrumental purposes. As a result, I think I have a pretty good sense of how applied users approach something like `pandas`, and I can just see this peculiarity of `pandas` driving this class of users batty. I've taken a year of computer science course work and am one of the most technically trained social scientists I know, and it drives me batty.
It's also unpythonic -- the behavior of basic operators (like `=`) should not depend on the type of columns in a `DataFrame`. Python 3 changed the behavior of the `/` operator because it was felt the behavior of `/` should not do depend on whether you were working with `float`s or `int`s. Since whether functions return a view or copy is in large part (but not exclusively) a function of whether a `DataFrame` is single or multi-typed (which occurs when some columns are `floats` and some are `ints`), we have the same problem -- the operation of a basic operation (`=`) depends on data types.
In other words, if one of the aims of `pandas` is to essentially surplant `R` among applied data scientists, then I think this is a major threat to achieving that goal.
(5) **I think solutions can be found that would have only limited effects on performance for the majority of users**
`pandas` uses views because they're so damn fast, so I understand the reluctance to drop them, but I think there are ways to minimize the performance hit. Obviously more talented developers with a better understanding of the innards of `pandas` may have better suggestions, but hopefully this can get the ball rolling.
```
* Solution 1: Move `views` to the background.
When a user tries to look at an object and it's possible to return a view, do so. But just never let a
user assign values to a view -- any time an attempt is made to set on a view, convert it to a copy
before executing the assignment. Views will still operate in the background providing high speed
data access in read-only environments, but users don't have to worry about what they're dealing
with. Users who *really* need access to views can work with `numpy` arrays.
(I would also note that given the unpredictability of when one will get a view or copy, it's not clear to
me how anyone can write code that takes advantage of the behavior of views, which makes me
doubt there are many people for whom this would seriously impact performance or written code, but
I'd be happy to hear if anyone has workarounds!)
* Solution 2: Create an indexer that always returns copies (like .take(), but for axis labels).
This would at least give users who want to avoid views all together a way to do so without littering
their code with `.copy()`s.
* Solution 3: Change the `SettingWithCopyWarning` to an exception by default.
This is currently a setting, but the global default is for it to be a warning. Personally, I still don't like
this solution since, as a result of (2) this means `pandas` will now raise exceptions unpredictably, but
at least data integrity will be preserved.
```
`pandas` is a brilliant tool, and a huge improvement on everything else out there. I am eager to see it becomes the standard not only among python users, but among data analysts more broadly. Hopefully, by addressing this issue, we can help make this happen.
With that in mind, I would like to suggest the need for two things:
1. A discussion about the desirability of the various solutions proposed above
2. Volunteers to help implement this change. Unfortunately, I don't have the programming sophistication or knowledge of `pandas` internals to take this on alone, and this is likely too big an undertaking for any one individual anyone, so a team is likely to be necessary.
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| null | 6 | 2015-08-31T20:42:01Z | 2018-08-24T14:44:18Z | 2018-08-24T14:44:18Z | NONE | null | I'm working with [vega-lite](https://github.com/uwdata/vega-lite) in a pipeline that goes from pandas to vega-lite. I would like to take a pandas DataFrame, output it as json, and incorporate it into a vega-lite JSON structure. Unfortunately none of pandas's DataFrame.to_json() "orient" options matches what vega-lite expects, so in the interest of better interoperability, I would like to request a 6th "orient" option, which I am calling 'zipped' but you could rename to anything.
Here is JSON for a vega-lite bar chart: https://github.com/uwdata/vega-lite#simple-bar-chart The "data" field is:
```
"data": {
"values": [
{"x":"A", "y":28}, {"x":"B", "y":55}, {"x":"C", "y":43},
{"x":"D", "y":91}, {"x":"E", "y":81}, {"x":"F", "y":53},
{"x":"G", "y":19}, {"x":"H", "y":87}, {"x":"I", "y":52}
]
},
```
I believe this is sort-of a dict(zip()) over a pandas DataFrame: every row in the DataFrame becomes one dict in the output list-of-dicts, with the keys in the dict equivalent to the column headings and the values in the dict equivalent to the values in the row associated with their respective column.
I am certainly aware that I could write code to manipulate an existing to_json() output, but it would seem this would be a useful output format for all.
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} | 5 | 2015-08-31T22:57:54Z | 2021-10-16T14:47:43Z | 2021-10-16T14:47:43Z | MEMBER | null | Indexing like `x[..., 0]` is a convenient way to indexing the last dimension of an array with NumPy. `...` gets turned into an `Ellipsis` object, which NumPy expands to the appropriate number of full slice objects.
It would be nice to support this in pandas, too. Currently, attempting to do this throws KeyError:
```
>>> df[...]
KeyError: Ellipsis
```
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} | 6 | 2015-09-01T01:52:22Z | 2015-09-01T13:22:24Z | 2015-09-01T12:47:11Z | CONTRIBUTOR | null | closes #10583
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} | 1 | 2015-09-01T02:53:10Z | 2015-09-01T13:23:33Z | 2015-09-01T11:03:07Z | CONTRIBUTOR | null | See issue #10906
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| null | 1 | 2015-09-01T11:43:29Z | 2015-09-01T11:53:21Z | 2015-09-01T11:52:48Z | NONE | null | This does not work
``` python
df = pd.DataFrame(np.arange(20).reshape(4,5)).astype(float)
df.apply(lambda x:pd.cut(x,2))
```
Here the result is pd.Series
This works
``` python
df = pd.DataFrame(np.arange(20).reshape(4,5))
df.apply(lambda x:pd.cut(x,2))
```
Here the result is a dataframe
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} | 5 | 2015-09-01T12:46:37Z | 2015-09-01T19:39:57Z | 2015-09-01T19:38:53Z | CONTRIBUTOR | null | closes #10583
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| null | 1 | 2015-09-01T13:52:22Z | 2015-09-01T14:23:14Z | 2015-09-01T14:22:44Z | NONE | null | Hello,
I tried to use the clip function on a basic Panel but it seems that the axis management is not working as expected. I believe this is an update to issue https://github.com/pydata/pandas/issues/8344
```
import numpy as np
import pandas as pd
data = np.random.randn(3, 4, 5)
panel = pd.Panel(data)
panel.clip(0, 1)
```
If I run the script with pandas 0.16.2 on python 2.7.10 and 3.4.3, I'm getting the following error:
```
.../pandas/core/generic.pyc in clip(self, lower, upper, out, axis)
3052 result = self
3053 if lower is not None:
-> 3054 result = result.clip_lower(lower, axis)
3055 if upper is not None:
3056 result = result.clip_upper(upper, axis)
.../pandas/core/generic.pyc in clip_lower(self, threshold, axis)
3103 raise ValueError("Cannot use an NA value as a clip threshold")
3104
-> 3105 subset = self.ge(threshold, axis=axis) | isnull(self)
3106 return self.where(subset, threshold, axis=axis)
3107
TypeError: f() got an unexpected keyword argument 'axis'
```
**Thanks a lot for all your efforts!**
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https://api.github.com/repos/pandas-dev/pandas/issues/10962 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10962/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10962/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10962/events | https://github.com/pandas-dev/pandas/pull/10962 | 104,296,627 | MDExOlB1bGxSZXF1ZXN0NDM4OTI3ODE= | 10,962 | BUG: DataFrame subplot with duplicated columns output incorrect result | {
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} | 2 | 2015-09-01T15:52:12Z | 2015-09-05T02:14:56Z | 2015-09-05T02:14:54Z | MEMBER | null | When `DataFrame` has duplicated column name, each subplot will contain all the lines with same name.
```
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.rand(5, 5), columns=list('aaaaa'))
df.plot(subplots=True)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/10963 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10963/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10963/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10963/events | https://github.com/pandas-dev/pandas/issues/10963 | 104,330,874 | MDU6SXNzdWUxMDQzMzA4NzQ= | 10,963 | to_sql UnicodeEncodeError | {
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| null | 3 | 2015-09-01T18:50:04Z | 2015-09-03T09:20:55Z | 2015-09-03T07:50:16Z | NONE | null | writing a dataframe to sql gives a UnicodeEncodeError, even though sys.defaultencoding is set to "utf-8" and the mysql database is also using utf-8 as default collation. Any ideas? Thanks!
## In [6]: df1.to_sql(name='webshoptracks2', con=engine)
UnicodeEncodeError Traceback (most recent call last)
<ipython-input-6-4e38b4ac8c1d> in <module>()
----> 1 df1.to_sql(name='webshoptracks2', con=engine)
C:\Python27\lib\site-packages\pandas\core\generic.pyc in to_sql(self, name, con,
flavor, schema, if_exists, index, index_label, chunksize, dtype)
980 self, name, con, flavor=flavor, schema=schema, if_exists=if_
exists,
981 index=index, index_label=index_label, chunksize=chunksize,
--> 982 dtype=dtype)
983
984 def to_pickle(self, path):
C:\Python27\lib\site-packages\pandas\io\sql.pyc in to_sql(frame, name, con, flav
or, schema, if_exists, index, index_label, chunksize, dtype)
547 pandas_sql.to_sql(frame, name, if_exists=if_exists, index=index,
548 index_label=index_label, schema=schema,
--> 549 chunksize=chunksize, dtype=dtype)
550
551
C:\Python27\lib\site-packages\pandas\io\sql.pyc in to_sql(self, frame, name, if_
exists, index, index_label, schema, chunksize, dtype)
1186 schema=schema, dtype=dtype)
1187 table.create()
-> 1188 table.insert(chunksize)
1189 # check for potentially case sensitivity issues (GH7815)
1190 if name not in self.engine.table_names(schema=schema or self.met
a.schema):
C:\Python27\lib\site-packages\pandas\io\sql.pyc in insert(self, chunksize)
726
727 chunk_iter = zip(*[arr[start_i:end_i] for arr in data_li
st])
--> 728 self._execute_insert(conn, keys, chunk_iter)
729
730 def _query_iterator(self, result, chunksize, columns, coerce_float=T
rue,
C:\Python27\lib\site-packages\pandas\io\sql.pyc in _execute_insert(self, conn, k
eys, data_iter)
701 def _execute_insert(self, conn, keys, data_iter):
702 data = [dict((k, v) for k, v in zip(keys, row)) for row in data_
iter]
--> 703 conn.execute(self.insert_statement(), data)
704
705 def insert(self, chunksize=None):
C:\Python27\lib\site-packages\sqlalchemy\engine\base.pyc in execute(self, object
, _multiparams, *_params)
912 type(object))
913 else:
--> 914 return meth(self, multiparams, params)
915
916 def _execute_function(self, func, multiparams, params):
C:\Python27\lib\site-packages\sqlalchemy\sql\elements.pyc in _execute_on_connect
ion(self, connection, multiparams, params)
321
322 def _execute_on_connection(self, connection, multiparams, params):
--> 323 return connection._execute_clauseelement(self, multiparams, para
ms)
324
325 def unique_params(self, _optionaldict, *_kwargs):
C:\Python27\lib\site-packages\sqlalchemy\engine\base.pyc in _execute_clauseeleme
nt(self, elem, multiparams, params)
1008 compiled_sql,
1009 distilled_params,
-> 1010 compiled_sql, distilled_params
1011 )
1012 if self._has_events or self.engine._has_events:
C:\Python27\lib\site-packages\sqlalchemy\engine\base.pyc in _execute_context(sel
f, dialect, constructor, statement, parameters, *args)
1144 parameters,
1145 cursor,
-> 1146 context)
1147
1148 if self._has_events or self.engine._has_events:
C:\Python27\lib\site-packages\sqlalchemy\engine\base.pyc in _handle_dbapi_except
ion(self, e, statement, parameters, cursor, context)
1342 )
1343 else:
-> 1344 util.reraise(*exc_info)
1345
1346 finally:
C:\Python27\lib\site-packages\sqlalchemy\engine\base.pyc in _execute_context(sel
f, dialect, constructor, statement, parameters, *args)
1114 statement,
1115 parameters,
-> 1116 context)
1117 elif not parameters and context.no_parameters:
1118 if self.dialect._has_events:
C:\Python27\lib\site-packages\sqlalchemy\dialects\mysql\mysqldb.pyc in do_execut
emany(self, cursor, statement, parameters, context)
93
94 def do_executemany(self, cursor, statement, parameters, context=None
):
---> 95 rowcount = cursor.executemany(statement, parameters)
96 if context is not None:
97 context._rowcount = rowcount
C:\Python27\lib\site-packages\pymysql\cursors.pyc in executemany(self, query, ar
gs)
153 return self._do_execute_many(q_prefix, q_values, q_postfix,
args,
154 self.max_stmt_length,
--> 155 self._get_db().encoding)
156
157 self.rowcount = sum(self.execute(query, arg) for arg in args)
C:\Python27\lib\site-packages\pymysql\cursors.pyc in _do_execute_many(self, pref
ix, values, postfix, args, max_stmt_length, encoding)
179 if isinstance(v, text_type):
180 if PY2:
--> 181 v = v.encode(encoding)
182 else:
183 v = v.encode(encoding, 'surrogateescape')
UnicodeEncodeError: 'latin-1' codec can't encode characters in position 433-438:
ordinal not in range(256)
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} | 11 | 2015-09-01T20:32:15Z | 2015-09-05T20:24:26Z | 2015-09-05T17:22:03Z | CONTRIBUTOR | null | This PR implements more flexible Excel (spreadsheet) reader tests. The work was originally part of the larger but unsuccessful PR #9070.
This PR includes:
- Excel reader base class tests to facilitate adding the similar tests for other readers
- not all Excel file formats (xls, xlsm, xlsx) had a corresponding test file. This PR adds the missing ones. Consequently, all Excel data files are now tested against all 3 Excel file formats.
- rename `test` to `test1` (for xls, xlsm, xlsx extensions) for consistency with `test2..4` test data files. This also means that before merging (but after Travis has ran the tests) the URL of Excel test file `test1.*` has to be changed from `davidovitch/pandas` to `pydata/pandas` in `pandas/io/tests/test_excel.py` at line [378](https://github.com/davidovitch/pandas/blob/master/pandas/io/tests/test_excel.py#L378).
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} | 1 | 2015-09-01T20:49:25Z | 2015-09-02T08:52:40Z | 2015-09-02T08:52:40Z | CONTRIBUTOR | null | Fixes https://github.com/pydata/pandas/issues/10700
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https://api.github.com/repos/pandas-dev/pandas/issues/10966 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10966/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10966/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10966/events | https://github.com/pandas-dev/pandas/issues/10966 | 104,371,685 | MDU6SXNzdWUxMDQzNzE2ODU= | 10,966 | Unpickling of pre 0.15 gzip-compressed objects not possible | {
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| null | 6 | 2015-09-01T23:05:06Z | 2015-09-02T12:39:17Z | 2015-09-02T11:38:04Z | NONE | null | I pickled and gzip_compressed DataFrames in a tuple pre-0.15
```
with gzip.open('/tmp/test.pklz', 'wb') as datafile:
pickle.dump((df1,df2), datafile)
```
Pre-0.15 i would load them again like:
```
with gzip.open('/tmp/test.pklz', 'rb') as datafile:
df1, df2 = pickle.load(datafile)
```
Now after updating pandas, I can't load them back anyme:
```
>> TypeError: _reconstruct: First argument must be a sub-type of ndarray
```
If I directly try pd.read_pickle, it can't decrompress the file properly:
```
df1, df2= pd.read_pickle('/tmp/test.pklz')
>> KeyError: '\x1f'
```
Reading the decompressed file does not work too:
```
with gzip.open('/tmp/test.pklz', 'rb') as datafile:
df1, df2 = pd.read_pickle(datafile)
>> TypeError: coercing to Unicode: need string or buffer, GzipFile found
```
Is there any solution for this? This seems to be a serious issue for backwards compatibility if you need to downgrade pandas again to get the data back if there is no backup in other formats.
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} | 33 | 2015-09-02T00:25:28Z | 2016-09-15T13:39:47Z | 2015-09-09T12:06:09Z | CONTRIBUTOR | null | closes #4679
xref #10564
Output of `to_excel` should now be fully round-trippable with `read_excel` with the
right combination of `index_col` and `header`.
To make the semantics match `read_csv`, an index column name (`has_index_names=True`) is
always assumed if something is passed to `index_col` - this should be non-breaking;
if there are no names, it will be just filled to `None` as before.
```
In [7]: df = pd.DataFrame([[1,2,3,4], [5,6,7,8]],
...: columns = pd.MultiIndex.from_product([['foo','bar'],['a','b']],
...: names = ['col1', 'col2']),
...: index = pd.MultiIndex.from_product([['j'], ['l', 'k']],
...: names = ['i1', 'i2']))
In [8]: df
Out[8]:
col1 foo bar
col2 a b a b
i1 i2
j l 1 2 3 4
k 5 6 7 8
In [9]: df.to_excel('test.xlsx')
In [10]: df = pd.read_excel('test.xlsx', header=[0,1], index_col=[0,1])
In [11]: df
Out[11]:
col1 foo bar
col2 a b a b
i1 i2
j l 1 2 3 4
k 5 6 7 8
```
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} | 1 | 2015-09-02T01:19:44Z | 2015-09-02T13:09:57Z | 2015-09-02T08:51:21Z | CONTRIBUTOR | null | Function declaration contains default values for many of the parameters but the default values are not specified in the doc.
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| null | 2 | 2015-09-02T03:25:15Z | 2015-09-04T03:07:57Z | 2015-09-04T03:07:52Z | CONTRIBUTOR | null | See issue #10559. Done testing after the changes.
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} | 5 | 2015-09-02T09:52:29Z | 2015-10-10T00:23:39Z | 2015-10-10T00:23:39Z | CONTRIBUTOR | null | In some case a DataFrame exported to excel present some bad values.
It's is not a problem of Excel reading (the data inside the sheet1.xml of the .xlsx file is also incorrect).
The same DataFrame exported to ".csv" is correct.
The problem could be "solved" by renaming the column header as [col-1, col-2,...]. Maybe an encoding problem ?
The issue is that there is no warning/error during the export. It's very easy to miss it.
To reproduce:
```
import pandas as pd
df = pd.read_pickle('problematic_df.pkl')
df.to_excel('problematic_df.xlsx')
df.to_csv('problematic_df.csv')
```
with the file available here: https://drive.google.com/file/d/0Bzz_ZaP_wS_HMFdlMkVzaTR0cjA/view?usp=sharing
Note that the content of cell M14 is different in both file (at least when run on my computer)
Using:
- Python 3.4.3 |Anaconda 2.3.0 (64-bit)
- pandas 0.16.2
- Windows 7 64 bits
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} | 1 | 2015-09-02T12:48:34Z | 2015-09-03T15:18:09Z | 2015-09-03T15:18:09Z | MEMBER | null | With latest master:
```
In [1]: from pandas import *
In [7]: p = period_range('2013',periods=10)
In [8]: p
Out[8]:
PeriodIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
'2013-01-05', '2013-01-06', '2013-01-07', '2013-01-08',
'2013-01-09', '2013-01-10'],
dtype='int64', freq='D')
In [10]: Series(p)
Out[10]: -----------------------------------------------------------------------
----
AttributeError Traceback (most recent call last)
C:\Anaconda\envs\devel\lib\site-packages\IPython\core\formatters.pyc in __call__
(self, obj)
693 type_pprinters=self.type_printers,
694 deferred_pprinters=self.deferred_printers)
--> 695 printer.pretty(obj)
696 printer.flush()
697 return stream.getvalue()
...
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\format.pyc in _format_strings(s
elf)
2107
2108 def _format_strings(self):
-> 2109 values = np.array(self.values.to_native_types(), dtype=object)
2110 formatter = self.formatter or (lambda x: '%s' % x)
2111 fmt_values = [formatter(x) for x in values]
AttributeError: 'numpy.ndarray' object has no attribute 'to_native_types'
```
cc @sinhrks (I was trying to fix the stacklevel for the FutureWarnings, but saw this error, possibly related to your recent Period work?)
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} | 4 | 2015-09-02T18:00:54Z | 2017-02-27T21:38:08Z | 2017-02-27T21:38:08Z | CONTRIBUTOR | null | xref #11444, #13046 for addtl tests
``` python
In [27]: df = pd.DataFrame({'a': np.random.randint(0, 5, 365), 'b': pd.date_range('2015-01-01', periods=365, freq='D')})
In [28]: df.head()
Out[28]:
a b
0 3 2015-01-01
1 3 2015-01-02
2 4 2015-01-03
3 2 2015-01-04
4 4 2015-01-05
In [29]: df.groupby('a').b.transform(lambda x: x.dt.dayofweek - x.dt.dayofweek.mean()).head()
Out[29]:
0 1970-01-01 00:00:00.000000000
1 1970-01-01 00:00:00.000000001
2 1970-01-01 00:00:00.000000001
3 1970-01-01 00:00:00.000000002
4 1969-12-31 23:59:59.999999997
Name: b, dtype: datetime64[ns]
```
I expected a float. No idea how difficult this will be so I marked it for 0.18. I won't have time to get to it any earlier, but if someone else wants to...
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| null | 34 | 2015-09-02T20:56:46Z | 2020-09-06T18:46:29Z | 2015-11-02T12:05:08Z | CONTRIBUTOR | null | closes #10954
- deprecates the option `mode.chained_assignment` as its now unused (it shows the deprecation if you are explicity using it, more friendly this way).
- deprecate `NDFrame.is_copy` property
- remove `SettingWithCopyWarning` entirely
TODO:
- [ ] some systematic testing of the chaining might help (e.g. iterate thru the `iloc/loc/[]`, chain, and set various values including dtype changes
- [ ] huge note explaining the rationale & change structure
semantics:
Intermediate assignment trigger copy-on-write and do not propogate.
```
In [1]: df = DataFrame({'col1':[1,2], 'col2':[3,4]})
In [2]: intermediate = df.loc[1:1,]
In [3]: intermediate['col1'] = -99
In [4]: intermediate
Out[4]:
col1 col2
1 -99 4
In [5]: df
Out[5]:
col1 col2
0 1 3
1 2 4
```
Chained assignments _always_ work!
```
In [6]: df = DataFrame({'col1':[1,2], 'col2':[3,4]})
In [7]: df.loc[1:1,]['col1'] = -99
In [8]: df
Out[8]:
col1 col2
0 1 3
1 -99 4
```
Even true with cross-sections that change dtype
```
In [1]: df = DataFrame({'col1':[1,2], 'col2':[3,4]})
In [2]: df.loc[1]['col2'] = 'foo'
In [3]: df
Out[3]:
col1 col2
0 1 3
1 2 foo
```
except for a really egregious case, which will raise a `SettingWithCopyError` (maybe I could even fix this....)
```
In [10]: df.loc[1:1]['col2'].replace(10,5,inplace=True)
SettingWithCopyError: chained indexing detected, you can fix this ......
```
This is an invalid assignment. I suppose we _could_ make it work, but we currently havent' allowed the `.dt` to be used as a setitem accessor
```
In [9]: s = Series(pd.date_range('20130101',periods=3))
In [12]: s.dt.hour[0] = 5
SettingImmutableError: modifications to a property of a datetimelike object are not supported and are discarded. Change values on the original.
```
cc @nickeubank
cc @JanSchulz
cc @ellisonbg
cc @CarstVaartjes
@shoyer
special thanks to @JanSchulz for some reference checking code :)
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} | 1 | 2015-09-02T21:57:36Z | 2015-09-03T20:39:24Z | 2015-09-03T15:18:09Z | MEMBER | null | Closes #10971. It is caused by #10718.
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} | 3 | 2015-09-02T22:47:14Z | 2015-09-03T18:16:19Z | 2015-09-03T13:25:23Z | CONTRIBUTOR | null | Addresses [GH10952](https://github.com/pydata/pandas/issues/10952).
When passing an array with complex64 dtype to the DataFrame constructor, that array will no longer be automatically promoted to the complex128 dtype (different complex dtypes are now allowed).
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} | 2 | 2015-09-03T00:07:23Z | 2015-09-04T22:18:51Z | 2015-09-04T22:18:42Z | CONTRIBUTOR | null | I thought it might be nice to have some examples showing how to pass custom functions and functions with additional keywords.
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| null | 8 | 2015-09-03T07:41:36Z | 2021-04-18T23:32:35Z | null | NONE | null | apologies if this has already been raised but couldn't find an answer.
from the docs :
DateOffsets can be created to move dates forward a given number of valid dates. For example, Bday(2) can be added to a date to move it two business days forward. If the date does not start on a valid date, first it is moved to a valid date.
pd.Timestamp('20150830')+BDay(0) == pd.Timestamp('20150830')+BDay(1) is True
it doesn't move it first to a valid date and the code seem to confirm that. not sure what's the intended behaviour here but would be great to clarify. thanks in advance.
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} | 10 | 2015-09-03T09:26:51Z | 2016-07-29T11:03:11Z | 2015-09-03T13:16:38Z | NONE | null | I found some unexpected behaviour when looking for the group minima of a datetime column containing null values. It appears that when the `min` method is called on a `SeriesGroupBy` of dtype `datetime64` with null values, the values are cast to floats before the minima are computed. Consider the following:
```
df = pd.DataFrame({'datetime': pd.date_range('20150903', periods=4),
'groups': ['a', 'b']*2})
df.loc[0, 'datetime'] = pd.NaT
In [357]: df.groupby('groups').datetime.min()
Out[357]:
groups
a NaN
b 1.441325e+18
Name: datetime, dtype: float64
```
The `float` value of `pd.NaT` is `-2^63` and so it is determined to be the minimum of any group which contains it. The expected behaviour would be for null values to be ignored and the minima of the non-null values returned as `datetime64` objects. Interestingly, the `max` method seems to work as expected;
```
In [367]: df.groupby('groups').datetime.max()
Out[367]:
groups
a 2015-09-05
b 2015-09-06
Name: datetime, dtype: datetime64[ns]
```
The `min` method of the `DataFrameGroupBy` object is kind of half way between; it fails to ignore the null-values and gives `pd.NaT` as the min of any group which contains it but it does return the correct data type:
```
In [369]: df.groupby('groups').min()
Out[369]:
datetime
groups
a NaT
b 2015-09-04
```
I tried to trace the source of the error and I got as far as the call to
```
self.grouper.aggregate(obj.value, how='min')
```
where 'obj' is a (the only) set of values in self._iterate_slices. Within `self.grouper.aggregate` the lines
```
if is_datetime_or_timedelta_dtype(values.dtype):
values = values.view('int64')
```
and
```
if com.is_integer_dtype(result):
if len(result[result == tslib.iNaT]) > 0:
result = result.astype('float64')
result[result == tslib.iNaT] = np.nan
```
seem relevant. It might be worth noting that `self.aggregate(lambda x: np.min(x, axis=self.axis)` has the desired output while `self.aggregate(np.min)` does not. Also, changing the definition of the `min` method to
```
min = _groupby_function('min', 'min', np.min, numeric_only=True)
```
fixes this particular problem.
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} | 3 | 2015-09-03T13:38:13Z | 2015-09-08T00:32:36Z | 2015-09-08T00:32:36Z | CONTRIBUTOR | null | Regression from 0.16.2 to 0.16.1, but fixed in master, doced here: https://github.com/pydata/pandas/commit/2748e82ae99511f23f0f5134332706bde0798ef5
xref #10979 #9345
Ideally want identify where this happened, and if we have enough test coverage.
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} | 0 | 2015-09-03T14:43:46Z | 2015-09-04T15:34:30Z | 2015-09-04T15:34:30Z | CONTRIBUTOR | null | xref #10976 broke some tests on windows (these prob just need some `_ensure_int64(..)` before passing the pointers in, the `test_rsample_group_info/test_series_groupby_nunique` are from a previous issue; these just need `np.array(...., dtype='int64')` in the comparison functions (as windows makes these `np.int32` by default)
cc @behzadnouri
```
[pandas] C:\Users\Jeff Reback\pandas>
[pandas] C:\Users\Jeff Reback\pandas>nosetests --exe -A "not slow and not network and not disabled" build\lib.win-amd64-2.7\pandas
C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\io\data.py:33: FutureWarning:
The pandas.io.data module is moved to a separate package (pandas-datareader) and will be removed from pandas in a future version.
After installing the pandas-datareader package (https://github.com/pydata/pandas-datareader), you can change the import ``from pandas.io import data, wb`` to ``from pandas_datareader import data, wb``
.
FutureWarning)
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.....................................................................................................................................................C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7
\pandas\core\algorithms.py:149: RuntimeWarning: tp_compare didn't return -1 or -2 for exception
sorter = uniques.argsort()
C:\Miniconda\envs\pandas\lib\site-packages\numpy\core\fromnumeric.py:799: RuntimeWarning: tp_compare didn't return -1 or -2 for exception
a.sort(axis, kind, order)
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....................................................................................................................S.SSSSSC:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tests\test_frame.p
y:7709: RuntimeWarning: Cannot compare type 'Timestamp' with type 'str', sort order is undefined for incomparable objects
colcorr = a.corrwith(b, axis=0)
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..
======================================================================
ERROR: test_custom_grouper (pandas.tseries.tests.test_resample.TestResample)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_resample.py", line 54, in test_custom_grouper
g._cython_agg_general(f)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1129, in _cython_agg_general
result, names = self.grouper.aggregate(obj.values, how)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1566, in aggregate
result = self._aggregate(result, counts, values, agg_func, is_numeric)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1611, in _aggregate
agg_func(result, counts, values, comp_ids)
File "pandas\src\generated.pyx", line 6743, in pandas.algos.group_add_float64 (pandas\algos.c:118485)
def group_add_float64(ndarray[float64_t, ndim=2] out,
ValueError: Buffer dtype mismatch, expected 'int64_t' but got 'long'
======================================================================
ERROR: test_how_lambda_functions (pandas.tseries.tests.test_resample.TestResample)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_resample.py", line 850, in test_how_lambda_functions
bar_exp = ts.resample('M', how='std')
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\generic.py", line 3486, in resample
return sampler.resample(self).__finalize__(self)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 82, in resample
rs = self._resample_timestamps()
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 287, in _resample_timestamps
result = grouped.aggregate(self._agg_method)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 2325, in aggregate
return getattr(self, func_or_funcs)(*args, **kwargs)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 766, in std
return np.sqrt(self.var(ddof=ddof))
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 775, in var
return self._cython_agg_general('var')
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1129, in _cython_agg_general
result, names = self.grouper.aggregate(obj.values, how)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1566, in aggregate
result = self._aggregate(result, counts, values, agg_func, is_numeric)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1611, in _aggregate
agg_func(result, counts, values, comp_ids)
File "pandas\src\generated.pyx", line 6988, in pandas.algos.group_var_float64 (pandas\algos.c:121664)
def group_var_float64(ndarray[float64_t, ndim=2] out,
ValueError: Buffer dtype mismatch, expected 'int64_t' but got 'long'
======================================================================
ERROR: test_ohlc_5min (pandas.tseries.tests.test_resample.TestResample)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_resample.py", line 504, in test_ohlc_5min
label='right')
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\generic.py", line 3486, in resample
return sampler.resample(self).__finalize__(self)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 82, in resample
rs = self._resample_timestamps()
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 287, in _resample_timestamps
result = grouped.aggregate(self._agg_method)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 2325, in aggregate
return getattr(self, func_or_funcs)(*args, **kwargs)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 816, in ohlc
lambda x: x._cython_agg_general('ohlc'))
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 2689, in _apply_to_column_groupbys
return func(self)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 816, in <lambda>
lambda x: x._cython_agg_general('ohlc'))
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1129, in _cython_agg_general
result, names = self.grouper.aggregate(obj.values, how)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1566, in aggregate
result = self._aggregate(result, counts, values, agg_func, is_numeric)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1611, in _aggregate
agg_func(result, counts, values, comp_ids)
File "pandas\src\generated.pyx", line 7195, in pandas.algos.group_ohlc_float64 (pandas\algos.c:124745)
def group_ohlc_float64(ndarray[float64_t, ndim=2] out,
ValueError: Buffer dtype mismatch, expected 'int64_t' but got 'long'
======================================================================
ERROR: test_resample_axis1 (pandas.tseries.tests.test_resample.TestResample)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_resample.py", line 539, in test_resample_axis1
result = df.resample('M', axis=1)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\generic.py", line 3486, in resample
return sampler.resample(self).__finalize__(self)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 82, in resample
rs = self._resample_timestamps()
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 287, in _resample_timestamps
result = grouped.aggregate(self._agg_method)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 2786, in aggregate
return getattr(self, arg)(*args, **kwargs)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 738, in mean
return self._python_agg_general(f)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1147, in _python_agg_general
result, counts = self.grouper.agg_series(obj, f)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1862, in agg_series
return grouper.get_result()
File "pandas\src\reduce.pyx", line 274, in pandas.lib.SeriesBinGrouper.get_result (pandas\lib.c:34306)
raise
File "pandas\src\reduce.pyx", line 260, in pandas.lib.SeriesBinGrouper.get_result (pandas\lib.c:34160)
res = self.f(cached_typ)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1141, in <lambda>
f = lambda x: func(x, *args, **kwargs)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 737, in <lambda>
f = lambda x: x.mean(axis=self.axis)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\generic.py", line 4509, in stat_func
skipna=skipna, numeric_only=numeric_only)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\series.py", line 2111, in _reduce
self._get_axis_number(axis)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\generic.py", line 312, in _get_axis_number
.format(axis, type(self)))
ValueError: No axis named 1 for object type <class 'pandas.core.series.Series'>
======================================================================
ERROR: test_resample_empty (pandas.tseries.tests.test_resample.TestResample)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_resample.py", line 688, in test_resample_empty
empty_series.resample('d', how)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\generic.py", line 3486, in resample
return sampler.resample(self).__finalize__(self)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 82, in resample
rs = self._resample_timestamps()
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 287, in _resample_timestamps
result = grouped.aggregate(self._agg_method)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 2325, in aggregate
return getattr(self, func_or_funcs)(*args, **kwargs)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 738, in mean
return self._python_agg_general(f)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1147, in _python_agg_general
result, counts = self.grouper.agg_series(obj, f)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1862, in agg_series
return grouper.get_result()
File "pandas\src\reduce.pyx", line 227, in pandas.lib.SeriesBinGrouper.get_result (pandas\lib.c:33564)
counts[0] = self.bins[0]
IndexError: index 0 is out of bounds for axis 0 with size 0
======================================================================
ERROR: test_resample_frame_basic (pandas.tseries.tests.test_resample.TestResample)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_resample.py", line 322, in test_resample_frame_basic
g._cython_agg_general(f)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 2711, in _cython_agg_general
new_items, new_blocks = self._cython_agg_blocks(how, numeric_only=numeric_only)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 2747, in _cython_agg_blocks
result, _ = self.grouper.aggregate(block.values, how, axis=agg_axis)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1566, in aggregate
result = self._aggregate(result, counts, values, agg_func, is_numeric)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1611, in _aggregate
agg_func(result, counts, values, comp_ids)
File "pandas\src\generated.pyx", line 6743, in pandas.algos.group_add_float64 (pandas\algos.c:118485)
def group_add_float64(ndarray[float64_t, ndim=2] out,
ValueError: Buffer dtype mismatch, expected 'int64_t' but got 'long'
======================================================================
ERROR: test_resample_how (pandas.tseries.tests.test_resample.TestResample)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_resample.py", line 131, in test_resample_how
closed='right', label='right')
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\generic.py", line 3486, in resample
return sampler.resample(self).__finalize__(self)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 82, in resample
rs = self._resample_timestamps()
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 287, in _resample_timestamps
result = grouped.aggregate(self._agg_method)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 2325, in aggregate
return getattr(self, func_or_funcs)(*args, **kwargs)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 766, in std
return np.sqrt(self.var(ddof=ddof))
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 775, in var
return self._cython_agg_general('var')
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1129, in _cython_agg_general
result, names = self.grouper.aggregate(obj.values, how)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1566, in aggregate
result = self._aggregate(result, counts, values, agg_func, is_numeric)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1611, in _aggregate
agg_func(result, counts, values, comp_ids)
File "pandas\src\generated.pyx", line 6988, in pandas.algos.group_var_float64 (pandas\algos.c:121664)
def group_var_float64(ndarray[float64_t, ndim=2] out,
ValueError: ("Buffer dtype mismatch, expected 'int64_t' but got 'long'", 'how=std')
======================================================================
ERROR: test_resample_ohlc (pandas.tseries.tests.test_resample.TestResample)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_resample.py", line 406, in test_resample_ohlc
result = s.resample('5Min', how='ohlc')
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\generic.py", line 3486, in resample
return sampler.resample(self).__finalize__(self)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 82, in resample
rs = self._resample_timestamps()
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 287, in _resample_timestamps
result = grouped.aggregate(self._agg_method)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 2325, in aggregate
return getattr(self, func_or_funcs)(*args, **kwargs)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 816, in ohlc
lambda x: x._cython_agg_general('ohlc'))
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 2689, in _apply_to_column_groupbys
return func(self)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 816, in <lambda>
lambda x: x._cython_agg_general('ohlc'))
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1129, in _cython_agg_general
result, names = self.grouper.aggregate(obj.values, how)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1566, in aggregate
result = self._aggregate(result, counts, values, agg_func, is_numeric)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1611, in _aggregate
agg_func(result, counts, values, comp_ids)
File "pandas\src\generated.pyx", line 7195, in pandas.algos.group_ohlc_float64 (pandas\algos.c:124745)
def group_ohlc_float64(ndarray[float64_t, ndim=2] out,
ValueError: Buffer dtype mismatch, expected 'int64_t' but got 'long'
======================================================================
ERROR: test_resample_ohlc_dataframe (pandas.tseries.tests.test_resample.TestResample)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_resample.py", line 431, in test_resample_ohlc_dataframe
res = df.resample('H', how='ohlc')
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\generic.py", line 3486, in resample
return sampler.resample(self).__finalize__(self)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 82, in resample
rs = self._resample_timestamps()
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 315, in _resample_timestamps
result = grouped.aggregate(self._agg_method)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 2786, in aggregate
return getattr(self, arg)(*args, **kwargs)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 816, in ohlc
lambda x: x._cython_agg_general('ohlc'))
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 3460, in _apply_to_column_groupbys
keys=self._selected_obj.columns, axis=1)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tools\merge.py", line 760, in concat
copy=copy)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tools\merge.py", line 790, in __init__
objs = list(objs)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 3458, in <genexpr>
(func(col_groupby) for _, col_groupby
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 816, in <lambda>
lambda x: x._cython_agg_general('ohlc'))
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1129, in _cython_agg_general
result, names = self.grouper.aggregate(obj.values, how)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1566, in aggregate
result = self._aggregate(result, counts, values, agg_func, is_numeric)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1611, in _aggregate
agg_func(result, counts, values, comp_ids)
File "pandas\src\generated.pyx", line 7195, in pandas.algos.group_ohlc_float64 (pandas\algos.c:124745)
def group_ohlc_float64(ndarray[float64_t, ndim=2] out,
ValueError: Buffer dtype mismatch, expected 'int64_t' but got 'long'
======================================================================
ERROR: test_resample_panel (pandas.tseries.tests.test_resample.TestResample)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_resample.py", line 552, in test_resample_panel
result = panel.resample('M', axis=1)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\generic.py", line 3486, in resample
return sampler.resample(self).__finalize__(self)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 82, in resample
rs = self._resample_timestamps()
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 287, in _resample_timestamps
result = grouped.aggregate(self._agg_method)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 3501, in aggregate
return getattr(self, arg)(*args, **kwargs)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 738, in mean
return self._python_agg_general(f)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1145, in _python_agg_general
for name, obj in self._iterate_slices():
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 3477, in _iterate_slices
raise NotImplementedError("axis other than 0 is not supported")
NotImplementedError: axis other than 0 is not supported
======================================================================
ERROR: test_resample_panel_numpy (pandas.tseries.tests.test_resample.TestResample)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_resample.py", line 578, in test_resample_panel_numpy
expected = panel.resample('M', how='mean', axis=1)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\generic.py", line 3486, in resample
return sampler.resample(self).__finalize__(self)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 82, in resample
rs = self._resample_timestamps()
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 287, in _resample_timestamps
result = grouped.aggregate(self._agg_method)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 3501, in aggregate
return getattr(self, arg)(*args, **kwargs)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 738, in mean
return self._python_agg_general(f)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1145, in _python_agg_general
for name, obj in self._iterate_slices():
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 3477, in _iterate_slices
raise NotImplementedError("axis other than 0 is not supported")
NotImplementedError: axis other than 0 is not supported
======================================================================
ERROR: test_all_values_single_bin (pandas.tseries.tests.test_resample.TestResamplePeriodIndex)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_resample.py", line 1411, in test_all_values_single_bin
result = s.resample("A", how='mean')
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\generic.py", line 3486, in resample
return sampler.resample(self).__finalize__(self)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 92, in resample
rs = self._resample_periods()
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 357, in _resample_periods
return grouped.aggregate(self._agg_method)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 2325, in aggregate
return getattr(self, func_or_funcs)(*args, **kwargs)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 738, in mean
return self._python_agg_general(f)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1147, in _python_agg_general
result, counts = self.grouper.agg_series(obj, f)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1862, in agg_series
return grouper.get_result()
File "pandas\src\reduce.pyx", line 274, in pandas.lib.SeriesBinGrouper.get_result (pandas\lib.c:34306)
raise
File "pandas\src\reduce.pyx", line 249, in pandas.lib.SeriesBinGrouper.get_result (pandas\lib.c:33847)
cached_ityp = self.ityp(islider.buf)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\period.py", line 184, in __new__
ordinal, freq = cls._from_arraylike(data, freq, tz)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\period.py", line 250, in _from_arraylike
raise ValueError('freq not specified and cannot be '
ValueError: freq not specified and cannot be inferred from first element
======================================================================
ERROR: test_basic_downsample (pandas.tseries.tests.test_resample.TestResamplePeriodIndex)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_resample.py", line 1063, in test_basic_downsample
result = ts.resample('a-dec')
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\generic.py", line 3486, in resample
return sampler.resample(self).__finalize__(self)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 92, in resample
rs = self._resample_periods()
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 357, in _resample_periods
return grouped.aggregate(self._agg_method)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 2325, in aggregate
return getattr(self, func_or_funcs)(*args, **kwargs)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 738, in mean
return self._python_agg_general(f)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1147, in _python_agg_general
result, counts = self.grouper.agg_series(obj, f)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1862, in agg_series
return grouper.get_result()
File "pandas\src\reduce.pyx", line 274, in pandas.lib.SeriesBinGrouper.get_result (pandas\lib.c:34306)
raise
File "pandas\src\reduce.pyx", line 249, in pandas.lib.SeriesBinGrouper.get_result (pandas\lib.c:33847)
cached_ityp = self.ityp(islider.buf)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\period.py", line 184, in __new__
ordinal, freq = cls._from_arraylike(data, freq, tz)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\period.py", line 250, in _from_arraylike
raise ValueError('freq not specified and cannot be '
ValueError: freq not specified and cannot be inferred from first element
======================================================================
ERROR: test_basic_upsample (pandas.tseries.tests.test_resample.TestResamplePeriodIndex)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_resample.py", line 1084, in test_basic_upsample
result = ts.resample('a-dec')
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\generic.py", line 3486, in resample
return sampler.resample(self).__finalize__(self)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 92, in resample
rs = self._resample_periods()
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 357, in _resample_periods
return grouped.aggregate(self._agg_method)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 2325, in aggregate
return getattr(self, func_or_funcs)(*args, **kwargs)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 738, in mean
return self._python_agg_general(f)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1147, in _python_agg_general
result, counts = self.grouper.agg_series(obj, f)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1862, in agg_series
return grouper.get_result()
File "pandas\src\reduce.pyx", line 274, in pandas.lib.SeriesBinGrouper.get_result (pandas\lib.c:34306)
raise
File "pandas\src\reduce.pyx", line 249, in pandas.lib.SeriesBinGrouper.get_result (pandas\lib.c:33847)
cached_ityp = self.ityp(islider.buf)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\period.py", line 184, in __new__
ordinal, freq = cls._from_arraylike(data, freq, tz)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\period.py", line 250, in _from_arraylike
raise ValueError('freq not specified and cannot be '
ValueError: freq not specified and cannot be inferred from first element
======================================================================
ERROR: test_quarterly_resampling (pandas.tseries.tests.test_resample.TestResamplePeriodIndex)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_resample.py", line 1344, in test_quarterly_resampling
result = ts.resample('A')
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\generic.py", line 3486, in resample
return sampler.resample(self).__finalize__(self)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 92, in resample
rs = self._resample_periods()
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 357, in _resample_periods
return grouped.aggregate(self._agg_method)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 2325, in aggregate
return getattr(self, func_or_funcs)(*args, **kwargs)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 738, in mean
return self._python_agg_general(f)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1147, in _python_agg_general
result, counts = self.grouper.agg_series(obj, f)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1862, in agg_series
return grouper.get_result()
File "pandas\src\reduce.pyx", line 274, in pandas.lib.SeriesBinGrouper.get_result (pandas\lib.c:34306)
raise
File "pandas\src\reduce.pyx", line 249, in pandas.lib.SeriesBinGrouper.get_result (pandas\lib.c:33847)
cached_ityp = self.ityp(islider.buf)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\period.py", line 184, in __new__
ordinal, freq = cls._from_arraylike(data, freq, tz)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\period.py", line 250, in _from_arraylike
raise ValueError('freq not specified and cannot be '
ValueError: freq not specified and cannot be inferred from first element
======================================================================
ERROR: test_aggregate_normal (pandas.tseries.tests.test_resample.TestTimeGrouper)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_resample.py", line 1552, in test_aggregate_normal
dt_result = getattr(dt_grouped, func)()
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 775, in var
return self._cython_agg_general('var')
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 2711, in _cython_agg_general
new_items, new_blocks = self._cython_agg_blocks(how, numeric_only=numeric_only)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 2747, in _cython_agg_blocks
result, _ = self.grouper.aggregate(block.values, how, axis=agg_axis)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1566, in aggregate
result = self._aggregate(result, counts, values, agg_func, is_numeric)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1611, in _aggregate
agg_func(result, counts, values, comp_ids)
File "pandas\src\generated.pyx", line 6988, in pandas.algos.group_var_float64 (pandas\algos.c:121664)
def group_var_float64(ndarray[float64_t, ndim=2] out,
ValueError: Buffer dtype mismatch, expected 'int64_t' but got 'long'
======================================================================
ERROR: test_panel_aggregation (pandas.tseries.tests.test_resample.TestTimeGrouper)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_resample.py", line 1512, in test_panel_aggregation
binagg = bingrouped.mean()
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 738, in mean
return self._python_agg_general(f)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1145, in _python_agg_general
for name, obj in self._iterate_slices():
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 3477, in _iterate_slices
raise NotImplementedError("axis other than 0 is not supported")
NotImplementedError: axis other than 0 is not supported
======================================================================
ERROR: test_period_resample (pandas.tseries.tests.test_timeseries.TestTimeSeries)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_timeseries.py", line 2042, in test_period_resample
result = s.to_period().resample('T', kind='period')
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\generic.py", line 3486, in resample
return sampler.resample(self).__finalize__(self)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 92, in resample
rs = self._resample_periods()
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 357, in _resample_periods
return grouped.aggregate(self._agg_method)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 2325, in aggregate
return getattr(self, func_or_funcs)(*args, **kwargs)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 738, in mean
return self._python_agg_general(f)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1147, in _python_agg_general
result, counts = self.grouper.agg_series(obj, f)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1862, in agg_series
return grouper.get_result()
File "pandas\src\reduce.pyx", line 274, in pandas.lib.SeriesBinGrouper.get_result (pandas\lib.c:34306)
raise
File "pandas\src\reduce.pyx", line 249, in pandas.lib.SeriesBinGrouper.get_result (pandas\lib.c:33847)
cached_ityp = self.ityp(islider.buf)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\period.py", line 184, in __new__
ordinal, freq = cls._from_arraylike(data, freq, tz)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\period.py", line 250, in _from_arraylike
raise ValueError('freq not specified and cannot be '
ValueError: freq not specified and cannot be inferred from first element
======================================================================
ERROR: pandas.tests.test_tseries.test_group_ohlc
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Miniconda\envs\pandas\lib\site-packages\nose\case.py", line 197, in runTest
self.test(*self.arg)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tests\test_tseries.py", line 507, in test_group_ohlc
_check('float32')
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tests\test_tseries.py", line 489, in _check
func(out, counts, obj[:, None], labels)
File "pandas\src\generated.pyx", line 7240, in pandas.algos.group_ohlc_float32 (pandas\algos.c:125372)
def group_ohlc_float32(ndarray[float32_t, ndim=2] out,
ValueError: Buffer dtype mismatch, expected 'int64_t' but got 'long'
======================================================================
FAIL: test_resample_dup_index (pandas.tseries.tests.test_resample.TestResample)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_resample.py", line 454, in test_resample_dup_index
result = df.resample('Q',axis=1)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\generic.py", line 3486, in resample
return sampler.resample(self).__finalize__(self)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 92, in resample
rs = self._resample_periods()
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\resample.py", line 357, in _resample_periods
return grouped.aggregate(self._agg_method)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 2786, in aggregate
return getattr(self, arg)(*args, **kwargs)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 738, in mean
return self._python_agg_general(f)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1147, in _python_agg_general
result, counts = self.grouper.agg_series(obj, f)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\core\groupby.py", line 1862, in agg_series
return grouper.get_result()
File "pandas\src\reduce.pyx", line 238, in pandas.lib.SeriesBinGrouper.get_result (pandas\lib.c:33723)
vslider = Slider(self.arr, self.dummy_arr)
File "pandas\src\reduce.pyx", line 434, in pandas.lib.Slider.__init__ (pandas\lib.c:38534)
assert(values.ndim == 1)
AssertionError
======================================================================
FAIL: test_resample_group_info (pandas.tseries.tests.test_resample.TestResample)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_resample.py", line 942, in test_resample_group_info
assert_series_equal(left, right)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\util\testing.py", line 865, in assert_series_equal
assert_attr_equal('dtype', left, right)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\util\testing.py", line 667, in assert_attr_equal
left_attr, right_attr)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\util\testing.py", line 757, in raise_assert_detail
raise AssertionError(msg)
AssertionError: Attributes are different
Attribute "dtype" are different
[left]: int32
[right]: int64
======================================================================
FAIL: test_aggregate_with_nat (pandas.tseries.tests.test_resample.TestTimeGrouper)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tseries\tests\test_resample.py", line 1607, in test_aggregate_with_nat
assert_frame_equal(expected, dt_result)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\util\testing.py", line 987, in assert_frame_equal
obj='DataFrame.iloc[:, {0}]'.format(i))
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\util\testing.py", line 884, in assert_series_equal
check_less_precise, obj='{0}'.format(obj))
File "pandas\src\testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas\src\testing.c:3615)
cpdef assert_almost_equal(a, b, bint check_less_precise=False,
File "pandas\src\testing.pyx", line 147, in pandas._testing.assert_almost_equal (pandas\src\testing.c:2577)
raise_assert_detail(obj, msg, lobj, robj)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\util\testing.py", line 757, in raise_assert_detail
raise AssertionError(msg)
AssertionError: DataFrame.iloc[:, 0] are different
DataFrame.iloc[:, 0] values are different (20.0 %)
[left]: [1.2509771188, -0.314073359135, nan, -1.59805928254, -2.4494635435]
[right]: [1.2509771188, -0.314073359135, 0.0, -1.59805928254, -2.4494635435]
======================================================================
FAIL: test_groupby_with_timegrouper (pandas.tests.test_groupby.TestGroupBy)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tests\test_groupby.py", line 4020, in test_groupby_with_timegrouper
assert_frame_equal(result1, expected)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\util\testing.py", line 950, in assert_frame_equal
'{0}, {1}'.format(right.shape[1], right.columns))
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\util\testing.py", line 757, in raise_assert_detail
raise AssertionError(msg)
AssertionError: DataFrame are different
DataFrame shape (number of columns) are different
[left]: 2, Index([u'Buyer', u'Quantity'], dtype='object')
[right]: 1, Index([u'Quantity'], dtype='object')
======================================================================
FAIL: test_series_groupby_nunique (pandas.tests.test_groupby.TestGroupBy)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tests\test_groupby.py", line 1641, in test_series_groupby_nunique
check_nunique(frame, ['jim'])
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tests\test_groupby.py", line 1631, in check_nunique
assert_series_equal(left, right)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\util\testing.py", line 865, in assert_series_equal
assert_attr_equal('dtype', left, right)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\util\testing.py", line 667, in assert_attr_equal
left_attr, right_attr)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\util\testing.py", line 757, in raise_assert_detail
raise AssertionError(msg)
AssertionError: Attributes are different
Attribute "dtype" are different
[left]: int32
[right]: int64
======================================================================
FAIL: test_timegrouper_with_reg_groups (pandas.tests.test_groupby.TestGroupBy)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\tests\test_groupby.py", line 4188, in test_timegrouper_with_reg_groups
assert_frame_equal(result, expected)
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\util\testing.py", line 950, in assert_frame_equal
'{0}, {1}'.format(right.shape[1], right.columns))
File "C:\Users\Jeff Reback\pandas\build\lib.win-amd64-2.7\pandas\util\testing.py", line 757, in raise_assert_detail
raise AssertionError(msg)
AssertionError: DataFrame are different
DataFrame shape (number of columns) are different
[left]: 3, Index([u'Branch', u'Buyer', u'Quantity'], dtype='object')
[right]: 1, Index([u'Quantity'], dtype='object')
----------------------------------------------------------------------
Ran 8031 tests in 565.959s
FAILED (SKIP=622, errors=19, failures=6)
[pandas] C:\Users\Jeff Reback\pandas>
```
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https://api.github.com/repos/pandas-dev/pandas/issues/10982 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10982/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10982/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10982/events | https://github.com/pandas-dev/pandas/issues/10982 | 104,714,589 | MDU6SXNzdWUxMDQ3MTQ1ODk= | 10,982 | .to_excel() cuts off columns | {
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} | 2 | 2015-09-03T14:55:26Z | 2015-10-10T00:23:39Z | 2015-10-10T00:23:39Z | NONE | null | Today I discovered a strange behavior: When I am writing a DataFrame with `.to_excel()`, it cuts columns. Compared with the same DataFrame with `.to_csv()` or `.head()`, you can see the difference, that the last 8 columns are missing.
You can reproduce this by downloading `Features.pkl` from [here](https://dl.dropboxusercontent.com/u/16985007/Features.pkl) and then:
``` python
import pandas as pd
df = pd.read_pickle('Features.pkl')
df.head() # see the last 8 columns!
df.to_excel('Features.xlsx', index=False, header=False)
# see the Excel, you do not have these last 8 columns
# in a .to_csv() you have them
```
Funny part: If you `df.ix[:,-71:].to_excel('Features.xlsx', index=False, header=False)` you have one of the missing columns. If you do `df.ix[:,-70:].to_excel('Features.xlsx', index=False, header=False)` you have two and so on...
``` python
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8
pandas: 0.16.2
nose: 1.3.7
Cython: 0.22.1
numpy: 1.9.2
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 3.2.0
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.4
bottleneck: 1.0.0
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: 1.8.6
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: 0.9
apiclient: None
sqlalchemy: 1.0.5
pymysql: None
psycopg2: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/10983 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10983/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10983/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10983/events | https://github.com/pandas-dev/pandas/pull/10983 | 104,732,390 | MDExOlB1bGxSZXF1ZXN0NDQxMjcyMDE= | 10,983 | TST/DOC #10846 Test and document use of SQLAlchemy expressions in read_sql() | {
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} | 3 | 2015-09-03T16:18:35Z | 2015-09-22T12:45:07Z | 2015-09-22T12:45:07Z | NONE | null | This provides 2 tests using SQLALchemy expressions to read SQL data into a dataframe and accompanying documentation.
It attempts to address #10846 by explaining how this feature is already supported by pandas.
Closes #10846
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} | 6 | 2015-09-03T23:51:45Z | 2020-01-03T23:23:44Z | 2020-01-03T23:23:44Z | CONTRIBUTOR | null | ```
In [179]: midx = MultiIndex.from_tuples([('A', 1, 2), ('A', 1, 2), ('B', 1, 2)])
...: df = DataFrame(np.random.randn(3, 3), index=midx,
...: columns=['x', 'y', 'z'])
In [180]: df
Out[180]:
x y z
A 1 2 1.236013 2.239172 -1.513059
2 -0.159620 0.222250 -1.635894
B 1 2 0.211036 -0.441065 -0.661737
In [181]: df.to_csv('test.csv')
In [182]: result = pd.read_csv('test.csv', index_col=[0, 1, 2])
In [183]: result
Out[183]:
x y z
Unnamed: 1 Unnamed: 2
A 1 2 1.236013 2.239172 -1.513059
2 -0.159620 0.222250 -1.635894
B 1 2 0.211036 -0.441065 -0.661737
```
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| null | 4 | 2015-09-04T03:01:12Z | 2015-10-18T14:03:47Z | 2015-10-18T14:03:47Z | CONTRIBUTOR | null | See #10882
But I am having trouble testing this change. When I run `nosetests pandas/io/tests/test_parsers.py`, it says `Segmentation fault: 11` and this pops up `Python quit unexpectedly while using the parser.so plug-in.`
Any ideas of how to deal with this?
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```
for field in ["release_date", "date"]:
if field in df:
df[field] = pd.to_datetime(df[field], unit="ms")
```
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} | 5 | 2015-09-04T16:26:01Z | 2015-10-19T13:54:03Z | 2015-10-19T11:39:44Z | CONTRIBUTOR | null | First, an example that works as expected (non-categorical):
``` python
In [22]: pd.__version__
Out[22]: '0.16.2'
In [23]: data = pd.DataFrame({'x': np.arange(99),
'y': np.arange(99) // 50,
'z': np.arange(99) % 3})
In [24]: data.pivot_table('x', 'y', 'z')
Out[24]:
z 0 1 2
y
0 24.0 25.0 24.5
1 73.5 74.5 74.0
In [25]: data.pivot_table('x', 'y', 'z', margins=True)
Out[25]:
z 0 1 2 All
y
0 24.0 25.0 24.5 24.5
1 73.5 74.5 74.0 74.0
All 48.0 49.0 50.0 49.0
```
Now convert `y` and `z` to categories; pivot table works without margins but fails with:
``` python
In [27]: data.y = data.y.astype('category')
In [28]: data.z = data.z.astype('category')
In [29]: data.pivot_table('x', 'y', 'z')
Out[29]:
z 0 1 2
y
0 24.0 25.0 24.5
1 73.5 74.5 74.0
In [32]: data.pivot_table('x', 'y', 'z', margins=True)
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
/Users/jakevdp/anaconda/envs/py3k/lib/python3.3/site-packages/pandas/core/internals.py in set(self, item, value, check)
2979 try:
-> 2980 loc = self.items.get_loc(item)
2981 except KeyError:
/Users/jakevdp/anaconda/envs/py3k/lib/python3.3/site-packages/pandas/core/index.py in get_loc(self, key, method)
5072 key = tuple(map(_maybe_str_to_time_stamp, key, self.levels))
-> 5073 return self._engine.get_loc(key)
5074
pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3824)()
pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3704)()
pandas/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12280)()
pandas/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12231)()
KeyError: ('x', 'All')
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-32-7436e0e1c9bb> in <module>()
----> 1 data.pivot_table('x', 'y', 'z', margins=True)
/Users/jakevdp/anaconda/envs/py3k/lib/python3.3/site-packages/pandas/tools/pivot.py in pivot_table(data, values, index, columns, aggfunc, fill_value, margins, dropna)
141 if margins:
142 table = _add_margins(table, data, values, rows=index,
--> 143 cols=columns, aggfunc=aggfunc)
144
145 # discard the top level
/Users/jakevdp/anaconda/envs/py3k/lib/python3.3/site-packages/pandas/tools/pivot.py in _add_margins(table, data, values, rows, cols, aggfunc)
167
168 if values:
--> 169 marginal_result_set = _generate_marginal_results(table, data, values, rows, cols, aggfunc, grand_margin)
170 if not isinstance(marginal_result_set, tuple):
171 return marginal_result_set
/Users/jakevdp/anaconda/envs/py3k/lib/python3.3/site-packages/pandas/tools/pivot.py in _generate_marginal_results(table, data, values, rows, cols, aggfunc, grand_margin)
236 # we are going to mutate this, so need to copy!
237 piece = piece.copy()
--> 238 piece[all_key] = margin[key]
239
240 table_pieces.append(piece)
/Users/jakevdp/anaconda/envs/py3k/lib/python3.3/site-packages/pandas/core/frame.py in __setitem__(self, key, value)
2125 else:
2126 # set column
-> 2127 self._set_item(key, value)
2128
2129 def _setitem_slice(self, key, value):
/Users/jakevdp/anaconda/envs/py3k/lib/python3.3/site-packages/pandas/core/frame.py in _set_item(self, key, value)
2203 self._ensure_valid_index(value)
2204 value = self._sanitize_column(key, value)
-> 2205 NDFrame._set_item(self, key, value)
2206
2207 # check if we are modifying a copy
/Users/jakevdp/anaconda/envs/py3k/lib/python3.3/site-packages/pandas/core/generic.py in _set_item(self, key, value)
1194
1195 def _set_item(self, key, value):
-> 1196 self._data.set(key, value)
1197 self._clear_item_cache()
1198
/Users/jakevdp/anaconda/envs/py3k/lib/python3.3/site-packages/pandas/core/internals.py in set(self, item, value, check)
2981 except KeyError:
2982 # This item wasn't present, just insert at end
-> 2983 self.insert(len(self.items), item, value)
2984 return
2985
/Users/jakevdp/anaconda/envs/py3k/lib/python3.3/site-packages/pandas/core/internals.py in insert(self, loc, item, value, allow_duplicates)
3100 self._blknos = np.insert(self._blknos, loc, len(self.blocks))
3101
-> 3102 self.axes[0] = self.items.insert(loc, item)
3103
3104 self.blocks += (block,)
/Users/jakevdp/anaconda/envs/py3k/lib/python3.3/site-packages/pandas/core/index.py in insert(self, loc, item)
5583 # other labels
5584 lev_loc = len(level)
-> 5585 level = level.insert(lev_loc, k)
5586 else:
5587 lev_loc = level.get_loc(k)
/Users/jakevdp/anaconda/envs/py3k/lib/python3.3/site-packages/pandas/core/index.py in insert(self, loc, item)
3217 code = self.categories.get_indexer([item])
3218 if (code == -1):
-> 3219 raise TypeError("cannot insert an item into a CategoricalIndex that is not already an existing category")
3220
3221 codes = self.codes
TypeError: cannot insert an item into a CategoricalIndex that is not already an existing category
```
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| null | 5 | 2015-09-04T16:34:25Z | 2015-09-04T19:19:50Z | 2015-09-04T19:19:39Z | NONE | null | Just doing what I'm told here by the exception :)
``` python
...
File "/home/tyler/repos/switchy/switchy/apps/measure/metrics.py", line 164, in append_row
self._store.append('data', self._df, dropna=False)
File "/usr/lib/python2.7/site-packages/pandas/io/pytables.py", line 912, in append
**kwargs)
File "/usr/lib/python2.7/site-packages/pandas/io/pytables.py", line 1268, in _write_to_group
s.write(obj=value, append=append, complib=complib, **kwargs)
File "/usr/lib/python2.7/site-packages/pandas/io/pytables.py", line 3805, in write
self.write_data(chunksize, dropna=dropna)
File "/usr/lib/python2.7/site-packages/pandas/io/pytables.py", line 3880, in write_data
values=[v[start_i:end_i] for v in bvalues])
File "/usr/lib/python2.7/site-packages/pandas/io/pytables.py", line 3926, in write_data_chunk
raise TypeError("tables cannot write this data -> %s" % detail)
TypeError: tables cannot write this data -> internal error: the number of indexed rows (4096)
and rows in the table (2048) is not equal; please report this to the authors.
```
What I'm doing...
I create a data frame and a store inside an instance:
``` python
class DataStore(object):
"""Wraps a `pd.DataFrame` which buffers recent data in memory and
offloads excess to disk using the HDF5 format.
"""
def __init__(self, data=None, columns=None, size=2**9,
hdf_path=None, metrics_func=lambda df: df):
self._df = pd.DataFrame(
data=data,
columns=columns,
index=range(size) if data is None else None,
dtype=np.float64,
)
self._len = len(self._df)
# current row insertion-index
self._ri = 0 if data is None else len(self._df) - 1
self._store = pd.HDFStore(
hdf_path or tempfile.mktemp() + '_switchy_metrics.h5'
)
self._store.open('a')
self._mfunc = metrics_func
```
An event loop writes rows to this instance using the following method:
``` python
def append_row(self, row=None):
'''Insert :var:`row` into the internal data frame at the current index
and increment. Return a bool indicating whether the current entry has
caused a flush to disk.
'''
flush = False
i = self.findex # just the row index modulo len(self.df)
if self._ri > self._len - 1 and i == 0:
# offload frame to disk
self._store.append('data', self._df, dropna=False)
flush = True
self._df.values[i, :] = row
self._ri += 1
return flush
```
So, in english, every time the in mem data frame fills up (i.e. after 512 rows insertions), write the `self._df` frame to disk by appending to the HDF table.
I'm assuming this is a PyTables issue. If there's something wrong with my usage I'd be glad to know. Also note this error only shows up a fraction of the times `self._store.append('data', self._df, dropna=False)` is actually called. The event loop rate is ~ 30 insertions / second but the target is to get it much higher.
Version info:
``` python
In [2]: show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 4.0.7-2-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.2
nose: 1.3.7
Cython: None
numpy: 1.9.2
scipy: 0.16.0
statsmodels: None
IPython: 4.0.0
sphinx: 1.3.1
patsy: None
dateutil: 2.4.2
pytz: 2015.4
bottleneck: None
tables: 3.2.1
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.6.9
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
```
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| null | 3 | 2015-09-04T17:40:16Z | 2015-09-05T18:24:05Z | 2015-09-05T17:59:11Z | CONTRIBUTOR | null | See #10970 and #10982. Users face bugs but no informative errors are given. This should warn them and inform them what might be wrong.
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} | 2 | 2015-09-04T19:16:02Z | 2015-09-05T14:19:49Z | 2015-09-05T14:19:49Z | CONTRIBUTOR | null | - makes the toc 4 levels
- increases the width of the side bar a bit
- increases the overall width just a bit
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} | 12 | 2015-09-04T20:00:17Z | 2015-10-20T17:29:50Z | 2015-10-20T17:29:50Z | CONTRIBUTOR | null | This is a fix for the issue reported in #10989. I suspect this is an example of "fixing the symptom" rather than "fixing the problem", but I think it makes clear what the source of the problem is: to compute margins, the pivot table must add a row and/or column to the result. If the index or column is categorical, a new value cannot be added.
Let me know if you think there are better approaches to this.
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| null | 3 | 2015-09-04T21:08:50Z | 2015-09-04T22:37:06Z | 2015-09-04T21:33:17Z | NONE | null | ``` python
import pandas as pd
from numpy import random
# Make a dataframe
foo = pd.DataFrame(random.rand(5,6),
columns=pd.MultiIndex.from_tuples(
[('A','1'), ('A','2'),('B','1'),('B','2'),('C','1'),('C','2')]))
# Select a subset of columns based on level[0] labels
fooAB = foo[['A','B']].copy()
# Compare multiindex levels of original and subset dataframes
print(foo.columns.levels[0], fooAB.columns.levels[0])
# They are the same! Expected behavior is that fooAB
# will have a multiindex with level[0] = ['A','B']
# The labels are different, which is what makes
# the fooAB column names appear correct
print(foo.columns.labels[0], fooAB.columns.labels[0])
```
This behavior occurs for both `foo[['A','B']].copy()` and `foo[['A','B']]`.
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} | 0 | 2015-09-04T22:04:33Z | 2015-10-26T12:04:00Z | 2015-10-26T12:04:00Z | CONTRIBUTOR | null | Documentation here http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.reindex.html
could use some improvement
- mention that `ffill`, `bfill`, `method` only available for monotonically increasing and decreasing indices
- add a ton of examples to help users navigate all the various options
- add note regarding https://github.com/pydata/pandas/issues/2067
Pull req is in the works.
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} | 14 | 2015-09-04T22:09:10Z | 2015-10-26T12:04:04Z | 2015-10-26T12:04:00Z | CONTRIBUTOR | null | Fixes https://github.com/pydata/pandas/issues/10995
Done/waiting for feedback.
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} | 1 | 2015-09-05T03:21:30Z | 2017-08-25T04:52:33Z | 2015-09-05T10:09:02Z | CONTRIBUTOR | null | Change to link to https://pip.pypa.io/en/latest/reference/pip_install.html#editable-installs
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} | 20 | 2015-09-05T11:37:01Z | 2015-09-13T00:29:16Z | 2015-09-12T23:46:46Z | MEMBER | null | Clean-up of the groupby benchmarks.
This is an example of how they can be cleaned up (grouping benchmarks with the same setup in common classes, removing a lot of the setup functions in this way)
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| null | 10 | 2015-09-05T12:44:43Z | 2017-08-25T04:52:52Z | 2015-10-18T14:04:11Z | CONTRIBUTOR | null | Reduce `for` loops in `df.corr(method='kendall')`
Don't need to loop through lower triangle of the matrix.
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} | 4 | 2015-09-05T16:08:22Z | 2019-09-11T08:31:47Z | 2019-09-11T08:31:40Z | CONTRIBUTOR | null | [here](https://github.com/pydata/pandas/pull/10992) added 4th level menu items where appropriate in io.rst.
might pay to do this somewhat in some other sections just to break the sub-sections up a bit
- http://pandas-docs.github.io/pandas-docs-travis/dsintro.html
maybe other places as well
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} | 1 | 2015-09-05T17:46:24Z | 2015-09-05T23:22:57Z | 2015-09-05T23:22:57Z | NONE | null | I'm on windows 7, anaconda, python 2.7.9, 32bit
pandas 0.16.2
try this:
``` python
import pandas as pd
df4 = pd.DataFrame(index=P.date_range('1750-1-1', '2050-1-1', freq='7D')
pd.to_pickle(df4, '7d.test')
pd.read_pickle('7d.test')
In [84]: P.read_pickle('7d.test')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-84-0108eecfbea7> in <module>()
----> 1 P.read_pickle('7d.test')
C:\Users\LV\Miniconda\lib\site-packages\pandas\io\pickle.pyc in read_pickle(path)
58
59 try:
---> 60 return try_read(path)
61 except:
62 if PY3:
C:\Users\LV\Miniconda\lib\site-packages\pandas\io\pickle.pyc in try_read(path, encoding)
55 except:
56 with open(path, 'rb') as fh:
---> 57 return pc.load(fh, encoding=encoding, compat=True)
58
59 try:
C:\Users\LV\Miniconda\lib\site-packages\pandas\compat\pickle_compat.pyc in load(fh, encoding, compat, is_verbose)
114 up.is_verbose = is_verbose
115
--> 116 return up.load()
117 except:
118 raise
C:\Users\LV\Miniconda\lib\pickle.pyc in load(self)
856 while 1:
857 key = read(1)
--> 858 dispatch[key](self)
859 except _Stop, stopinst:
860 return stopinst.value
C:\Users\LV\Miniconda\lib\site-packages\pandas\compat\pickle_compat.pyc in load_reduce(self)
18
19 try:
---> 20 stack[-1] = func(*args)
21 return
22 except Exception as e:
C:\Users\LV\Miniconda\lib\site-packages\pandas\tseries\index.pyc in _new_DatetimeIndex(cls, d)
113 # data are already in UTC
114 tz = d.pop('tz',None)
--> 115 result = cls.__new__(cls, **d)
116 result.tz = tz
117 return result
C:\Users\LV\Miniconda\lib\site-packages\pandas\util\decorators.pyc in wrapper(*args, **kwargs)
86 else:
87 kwargs[new_arg_name] = new_arg_value
---> 88 return func(*args, **kwargs)
89 return wrapper
90 return _deprecate_kwarg
C:\Users\LV\Miniconda\lib\site-packages\pandas\tseries\index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, closed, ambiguous, **kwargs)
334 if not np.array_equal(subarr.asi8, on_freq.asi8):
335 raise ValueError('Inferred frequency {0} from passed dates does not'
--> 336 'conform to passed frequency {1}'.format(inferred, freq.freqstr))
337
338 if freq_infer:
ValueError: Inferred frequency W-THU from passed dates does notconform to passed frequency 7D
```
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