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} | 18 | "2013-01-15T19:39:58Z" | "2019-08-06T13:22:17Z" | "2013-11-10T11:25:29Z" | CONTRIBUTOR | null | link to #1651
Saving a multiindex column to_excel saves a sparse index.
Migrated from this [StackOverflow question](http://stackoverflow.com/questions/14341584/exporting-pandas-dataframe-with-multiindex), with a smaller DataFrame.
```
In [1]: m = MultiIndex.from_tuples([(1,1),(1,2)], names=['a','b'])
In [2]: df = DataFrame([[1,2],[3,4]], columns=m)
In [3]: df
Out[3]:
a 1
b 1 2
0 1 2
1 3 4
In [4]: df.to_excel('test.xls')
```
Saves the xls:
```
1.1 .2
0 1 2
1 3 4
```
This differs from how `to_csv` (which is not sparse):
```
,"(1L, 1L)","(1L, 2L)"
0,1,2
1,3,4
```
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} | 9 | "2013-01-15T21:43:41Z" | "2013-09-06T03:11:15Z" | "2013-09-06T03:11:15Z" | NONE | null | Fancy or Boolean indexing on a Series has two strange behaviors. My examples only show the behavior with Fancy indexing, but it's the same for Boolean indexing.
## LHS vs RHS length
```
>>> s = pd.Series(list('abc'))
>>> s[[0,1,2]] = range(27)
>>> list(s)
[0, 1, 2]
```
I would have expected an error, similar to what I get with slice indexing
```
>>> s = pd.Series(list('abc'))
>>> s[0:3] = range(27)
ValueError: cannot copy sequence with size 27 to array axis with dimension 3
```
An even odder behavior is when you have too few items in the RHS
```
>>> s = pd.Series(list('abc'))
>>> s[[0,1,2]] = range(2)
>>> list(s)
[0, 1, 0]
```
It seems to be using something like itertools.cycle which seems very arbitrary to me
## Nested RHS
This may seem like a strange use of pandas, but I need to store Python lists
```
>>> s = pd.Series(list('abc'))
>>> s[[0,1,2]] = [[100,200], [300,400], [500,600]]
>>> list(s)
[100, 200, 300]
```
Very strange. It's like it flattens the input first.
But this flattening only happens if the nested levels are all the same size.
```
>>> s = pd.Series(list('abc'))
>>> s[[0,1,2]] = [[100,200], [300,400], [500,600, 601, 602]]
>>> list(s)
[[100,200], [300,400], [500,600, 601, 602]]
```
I know in numpy the array constructor would make a distinction between these two inputs, so maybe that's the reason for the difference, but I still don't see why ndarrays are being flattened.
I can work around the issue by converting the RHS to a 1-D array and passing that in.
```
>>> s = pd.Series(list('abc'))
>>> rhs = np.empty(3).astype('object')
>>> rhs[:] = [[100,200], [300,400], [500,600]]
>>> s[[0,1,2]] = rhs
>>> list(s)
[[100,200], [300,400], [500,600]]
```
Slice indexing doesn't have this problem at all
```
>>> s = pd.Series(list('abc'))
>>> s[0:3] = [[100,200], [300,400], [500,600]]
>>> list(s)
[[100,200], [300,400], [500,600]]
```
**My Question**: Are these behaviors a _bug_ or a "feature"? I think Fancy/Boolean indexing should operate the same as slice indexing -- i.e. check for matching lengths and don't auto-convert to numpy array.
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In [615]: df = pd.DataFrame([3,4,5],columns=['a'])
In [616]: df['b'] = 'nope'
In [617]: df[df['a'] == 3]['b'] = 'found'
In [618]: df
Out[618]:
a b
0 3 nope
1 4 nope
2 5 nope
#Expected
In [618]: df
Out[618]:
a b
0 3 found
1 4 nope
2 5 nope
```
I believe the intermediate filter step creates a new DataFrame, but as far as I know, the outer step (where the value is set) should modify in-place.
Pandas 0.10.
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} | 3 | "2013-01-17T13:26:47Z" | "2019-12-24T04:22:16Z" | null | CONTRIBUTOR | null | When I call resample to downsample a certain time series and aggregate the in-between values, sometimes I want both borders included in the aggregation bin (repeating the borders in two neighbouring bins).
This might be offered as a `closed='both'` option for `resample`.
An example when this might be necessary is e.g. numerical integration
```
from scipy import integrate
def ts_integrate(ts):
print ts
return integrate.simps(ts, ts.index.astype(np.int64) / 10**9)
ts2 = pd.Series(1, pd.date_range(start='2012-01-23', periods=12, freq='1s'))
ts3 = ts2.resample('3s', how = ts_integrate, closed='left')
```
Elements that enter the 1st bin are
```
2012-01-23 00:00:00 1
2012-01-23 00:00:01 1
2012-01-23 00:00:02 1
```
and `2012-01-23 00:00:03` ends up **only** in the 2nd bin. This results in the integral for the first bin equaling 2, whereas it should be 3 (the `00:00:02`-`00:00:03` interval isn't processed by anybody).
With the `closed='both'` option, bins would be
```
2012-01-23 00:00:00 1
2012-01-23 00:00:01 1
2012-01-23 00:00:02 1
2012-01-23 00:00:03 1
2012-01-23 00:00:03 1
2012-01-23 00:00:04 1
2012-01-23 00:00:05 1
2012-01-23 00:00:06 1
```
resulting in the wanted `[3, 3, ...]` integral values.
If the patch is not too difficult and someone points me in the right direction, I might try to do it myself.
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} | 15 | "2013-01-17T20:39:12Z" | "2014-05-29T05:01:18Z" | "2014-05-29T05:01:18Z" | NONE | null | I tried pickling a very large dataframe (20GB or so) and that succeeded to write to disk, but when I try to read it, it fails with: ValueError: buffer size does not match array size
Now I did a bit of research and found the following:
http://stackoverflow.com/questions/12060932/unable-to-load-a-previously-dumped-pickle-file-of-large-size-in-python
http://bugs.python.org/issue13555
I am thinking this is a numpy/python issue, but it does cause me pretty big pain when I want to back up a dataframe that took a long time to join together, and I want all the dtypes stored (namely what columns are datetimes). Perhaps a solution would be a csv file that keeps the dtypes somewhere (otherwise I'll have to figure out what columns are serialized dates). Any workarounds would be appreciated.
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} | 4 | "2013-01-19T03:39:00Z" | "2013-01-21T02:25:40Z" | "2013-01-20T21:59:52Z" | NONE | null | I am upgrading Pandas from 0.8.1 to 0.10.1.dev-f7f7e13 . My environment is Window XP with below: Python: 2.7.3 Numpy: 1.6.2 MPL: 1.1.1 Pandas: 0.10.1.dev-f7f7e13.
Then OK application on 0.8.1 now meets errors. I trace the root cause to filtering the duplicated index of Series. Detail in : http://stackoverflow.com/questions/14395678/how-to-drop-extra-copy-of-duplicate-index-of-pandas-series
simply put: below snippet has two issues :
import pandas as pd
idx_tp = [('600809', '20061231'), ('600809', '20070331'), ('600809', '20070630'), ('600809', '20070331')]
dt = ['demo','demo','demo','demo']
idx = pd.MultiIndex.from_tuples(idx_tp,names = ['STK_ID','RPT_Date'])
s = pd.Series(dt,index=idx)
# Issue 1: s[s.index.unique()] works well on 0.8.1 but not 0.10.1
# Issue 2: s.groupby(s.index).first() will crash on my machine
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} | 3 | "2013-01-19T19:36:56Z" | "2016-10-12T23:03:28Z" | "2013-01-20T20:17:12Z" | MEMBER | null | from mailing list
```
1) With numeric values everywhere:
df['tt1'] = df['tt2'] = 0
df.ix[1, ['tt1', 'tt2']] = [1, 2]
print df.ix[0:2,['tt1', 'tt2']]
Output:
tt1 tt2
0 0 0
1 1 2
2 0 0
2) With string values everywhere:
df['tt1'] = df['tt2'] = '0'
df.ix[1, ['tt1', 'tt2']] = ['1', '2']
print df.ix[0:2,['tt1', 'tt2']]
Output:
tt1 tt2
0 0 0
1 [1, 2] [1, 2]
2 0 0
```
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} | 4 | "2013-01-19T21:27:44Z" | "2014-06-17T21:41:04Z" | "2013-02-10T16:19:30Z" | CONTRIBUTOR | null | Support for numeric dtype propogation and coexistance in DataFrames. Prior to 0.10.2, numeric dtypes passed to DataFrames were always casted to `int64` or `float64`. Now, if a dtype is passed (either directly via the `dtype` keyword, a passed `ndarray`, or a passed `Series`, then it will be preserved in DataFrame operations. Furthermore, different numeric dtypes will **NOT** be combined. The following example will give you a taste. This closes GH #622
other changes introduced in this PR (i removed all datetime like issues to PR # 2752 - should be merged first)
ENH:
- validated get_numeric_data returns correct dtypes
- added `blocks` attribute (and as_blocks()) method that returns a
dict of dtype -> homogeneous dtyped DataFrame, analagous to `values` attribute
- added keyword 'raise_on_error' to astype, which can be set to false to exluded non-numeric columns
- changed `get_dtype_counts()` to use `blocks` attribute
- changed `convert_objects()` to use the internals method convert (which is block operated)
- added option to `convert_numeric=False` to `convert_objects` to force numeric conversion (or set to `np.nan`, turned off by default)
- added option to `convert_dates='coerce'` to `convert_objects` to force datetimelike conversions (or set to `NaT`) for invalid values, turned off by default, returns datetime64[ns] dtype
- groupby operations to respect dtype inputs wherever possible, even if intermediate casting is required (obviously if the input are ints and nans are resulting, this is casted),
all cython functions are implemented
- auto generation of most groupby functions by type is now in `generated_code.py`
e.g. (group_add,group_mean)
- added full `float32/int16/int8` support for all numeric operations, including (`diff, backfill, pad, take`)
- added `dtype` display to show on Series as a default
BUG:
- fixed up tests from from_records to use record arrays directly
NOTE: using tuples will remove dtype info from the input stream (using a record array is ok though!)
- fixed merging to correctly merge on multiple dtypes with blocks (e.g. float64 and float32 in other merger)
- (#2623 can be fixed, but is dependent on #2752)
- fixed #2778, bug in pad/backfill with 0-len frame
- fixed very obscure bug in DataFrame.from_records with dictionary and columns passed and hash randomization is on!
- integer upcasts will now happend on `where` when using inplace ops (#2793)
TST:
- tests added for merging changes, astype, convert
- fixes for test_excel on 32-bit
- fixed test_resample_median_bug_1688
- separated out test_from_records_dictlike
- added tests for (GH #797)
- added lots of tests for`where`
DOC:
- added DataTypes section in Data Structres intro
- whatsnew examples
_It would be really helpful if some users could give this a test run before merging. I have put in test cases for numeric operations, combining with DataFrame and Series, but I am sure there are some corner cases that were missed_
```
In [17]: df1 = DataFrame(randn(8, 1), columns = ['A'], dtype = 'float32')
In [18]: df1
Out[18]:
A
0 -0.007220
1 -0.236432
2 2.427172
3 -0.998639
4 -1.039410
5 0.336029
6 0.832988
7 -0.413241
In [19]: df1.dtypes
Out[19]: A float32
In [20]: df2 = DataFrame(dict( A = Series(randn(8),dtype='float16'),
B = Series(randn(8)),
C = Series(randn(8),dtype='uint8') ))
In [22]: df2
Out[22]:
A B C
0 1.150391 -1.033296 0
1 0.123047 1.915564 0
2 0.151367 -0.489826 0
3 -0.565430 -0.734238 0
4 -0.352295 -0.451430 0
5 -0.618164 0.673102 255
6 1.554688 0.322035 0
7 0.160767 0.420718 0
In [23]: df2.dtypes
Out[23]:
A float16
B float64
C uint8
In [24]: # here you get some upcasting
In [25]: df3 = df1.reindex_like(df2).fillna(value=0.0) + df2
In [26]: df3
Out[26]:
A B C
0 1.143170 -1.033296 0
1 -0.113385 1.915564 0
2 2.578539 -0.489826 0
3 -1.564069 -0.734238 0
4 -1.391705 -0.451430 0
5 -0.282135 0.673102 255
6 2.387676 0.322035 0
7 -0.252475 0.420718 0
In [27]: df3.dtypes
Out[27]:
A float32
B float64
C float64
```
the example from #622
```
In [23]: a = np.array(np.random.randint(10, size=1e6),dtype='int32')
In [24]: b = np.array(np.random.randint(10, size=1e6),dtype='int64')
In [25]: df = pandas.DataFrame(dict(a = a, b = b))
In [26]: df.dtypes
Out[26]:
a int32
b int64
```
Conversion examples
```
# conversion of dtypes
In [81]: df3.astype('float32').dtypes
Out[81]:
A float32
B float32
C float32
# mixed type conversions
In [82]: df3['D'] = '1.'
In [83]: df3['E'] = '1'
In [84]: df3.convert_objects(convert_numeric=True).dtypes
Out[84]:
A float32
B float64
C float64
D float64
E int64
# same, but specific dtype conversion
In [85]: df3['D'] = df3['D'].astype('float16')
In [86]: df3['E'] = df3['E'].astype('int32')
In [87]: df3.dtypes
Out[87]:
A float32
B float64
C float64
D float16
E int32
# forcing date coercion
In [18]: s = Series([datetime(2001,1,1,0,0), 'foo', 1.0, 1,
....: Timestamp('20010104'), '20010105'],dtype='O')
....:
In [19]: s.convert_objects(convert_dates='coerce')
Out[19]:
0 2001-01-01 00:00:00
1 NaT
2 NaT
3 NaT
4 2001-01-04 00:00:00
5 2001-01-05 00:00:00
Dtype: datetime64[ns]
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2709 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2709/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2709/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2709/events | https://github.com/pandas-dev/pandas/issues/2709 | 10,131,107 | MDU6SXNzdWUxMDEzMTEwNw== | 2,709 | BLD: add 64-bit build machine to travis | {
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} | 1 | "2013-01-20T01:45:31Z" | "2013-04-08T05:43:04Z" | "2013-04-08T05:43:04Z" | MEMBER | null | cf #2430
```
I'm still having problems with this:
data = ('a:b\n'
'c:d:e\n'
'f:g:h:i\n'
'j:k\n'
'ln\n')
##if first row does not match longest line length, you error out doing this
print pandas.read_csv(StringIO(data),header=None,sep=':')
##even if u specify names..
print pandas.read_csv(StringIO(data),header=None,sep=':',names=list(np.arange(4)))
It would be really awesome if there was a way to force the parser to expect a fixed number of columns.
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2712 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2712/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2712/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2712/events | https://github.com/pandas-dev/pandas/pull/2712 | 10,132,741 | MDExOlB1bGxSZXF1ZXN0Mzc1MzkwMA== | 2,712 | DOC: whatsnew example for PR #2595 | {
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} | [] | closed | false | null | [] | null | 2 | "2013-01-20T04:04:05Z" | "2014-06-16T01:01:37Z" | "2013-01-30T22:03:49Z" | CONTRIBUTOR | null | just a small whatsnew doc for the datetime64[ns] example when using np.nan to indicate NaT
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} | 1 | "2013-01-20T05:18:32Z" | "2013-01-20T16:39:57Z" | "2013-01-20T16:39:57Z" | CONTRIBUTOR | null | I'm getting an error with the segment option now. When I go to the query explorer, my segments look like this: "gaid::707967480"
http://ga-dev-tools.appspot.com/explorer/
So the error I'm getting is related to that string construction - is this a bug, or is this somehow my segment ids?
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-9-7b6938ee12f2> in <module>()
----> 1 df = ga.read_ga(['pagePath', 'date'], dimensions=['avgTimeonPage'], start_date='2012-09-01', segment=['742193199'], secrets='./client_secrets.json')
2 df
C:\Python27\lib\site-packages\pandas-0.10.0-py2.7-win32.egg\pandas\io\ga.pyc in read_ga(metrics, dimensions, start_date, **kwargs)
94 reader = GAnalytics(**reader_kwds)
95 return reader.get_data(metrics=metrics, start_date=start_date,
---> 96 dimensions=dimensions, **kwargs)
97
98 class OAuthDataReader(object):
C:\Python27\lib\site-packages\pandas-0.10.0-py2.7-win32.egg\pandas\io\ga.pyc in get_data(self, metrics, start_date, end_date, dimensions, segment, filters, start_index, max_results, index_col, parse_dates, keep_date_col, date_parser, na_values, converters, sort, dayfirst, account_name, account_id, property_name, property_id, profile_name, profile_id, chunksize)
283
284 if chunksize is None:
--> 285 return _read(start_index, max_results)
286
287 def iterator():
C:\Python27\lib\site-packages\pandas-0.10.0-py2.7-win32.egg\pandas\io\ga.pyc in _read(start, result_size)
279 except HttpError, inst:
280 raise ValueError('Google API error %s: %s' % (inst.resp.status,
--> 281 inst._get_reason()))
282
283
ValueError: Google API error 400: Invalid value 'ga:742193199' for segment.
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} | 6 | "2013-01-20T21:29:39Z" | "2013-09-16T15:07:10Z" | "2013-09-16T15:07:10Z" | CONTRIBUTOR | null | ```
In [22]: df
Out[22]:
A A B C
0 0.148734 -0.041765 1.575866 1.422524
1 0.188963 2.086219 1.595425 0.539364
2 1.498709 -0.338857 -0.210994 0.984697
3 -0.197789 -0.093690 -0.421010 0.710587
In [23]: df.ix[:, 'D'] = ['X', 'Y', 'Z', 'V']
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-23-e2003f3ca30c> in <module>()
----> 1 df.ix[:, 'D'] = ['X', 'Y', 'Z', 'V']
/Users/changshe/code/pandas/pandas/core/indexing.pyc in __setitem__(self, key, value)
69 raise IndexingError('only tuples of length <= %d supported',
70 self.ndim)
---> 71 indexer = self._convert_tuple(key)
72 else:
73 indexer = self._convert_to_indexer(key)
/Users/changshe/code/pandas/pandas/core/indexing.pyc in _convert_tuple(self, key)
78 keyidx = []
79 for i, k in enumerate(key):
---> 80 idx = self._convert_to_indexer(k, axis=i)
81 keyidx.append(idx)
82 return tuple(keyidx)
/Users/changshe/code/pandas/pandas/core/indexing.pyc in _convert_to_indexer(self, obj, axis)
535 return indexer
536 else:
--> 537 return labels.get_loc(obj)
538
539 def _tuplify(self, loc):
/Users/changshe/code/pandas/pandas/core/index.pyc in get_loc(self, key)
716 loc : int if unique index, possibly slice or mask if not
717 """
--> 718 return self._engine.get_loc(key)
719
720 def get_value(self, series, key):
/Users/changshe/code/pandas/pandas/index.so in pandas.index.IndexEngine.get_loc (pandas/index.c:3222)()
/Users/changshe/code/pandas/pandas/index.so in pandas.index.IndexEngine.get_loc (pandas/index.c:3053)()
/Users/changshe/code/pandas/pandas/index.so in pandas.index.IndexEngine._get_loc_duplicates (pandas/index.c:3389)()
KeyError: 'D'
```
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} | 2 | "2013-01-20T22:00:20Z" | "2016-07-24T04:24:45Z" | "2016-07-24T03:59:04Z" | MEMBER | null | cf #2706
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} | 21 | "2013-01-21T09:14:33Z" | "2014-03-09T05:41:21Z" | "2014-02-07T21:44:14Z" | CONTRIBUTOR | null | Currently, read_frame and write_frame in sql are specific to sqlite/mysql dialects (see #4163).
Rather than adding all possible dialects to pandas, another option is to detect whether sqlalchemy is installed and prefer to use its DB support.
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https://api.github.com/repos/pandas-dev/pandas/issues/2718 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2718/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2718/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2718/events | https://github.com/pandas-dev/pandas/issues/2718 | 10,160,730 | MDU6SXNzdWUxMDE2MDczMA== | 2,718 | BUG: should astype only TRY to convert string columns? | {
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} | 4 | "2013-01-21T15:36:31Z" | "2013-01-22T02:22:16Z" | "2013-01-22T02:22:16Z" | CONTRIBUTOR | null | should astype TRY to convert string columns (e.g. if the value of the string column is like '5'), or skip if its an exception (and just return it), rather than raise an exception on the whole operation?
I can fix for 0.10.2
```
In [4]: df = pd.DataFrame({ 'a' : 'foo', 'b' : 1. },index=np.arange(10))
In [6]: df .dtypes
Out[6]:
a object
b float64
In [8]: df.astype('float64')
/mnt/home/jreback/pandas/pandas/core/internals.pyc in astype(self, dtype)
613 new_blocks = []
614 for block in self.blocks:
--> 615 newb = make_block(com._astype_nansafe(block.values, dtype),
616 block.items, block.ref_items)
617 new_blocks.append(newb)
/mnt/home/jreback/pandas/pandas/core/common.pyc in _astype_nansafe(arr, dtype)
1058 return lib.astype_intsafe(arr.ravel(), dtype).reshape(arr.shape)
1059
-> 1060 return arr.astype(dtype)
1061
1062
ValueError: could not convert string to float: foo
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2719 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2719/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2719/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2719/events | https://github.com/pandas-dev/pandas/issues/2719 | 10,163,149 | MDU6SXNzdWUxMDE2MzE0OQ== | 2,719 | BUG? Reshaping a Series | {
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} | 3 | "2013-01-21T16:41:18Z" | "2013-12-04T00:40:06Z" | "2013-03-21T18:10:03Z" | NONE | null | It looks to me like a bug in pandas.Series.
```
a = pd.Series([1,2,3,4])
b = a.reshape(2,2)
b
```
b has type Series but can not be displayed, the last statement gives exception, very lengthy, the last line is "TypeError: %d format: a number is required, not numpy.ndarray". b.shape returns (2,2), which contradicts its type Series. I am guessing perhaps pandas.Series does not implement reshape function and I am calling the version from np.array. I am at pandas 0.9.1. Some discussion on stackoverflow is here http://stackoverflow.com/questions/14390224/reshape-of-pandas-series
If it does not make sense to reshape a Series, should it throw an exception?
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https://api.github.com/repos/pandas-dev/pandas/issues/2720 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2720/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2720/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2720/events | https://github.com/pandas-dev/pandas/issues/2720 | 10,169,267 | MDU6SXNzdWUxMDE2OTI2Nw== | 2,720 | ENH: stat ops on mixed numeric dtypes | {
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```
(Pdb) p self.mixed_float.dtypes
A float32
B float32
C float16
D float64
# this coerces (no way around this)
(Pdb) p self.mixed_float.sum(1).dtype
dtype('float64')
# question is here - I can do block by block computations to give a mixed dtype result (slightly slower I guess.....then what about overflow issues?)
(Pdb) p self.mixed_float.sum(0).dtype
dtype('float64')
(Pdb) p self.mixed_float.sum(0)
A -11.033966
B 0.356152
C 2.990944
D -3.296513
# ok if a uniform dtype (works now)
(Pdb) p self.mixed_float.reindex(columns=['A','B']).sum().dtype
dtype('float32')
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2721 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2721/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2721/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2721/events | https://github.com/pandas-dev/pandas/issues/2721 | 10,172,865 | MDU6SXNzdWUxMDE3Mjg2NQ== | 2,721 | Implement deep copy option in Series.copy | {
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} | 9 | "2013-01-21T21:48:29Z" | "2013-08-21T23:18:24Z" | "2013-07-29T10:24:17Z" | NONE | null | Not quite sure if this is intentional behavior, but noticed that df.copy() accepts argument deep and series.copy() does not.
> > > from pandas import Series, DataFrame
> > > s=Series([1,2,3])
> > > df=DataFrame(s)
> > > df.copy(deep=True)
> > > 0
> > > 0 1
> > > 1 2
> > > 2 3
> > > s.copy(deep=True)
> > > Traceback (most recent call last):
> > > File "<stdin>", line 1, in <module>
> > > TypeError: copy() got an unexpected keyword argument 'deep'
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https://api.github.com/repos/pandas-dev/pandas/issues/2722 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2722/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2722/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2722/events | https://github.com/pandas-dev/pandas/issues/2722 | 10,190,905 | MDU6SXNzdWUxMDE5MDkwNQ== | 2,722 | Some dates cause error when calling dataframe.convert_objects | {
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} | 5 | "2013-01-22T12:55:22Z" | "2013-09-21T14:21:39Z" | "2013-09-21T14:21:39Z" | NONE | null | - Example codes:
<pre>
li = [datetime.datetime(3800, 1, 1), ]
li_df = DataFrame(li)
li_df.convert_objects()
</pre>
then raise the following exception:
<pre>
ValueError Traceback (most recent call last)
<ipython-input-112-1cb5a6d822e9> in <module>()
----> 1 li_df.convert_objects()
/usr/local/lib/python2.6/dist-packages/pandas-0.10.1.dev_6e2b6ea-py2.6-linux-x86_64.egg/pandas/core/frame.pyc in convert_objects(self, convert_dates)
1661 for col, s in self.iteritems():
1662 if s.dtype == np.object_:
-> 1663 new_data[col] = convert_f(s)
1664 else:
1665 new_data[col] = s
/usr/local/lib/python2.6/dist-packages/pandas-0.10.1.dev_6e2b6ea-py2.6-linux-x86_64.egg/pandas/core/frame.pyc in <lambda>(x)
1656 new_data = {}
1657 convert_f = lambda x: lib.maybe_convert_objects(
-> 1658 x, convert_datetime=convert_dates)
1659
1660 # TODO: could be more efficient taking advantage of the block
/usr/local/lib/python2.6/dist-packages/pandas-0.10.1.dev_6e2b6ea-py2.6-linux-x86_64.egg/pandas/lib.so in pandas.lib.maybe_convert_objects (pandas/lib.c:33656)()
/usr/local/lib/python2.6/dist-packages/pandas-0.10.1.dev_6e2b6ea-py2.6-linux-x86_64.egg/pandas/tslib.so in pandas.tslib.convert_to_tsobject (pandas/tslib.c:10796)()
/usr/local/lib/python2.6/dist-packages/pandas-0.10.1.dev_6e2b6ea-py2.6-linux-x86_64.egg/pandas/tslib.so in pandas.tslib._check_dts_bounds (pandas/tslib.c:11827)()
ValueError: Out of bounds nanosecond timestamp: 3800-01-01 00:00:00
</pre>
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} | 1 | "2013-01-22T13:10:30Z" | "2014-06-20T08:05:53Z" | "2013-02-10T21:27:11Z" | CONTRIBUTOR | null | the default is still 1, so nothing should change if one wants a 'classic' lag plot
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https://api.github.com/repos/pandas-dev/pandas/issues/2724 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2724/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2724/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2724/events | https://github.com/pandas-dev/pandas/pull/2724 | 10,192,509 | MDExOlB1bGxSZXF1ZXN0Mzc4MDIzMw== | 2,724 | dataframe.convert_objects now ignores datetime64 outside supported timeperiod | {
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} | 3 | "2013-01-22T20:42:23Z" | "2013-03-09T03:30:10Z" | "2013-03-09T03:30:10Z" | NONE | null | ```
df = DataFrame(index=range(100))
df['x'] = np.random.randn(100)
df['y'] = df['x']
df.plot(x='x', y='y', linestyle='None', marker='o')
```
I expected to see column 'y' plotted against column 'x' and, as such, to see scatter points along the 45-degree line. Instead, I get a strange scatter plot. Am I doing something wrong or is this a bug? If it is a bug, is there a workaround?
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} | 4 | "2013-01-22T21:18:10Z" | "2016-10-12T23:03:29Z" | "2013-02-09T21:08:57Z" | NONE | null | ```
df = DataFrame(index=range(99, -1, -1))
df['y'] = range(100)
df.plot()
```
I observe a 45-degree line while what I expect to see is the -45-degree line (from top left corner to bottom right corner). Am I doing something wrong?
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} | 4 | "2013-01-22T22:22:18Z" | "2013-02-26T22:24:32Z" | "2013-02-11T00:32:19Z" | CONTRIBUTOR | null | When _df.index.inferred_type_ is _'floating'_, _df.ix[start:end]_ uses only label-based indexing only when **both** _start_ and _end_ are floating on the ___getitem___ side but when **either** _start_ or _end_ are floating on the ___setitem___ side (where _start_ and _end_ are both either integers or floats within epsilon of integers)
``` python
In [2]: df=p.DataFrame(["a", "b", "c", "d"], index=[1.00, 2.00, 3.00, 4.00])
In [3]: df.ix[1.0:4] # uses positional indexing to get last three rows
Out[3]:
0
2 b
3 c
4 d
In [4]: df.ix[1.0:4.0] # uses label-based indexing to get all four rows
Out[4]:
0
1 a
2 b
3 c
4 d
In [5]: df.ix[1.0:4] = "y" # uses label-based indexing to set all four rows
In [6]: df
Out[6]:
0
1 y
2 y
3 y
4 y
In [7]: df.ix[1.0:4.0] = "z" # uses label-based indexing to set all four rows
In [8]: df
Out[8]:
0
1 z
2 z
3 z
4 z
```
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} | 13 | "2013-01-23T00:06:26Z" | "2021-08-11T10:25:46Z" | null | NONE | null | dataframe.copy() should happen atomically/be threadsafe, meaning that it should produce a consistent dataframe even if the call to .copy() is made while another thread is deleting entries from the dataframe, or if another thread calls a deletion method while the call to .copy() is working (in other words, i guess .copy() should acquire a lock that prevents mutation during the copy). That is, the following code, which crashes in 0.7.3, should succeed:
``` python
import pandas
import threading
df = pandas.DataFrame()
def mutateDf(df):
while True:
df[0] = pandas.Series([1,2,3])
del df[0]
def readDf(df):
while True:
dfCopy = df.copy()
if 0 in dfCopy and 1 in dfCopy[0]:
a = dfCopy[0][1]
t1 = threading.Thread(target=mutateDf, args=(df,))
t2 = threading.Thread(target=readDf, args=(df,))
t1.start()
t2.start()
```
```
Exception in thread Thread-3:
Traceback (most recent call last):
File "/usr/lib/python2.7/threading.py", line 551, in __bootstrap_inner
self.run()
File "/usr/lib/python2.7/threading.py", line 504, in run
self.__target(*self.__args, **self.__kwargs)
File "<ipython-input-5-8aef72c7f1b4>", line 4, in readDf
if 0 in dfCopy and 1 in dfCopy[0]:
File "/usr/local/lib/python2.7/dist-packages/pandas-0.7.3-py2.7-linux-x86_64.egg/pandas/core/frame.py", line 1458, in __getitem__
return self._get_item_cache(key)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.7.3-py2.7-linux-x86_64.egg/pandas/core/generic.py", line 294, in _get_item_cache
values = self._data.get(item)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.7.3-py2.7-linux-x86_64.egg/pandas/core/internals.py", line 625, in get
_, block = self._find_block(item)
TypeError: 'NoneType' object is not iterable
```
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} | 1 | "2013-01-23T00:07:19Z" | "2013-04-08T04:59:30Z" | "2013-04-08T04:59:30Z" | NONE | null | http://pandas.pydata.org/pandas-docs/stable/gotchas.html should note that DataFrame objects are not threadsafe: http://stackoverflow.com/questions/13592618/python-pandas-dataframe-thread-safe
(as of 0.7.3, they still are not threadsafe afaict)
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https://api.github.com/repos/pandas-dev/pandas/issues/2730 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2730/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2730/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2730/events | https://github.com/pandas-dev/pandas/pull/2730 | 10,217,593 | MDExOlB1bGxSZXF1ZXN0Mzc5MjkzNg== | 2,730 | BUG: Mismatch between get and set behavior for slices of floating indices (fixes #2727) | {
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} | [] | closed | false | null | [] | null | 1 | "2013-01-23T01:32:00Z" | "2013-02-08T15:17:53Z" | "2013-01-30T17:45:53Z" | CONTRIBUTOR | null | fixes #2727: __NDFrameIndexer._convert_to_indexer_ modified to match behavior in __NDFrameIndexer._get_slice_axis_, new tests added to existing unit test
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https://api.github.com/repos/pandas-dev/pandas/issues/2731 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2731/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2731/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2731/events | https://github.com/pandas-dev/pandas/issues/2731 | 10,220,360 | MDU6SXNzdWUxMDIyMDM2MA== | 2,731 | BUG? Inconsistent index type inferencing for mixed integer/floating, index=list vs index=Index(list) | {
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```
In [2]: df=p.DataFrame(["a", "b", "c", "d", "e"], index=[1.0, 2.5, 3, 4.5, 5.0])
In [3]: df.index.inferred_type
Out[3]: 'floating'
In [4]: df=p.DataFrame(["a", "b", "c", "d", "e"], index=p.Index([1.0, 2.5, 3, 4.5, 5.0]))
In [5]: df.index.inferred_type
Out[5]: 'mixed-integer-float'
```
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} | 1 | "2013-01-23T08:15:48Z" | "2014-04-23T19:36:56Z" | "2013-03-14T03:48:03Z" | CONTRIBUTOR | null | I have a case where I wrote a requirements file to install pandas. Included in the requirements file is an entry to install numpy alongside. The installation fails because pandas is trying to do an import (in its setup.py) of numpy which isn't installed yet.
Due to the fact that either setuptools or distribute is used for the installation of pandas, I think it's safe to trust that setuptools/distribute will ensure that the necessary version of numpy exists before installing pandas.
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} | 10 | "2013-01-23T10:30:39Z" | "2014-03-01T07:45:12Z" | "2014-03-01T07:45:12Z" | NONE | null | If I have data (without a header) separated by "," usecols works, however if the data is separated by white space it doesn't seem to work:
``` python
In [30]: data = '1,2,3\n4,5,6\n7,8,9'
In [31]: pd.read_csv(StringIO(data), usecols=[0, 1], header=None)
Out[31]:
0 1
0 1 2
1 4 5
2 7 8
In [32]: data = '1 2 3\n4 5 6\n7 8 9'
In [33]: pd.read_csv(StringIO(data), sep='\s+', header=None)
Out[33]:
0 1 2
0 1 2 3
1 4 5 6
2 7 8 9
In [34]: pd.read_csv(StringIO(data), sep='\s+', usecols=[0, 1], header=None)
Out[34]:
0 1 2
0 1 2 3
1 4 5 6
2 7 8 9
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2734 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2734/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2734/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2734/events | https://github.com/pandas-dev/pandas/pull/2734 | 10,237,738 | MDExOlB1bGxSZXF1ZXN0MzgwMjcyOA== | 2,734 | ENH: provide squeeze method for removing 1-len dimensions, close (GH #2544) | {
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} | 1 | "2013-01-23T16:09:37Z" | "2014-06-18T21:31:15Z" | "2013-02-10T21:31:31Z" | CONTRIBUTOR | null | ```
In [1]: p = Panel(randn(3,4,4),items=['ItemA','ItemB','ItemC'],
...: major_axis=date_range('20010102',periods=4),
...: minor_axis=['A','B','C','D'])
...:
In [2]: p
Out[2]:
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 4 (major_axis) x 4 (minor_axis)
Items axis: ItemA to ItemC
Major_axis axis: 2001-01-02 00:00:00 to 2001-01-05 00:00:00
Minor_axis axis: A to D
In [3]: p.reindex(items=['ItemA']).squeeze()
Out[3]:
A B C D
2001-01-02 -1.182806 -1.835015 -0.008415 1.665945
2001-01-03 -0.347332 -0.257820 0.167772 -1.193518
2001-01-04 -0.502643 0.055733 0.733034 -1.070536
2001-01-05 -1.900588 -1.728706 1.060564 -1.138837
In [4]: p.reindex(items=['ItemA'],minor=['B']).squeeze()
Out[4]:
2001-01-02 -1.835015
2001-01-03 -0.257820
2001-01-04 0.055733
2001-01-05 -1.728706
Freq: D, Name: B
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2735 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2735/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2735/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2735/events | https://github.com/pandas-dev/pandas/issues/2735 | 10,242,055 | MDU6SXNzdWUxMDI0MjA1NQ== | 2,735 | Add suppress option to pd.set_option | {
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} | 2 | "2013-01-23T17:28:13Z" | "2013-03-11T14:59:23Z" | "2013-03-11T14:59:23Z" | CONTRIBUTOR | null | I do computational physics and economics and often generate data that is very close to zero (on the order of 1e-14 to 1e-16). When printing out a numpy array I often use the command
``` python
import numpy as np
np.set_printoptions(suppress=True)
```
This will cause items close to zero to be displayed as zero.
I think this would be a very simple addition to `pandas.set_option`. I'll try to work on it, but someone else who already knows the format of extending `pandas.set_option` will be able to get it done much more quickly.
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https://api.github.com/repos/pandas-dev/pandas/issues/2736 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2736/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2736/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2736/events | https://github.com/pandas-dev/pandas/issues/2736 | 10,243,501 | MDU6SXNzdWUxMDI0MzUwMQ== | 2,736 | Add "corner" function | {
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} | [] | closed | false | null | [] | null | 0 | "2013-01-23T18:04:49Z" | "2013-02-02T01:24:11Z" | "2013-02-02T01:24:11Z" | MEMBER | null | Something like [this](http://cran.r-project.org/web/packages/useful/useful.pdf).
I often have `DataFrame`s that have 4 - 5 `MultiIndex` levels on each axis and I can't get a visual idea about the structure unless I do something like
``` python
df.head().T.head().T
```
Of course, in the case that the indices are simple I can just slice the `ix` attribute, but for `MultiIndex`ed `DataFrame`s I can't do that.
I've written the following as as starter:
``` python
def corner(df, r=5, c=5):
return df.head(r).T.head(c).T
```
Aside from being ugly, this code shouldn't have any issues. I'm not sure if the case of many-leveled `MultiIndex`es is very common, so feel free to close this if it doesn't seem that useful. I'd also like to know if there's another way to achieve this, if possible.
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https://api.github.com/repos/pandas-dev/pandas/issues/2737 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2737/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2737/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2737/events | https://github.com/pandas-dev/pandas/pull/2737 | 10,244,580 | MDExOlB1bGxSZXF1ZXN0MzgwNTc0OA== | 2,737 | improving docstring for swaplevel method | {
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} | 9 | "2013-01-23T18:42:41Z" | "2014-04-04T15:03:29Z" | "2014-01-24T14:35:14Z" | NONE | null | Calling pandas.io.sql.read_frame can results in data frame with duplicate column names. For example when SQL query contains joins on tables with duplicate columns.
Data frames with duplicate column names cause errors in many pandas functions. I can't even rename columns as df.columns = new_columns generates errors.
I think correct behavior would be for pandas.io.sql.read_frame have an option to "deduplicate" column names (for example by adding a number) or generate an error with duplicate column names.
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} | 4 | "2013-01-23T19:16:43Z" | "2014-06-12T08:13:07Z" | "2013-02-10T21:33:29Z" | NONE | null | Doesn't cover complex numbers for now (no formatter implemented in format.py)
I think the "supress" kwd choice in numpy is not that awesome. So I borrowed `chop` from
the mathematica parlance.
#2734
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} | 1 | "2013-01-23T19:30:53Z" | "2014-06-30T05:04:02Z" | "2013-02-10T21:53:47Z" | CONTRIBUTOR | null | Pandas will normally fail to install if numpy is not installed on the target host. This should normally be resolved by specifying numpy as a dependency but Pandas will still require that numpy is installed first due to its need of the numpy include files.
This patch resolves this problem by changing the way the numpy include path is retrieved so that it is not necessary that numpy is first installed before pandas - they can both be installed together; numpy coming first of course.
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} | 10 | "2013-01-23T21:45:55Z" | "2016-08-01T21:17:30Z" | "2016-08-01T21:17:12Z" | CONTRIBUTOR | null | This is a weird one from [StackOverflow](http://stackoverflow.com/questions/14488697/pandas-read-csv-failing-on-columns-with-null-characters), this file has some `\x00`s which seem to be ignored when printing but confuse `read_csv`:
```
x = 'x,y\n \x00\x00\x00,Reg\n \x00\x00\x00,Reg\nI,Swp\nI,Swp\n'
X = StringIO(x)
In [3]: pd.read_csv(X)
Out[3]:
x y
0
1 NaN NaN
2 I Swp
3 I Swp
In [4]: print x
x,y
,Reg
,Reg
I,Swp
I,Swp
```
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} | 2 | "2013-01-24T00:01:57Z" | "2013-04-01T19:58:06Z" | "2013-04-01T19:58:06Z" | NONE | null | Used versions: Python 2.7.3 Pandas 0.10.1
Code:
from pandas import Period
from datetime import datetime
Period(datetime(2013, 1, 1, 0, 0), freq='W-SAT')
outputs: Period('2012-12-30/2013-01-05', 'W-SAT') - correct
Period(datetime(2013, 1, 1, 0, 0), freq='W-SAT').end_time
outputs: Timestamp: 2013-01-11 23:59:59.999999999
Is it expected behaviour ? Week end on 2013-01-11 not on 2013-01-05 ?
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} | 3 | "2013-01-24T00:44:16Z" | "2013-03-03T20:15:58Z" | "2013-03-03T20:15:58Z" | NONE | null | Hi,
If we specify custom rgb 3-ples for rcParams['axes.color_cycle'], DataFrame.plot breaks because _get_colors expects strings (the usual rcParams['axes.color_cycle'] = ['b','g'...] etc).
Test case:
rcParams['axes.color_cycle'] = [(1,1,1),(.1,.1,.1)]
DataFrame({'a':[1,1], 'b':[2,2]}).plot()
Seems like having _get_color return a list instead of taking the list and joining all the elements would fix it?
Thanks!
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} | 1 | "2013-01-24T00:54:00Z" | "2014-05-29T05:53:27Z" | "2014-05-29T05:53:27Z" | NONE | null | I'm doing some data processing in pandas, "pivoting" the dataframe and saving it to a sqlite table.
The data processing is rather expensive and, for performance reasons, I'd like to process it once with pandas and write the cross-tabulated data frame into a sqlite table for later queries. This strategy works well until I get over 1000 columns when I run into the following sqlite limitation (See "9. Maximum Number Of Host Parameters In A Single SQL Statement" on http://www.sqlite.org/limits.html)
> To prevent excessive memory allocations, the maximum value of a host parameter number
> is SQLITE_MAX_VARIABLE_NUMBER, which defaults to 999.
The full traceback is
```
Traceback (most recent call last):
File "create_sqlite.py", line 28, in <module>
sql.write_frame(final, name='cond', con=conn)
File "/usr/local/apps/murdock/env/lib/python2.6/site-packages/pandas/io/sql.py", line 210, in write_frame
func(frame, name, safe_names, cur)
File "/usr/local/apps/murdock/env/lib/python2.6/site-packages/pandas/io/sql.py", line 222, in _write_sqlite
cur.executemany(insert_query, data)
sqlite3.OperationalError: too many SQL variables
```
Do you think it would be possible to get around the issue by chunking the insert into smaller statements? Any ideas on a workaround (aside from recompiling sqlite)?
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} | 19 | "2013-01-24T10:30:18Z" | "2013-09-05T15:00:16Z" | "2013-03-22T18:54:28Z" | CONTRIBUTOR | null | this used to work in 0.10 but now fails in 0.10.1:
```
import pandas
df = pandas.DataFrame(index=[1,2])
df['test'] = [1,2]
df['test'][[True, False]] = [0]
```
Now gives:
ValueError: Length of replacements must equal series length
possibly related to closed issue https://github.com/pydata/pandas/issues/2703
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} | 7 | "2013-01-24T13:22:45Z" | "2013-03-01T18:30:55Z" | "2013-03-01T18:30:55Z" | CONTRIBUTOR | null | this fails because we are putmasking on a dtype=int ndarray
need to cast if this is the case (in series.where)
```
import pandas
df = pandas.DataFrame(index=[1,2])
df['test'] = [1,2]
df['test'][[True, False]] = pandas.Series([0],index=[1])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-9-7738933c68bb> in <module>()
----> 1 df['test'][[True,False]] = pandas.Series([0],index=[1])
/mnt/home/jreback/pandas/pandas/core/series.pyc in __setitem__(self, key, value)
677 if _is_bool_indexer(key):
678 key = self._check_bool_indexer(key)
--> 679 self.where(~key,value,inplace=True)
680 else:
681 self._set_with(key, value)
/mnt/home/jreback/pandas/pandas/core/series.pyc in where(self, cond, other, inplace)
630 raise ValueError('Length of replacements must equal series length')
631
--> 632 np.putmask(ser, ~cond, other)
633
634 return None if inplace else ser
TypeError: array cannot be safely cast to required type
```
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} | 7 | "2013-01-24T13:44:00Z" | "2013-09-03T08:31:48Z" | "2013-02-11T02:17:16Z" | CONTRIBUTOR | null | The following unit-test fails:
``` python
def test_dataframe_clip(lb=-1, ub=1):
df = pd.DataFrame(np.random.randn(1000,2))
lb_mask = df.values <= lb
ub_mask = df.values >= ub
mask = ~lb_mask & ~ub_mask
clipped_df = df.clip(lb, ub)
assert (clipped_df.values[lb_mask] == lb).all()
assert (clipped_df.values[ub_mask] == ub).all()
assert (clipped_df.valus[mask] == df.values[mask]).all()
#
```
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} | 1 | "2013-01-24T16:18:16Z" | "2014-08-04T13:02:02Z" | "2013-02-10T22:06:19Z" | CONTRIBUTOR | null | The test case doesn't fix the issue. Just a test about the `usecols` parameter in `read_csv` when there are whitespaces in the reading data. I'm not sure the test case is worth it. Let me know.
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} | 0 | "2013-01-24T17:45:07Z" | "2014-04-30T19:36:58Z" | "2014-04-30T19:36:58Z" | NONE | null | Would it be possible to include the mode within the describe() function. I think this would be highly useful for some.
Thank You.
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https://api.github.com/repos/pandas-dev/pandas/issues/2750 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2750/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2750/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2750/events | https://github.com/pandas-dev/pandas/pull/2750 | 10,282,116 | MDExOlB1bGxSZXF1ZXN0MzgyNDgzNw== | 2,750 | BUG: DataFrame.clip arguments inconsistent (close #GH 2747) | {
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} | 7 | "2013-01-24T17:56:03Z" | "2013-02-10T21:21:52Z" | "2013-02-10T21:21:14Z" | CONTRIBUTOR | null | arguments are inconsistent to numpy and Series.clip
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https://api.github.com/repos/pandas-dev/pandas/issues/2751 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2751/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2751/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2751/events | https://github.com/pandas-dev/pandas/issues/2751 | 10,309,903 | MDU6SXNzdWUxMDMwOTkwMw== | 2,751 | BUG: datetime64[ns] conversion to object in DataFrame constructor inconsistent | {
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```
In [55]: df = pd.DataFrame({'a':[1,2,4,7],
'b':[1.2, 2.3, 5.1, 6.3],
'c':list('abcd'),
'd':[pd.datetime(2000,1,1) for i in range(4)]})
In [56]: df
Out[56]:
a b c d
0 1 1.2 a 2000-01-01 00:00:00
1 2 2.3 b 2000-01-01 00:00:00
2 4 5.1 c 2000-01-01 00:00:00
3 7 6.3 d 2000-01-01 00:00:00
In [57]: df.dtypes
Out[57]:
a int64
b float64
c object
d object
# this is ok
In [64]: df = pd.DataFrame({'a':[1,2,4,7], 'b':[1.2, 2.3, 5.1, 6.3], 'd':[pd.datetime(2000,1,1) for i in range(4)]})
In [65]: df.dtypes
Out[65]:
a int64
b float64
d datetime64[ns]
```
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} | 10 | "2013-01-25T13:55:02Z" | "2014-06-16T01:01:22Z" | "2013-02-10T20:39:14Z" | CONTRIBUTOR | null | 0 and 1 len ndarrays not inferring dtype correctly
datetimes that are single objects not inferring dtype
mixed datetimes and objects (GH #2751), casting datetimes to object
timedelta64 creation on series subtraction (of datetime64[ns])
astype on datetimes to object are now handled (as well as NaT conversions to np.nan)
astype conversion
```
In [4]: s = pd.Series([datetime.datetime(2001, 1, 2, 0, 0) for i in range(3)])
In [5]: s.dtype
Out[5]: dtype('datetime64[ns]')
In [6]: s[1] = np.nan
In [7]: s
Out[7]:
0 2001-01-02 00:00:00
1 NaT
2 2001-01-02 00:00:00
In [8]: s.dtype
Out[8]: dtype('datetime64[ns]')
In [9]: s = s.astype('O')
In [10]: s
Out[10]:
0 2001-01-02 00:00:00
1 NaN
2 2001-01-02 00:00:00
In [11]: s.dtype
Out[11]: dtype('object')
```
construction with datetimes
```
In [7]: pd.DataFrame({'A' : 1, 'B' : 'foo', 'C' : 'bar',
'D' : pd.Timestamp("20010101"),
'E' : datetime.datetime(2001,1,2,0,0) }, index=np.arange(5)).get_dtype_counts()
Out[7]:
datetime64[ns] 2
int64 1
object 2
In [16]: pd.DataFrame({'a':[1,2,4,7], 'b':[1.2, 2.3, 5.1, 6.3],
'c':list('abcd'),
'd':[datetime.datetime(2000,1,1) for i in range(4)] }).get_dtype_counts()
Out[16]:
datetime64[ns] 1
float64 1
int64 1
object 1
```
1 len ndarrays
```
In [16]: pd.DataFrame({'a': 1., 'b': 2, 'c': 'foo', 'float64' : np.array(1.,dtype='float64'),
'int64' : np.array(1,dtype='int64')}, index=np.arange(10)).get_dtype_counts()
Out[16]:
float64 2
int64 2
object 1
```
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} | 6 | "2013-01-25T18:14:57Z" | "2016-10-12T23:03:29Z" | "2013-04-01T10:30:45Z" | CONTRIBUTOR | null | The first resample starts at 2000-01-03 (as does `foo`) whilst the second resample starts at 2000-01-04, should it?
```
foo = pd.Series(index=pd.bdate_range(datetime.date(2000,1,1),datetime.date(2001,1,1)))
foo.resample("BMS")
foo.resample("BMS").resample("B")
```
_Migrated from [StackOverflow](http://stackoverflow.com/questions/14525942/pandas-resampling-skips-first-date-of-timeseries)._
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} | 13 | "2013-01-25T18:21:21Z" | "2014-09-13T21:48:56Z" | "2014-09-13T21:48:56Z" | CONTRIBUTOR | null | UPDATE from @jorisvandenbossche:
Overview of current status writing nan values (see also tests added in #7100):
- For MySQL using pymysql/MySQLdb nothing works: you get message `Unknown column 'nan' in 'field list'` (see also eg http://stackoverflow.com/questions/23353732/python-pandas-write-to-sql-with-nan-values)
- Numeric columns:
- working for sqlite and postgres
- Only full NaN columns stay `None` in sqlite
- Object columns (eg strings)
- for postgresql: NaN is converted to the string `u'NaN'`, which is not really what we want
- for sqlite it is returned as `None`
- `NaT`:
- postgresql: gives error on inserting "0001-255-255T00:00:00"
- sqlite3: writing works, but reading it with query returns `'-001--1--1 -1:-1:-1.-00001'`
- MSSQL: not working with message "The supplied value is not a valid instance of data type float", see #8088 for more details
---
not sure exactly what sql expects (Nones?) rather than np.nan (or NaT)
https://groups.google.com/forum/?fromgroups#!topic/pydata/lxhnFtuzvWQ
also provide pandas `datetime64[ns]`, instead of `datetime/date` types
https://github.com/pydata/pandas/issues/3532
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https://api.github.com/repos/pandas-dev/pandas/issues/2755 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2755/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2755/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2755/events | https://github.com/pandas-dev/pandas/pull/2755 | 10,323,463 | MDExOlB1bGxSZXF1ZXN0Mzg0NTg5Ng== | 2,755 | BUG: issue in HDFStore with too many selectors in a where | {
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} | 8 | "2013-01-25T19:38:53Z" | "2014-06-25T12:57:27Z" | "2013-02-10T22:10:36Z" | CONTRIBUTOR | null | - this is very hard to reproduce, and can give a ValueError or just crash (as somewhere in numexpr or hdf5 it has trouble parsing the expression, and can run out of memory), easiest solution is just to limit the number of selectors
just to be clear, you really have to do something like this:
select.('df',[ Term('index',['A','B','C'......]) ])
where the term has more than 31 selectors (and less than 61) to produce this error and only sometimes
it triggers.
This doesn't change functionaility at all, as if there are more than the specified number of selectors, it just
does a filter (eg. bring in the whole table and then just reindex), and this is really only an issue
if you actually try to specifiy many vaues that the index can be (which usually isn't a string anyhow)
but I did hit it! so its a 'buglet'
heres the issue in PyTables (but it is actually somewhere in numexpr)
http://sourceforge.net/mailarchive/message.php?msg_id=30390757
- added cleanup code to force removal of output files if the testing is interrupted by ctrl-c
- added dotted (attribute) acces to stores, e.g. store.df == store['df']
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} | 4 | "2013-01-25T20:40:53Z" | "2013-03-18T18:52:00Z" | "2013-03-18T18:26:02Z" | NONE | null | I create a pandas scatter-matrix usng the following code:
``` python
import numpy as np
import pandas as pd
a = np.random.normal(1, 3, 100)
b = np.random.normal(3, 1, 100)
c = np.random.normal(2, 2, 100)
df = pd.DataFrame({'A':a,'B':b,'C':c})
pd.scatter_matrix(df, diagonal='kde')
```
This result in the following scatter-matrix:
The first row has no ytick labels, the 3th column no xtick labels, the 3th item 'C' is not labeled.
Any idea how to complete this plot with the missing labels ?
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} | 3 | "2013-01-25T21:34:44Z" | "2013-02-04T12:07:27Z" | "2013-02-04T12:07:19Z" | CONTRIBUTOR | null | - Improve docstrings and error messages
- Convert final `elif` to `else`: the `elif` condition was guaranteed to be true, so this is a silent code cleanup -- it does not change the behavior of the code.
### Testing
I checked that the `from_items` method was working as usual, and that the new docstrings and error messages looked OK, in the following session.
``` python
>>> pd.DataFrame.from_items([('a', [1,2]), ('b', [3,4])])
a b
0 1 3
1 2 4
>>> pd.DataFrame.from_items([('a', [1,2]), ('b', [3,4])], orient='columns')
a b
0 1 3
1 2 4
>>> pd.DataFrame.from_items([('a', [1,2]), ('b', [3,4])], orient='index')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-14-42f50cd4d05d> in <module>()
----> 1 pd.DataFrame.from_items([('a', [1,2]), ('b', [3,4])], orient='index')
/Users/dan/lib/python/pandas/pandas/core/frame.pyc in from_items(cls, items, columns, orient)
1144 elif orient == 'index':
1145 if columns is None:
-> 1146 raise ValueError("Must pass columns with orient='index'")
1147
1148 keys = _ensure_index(keys)
ValueError: Must pass columns with orient='index'
>>> pd.DataFrame.from_items([('a', [1,2]), ('b', [3,4])], orient='index', columns=['col1', 'col2'])
col1 col2
a 1 2
b 3 4
>>> pd.DataFrame.from_items([('a', [1,2]), ('b', [3,4])], orient='indexXXX', columns=['col1', 'col2']) ---------------------------------------------------------------------------ValueError Traceback (most recent call last)<ipython-input-17-c3448a7551f4> in <module>()----> 1 pd.DataFrame.from_items([('a', [1,2]), ('b', [3,4])], orient='indexXXX', columns=['col1', 'col2'])/Users/dan/lib/python/pandas/pandas/core/frame.pyc in from_items(cls, items, columns, orient)
1152 return cls._from_arrays(data, columns, keys)
1153 else: # pragma: no cover
-> 1154 raise ValueError("'orient' must be either 'columns' or 'index'")
1155
1156 @classmethod
ValueError: 'orient' must be either 'columns' or 'index'
>>> pd.DataFrame.from_items?
Type: instancemethod
String Form:<bound method type.from_items of <class 'pandas.core.frame.DataFrame'>>
File: /Users/dan/lib/python/pandas/pandas/core/frame.py
Definition: pd.DataFrame.from_items(cls, items, columns=None, orient='columns')
Docstring:
Convert (key, value) pairs to DataFrame. The keys will be the axis
index (usually the columns, but depends on the specified
orientation). The values should be arrays or Series.
Parameters
----------
items : sequence of (key, value) pairs
Values should be arrays or Series.
columns : sequence of column labels, optional
Must be passed if orient='index'.
orient : {'columns', 'index'}, default 'columns'
The "orientation" of the data. If the keys of the
input correspond to column labels, pass 'columns'
(default). Otherwise if the keys correspond to the index,
pass 'index'.
Returns
-------
frame : DataFrame
>>>
```
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} | 8 | "2013-01-26T07:45:56Z" | "2014-06-13T11:53:46Z" | "2013-02-10T21:16:38Z" | CONTRIBUTOR | null | Mostly housekeeping, but there were a few functionality additions:
- BUG: The URL's for current month options have been changed by yahoo so I changed the methods to reflect that.
- ENH: Now using lxml to html scraping under the hood (was using BeautifulSoup).
- ENH: When `Options.get_xxx_data()`, where xxx is either `options`, `call`, or `put`, a new instance variable is created. For current month options the variable is simply `self.calls` or `self.puts`. When future month data is retrieved the ivar becomes `self.callsMMYY` or `self.putsMMYY`.
- ENH: `Options.get_near_stock_price` now allows the user to specify optional `month` and `year` kwargs to get data near the stock price for a given expiry date.
- ENH: `Options.get_forward_data` now has optional kwargs `near` and `above_below`. This allows the user to specify if they would like to only return forward looking data for options near the current stock price. This just obtains the data from `Options.get_near_stock_price` instead of `Options.get_xxx_data()`.
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} | 11 | "2013-01-27T10:17:34Z" | "2013-02-11T07:44:07Z" | "2013-02-11T02:17:32Z" | CONTRIBUTOR | null | It seems that DataFrame (both constructor and `astype`) promotes the dtypes np.int[8,16,32] to np.int64 ::
```
>>> pd.DataFrame(data=np.ones((10, 2), dtype=np.int32), dtype=np.int32).get_dtype_counts()
int64 2
```
I'm not sure whether this is a but or on purpose (if so, a note in the docstring would be great).
Is there a specific reason why this is the case (e.g. to accomodate NA values?). If there are no NA's in the frame, can the promotion be turned off?
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https://api.github.com/repos/pandas-dev/pandas/issues/2760 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2760/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2760/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2760/events | https://github.com/pandas-dev/pandas/issues/2760 | 10,358,277 | MDU6SXNzdWUxMDM1ODI3Nw== | 2,760 | Series near-zero subtraction loss of precision | {
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} | [] | closed | false | null | [] | null | 3 | "2013-01-27T20:11:32Z" | "2019-02-06T13:17:52Z" | "2013-01-30T06:09:41Z" | CONTRIBUTOR | null | Unless I am unaware of some preset precision in pandas, I've discovered a bug in what I believe to be some part of the Series subtraction operation in pandas 0.10.1, numpy 1.6.2. I haven't tested in other versions.
Data set:
```
a = [49105.962326,
49106.062271,
49106.112167,
49106.222305,
49106.332290]
s = pd.Series(a)
```
A simple slice to calculate deltas:
```
dt = s[1:] - s[:-1]
```
results in:
```
0 NaN
1 0
2 0
3 0
4 NaN
```
The resultant values (i.e., values stored at `dt[1:3]`) are exactly zero and of type `numpy.float64`. These zeros and the final null value are not correct, which can be shown with conversion to a numpy array and performing the same operation:
```
npa = np.array(s)
npa[1:] - npa[:-1]
```
results in the expected (and correct) values:
```
array([ 0.099945, 0.049896, 0.110138, 0.109985])
```
A little digging didn't reveal that this issue had been already mentioned, but may be related to Issues #2697 and/or #2069?
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} | 1 | "2013-01-27T21:48:41Z" | "2013-02-15T00:29:44Z" | "2013-02-15T00:29:44Z" | CONTRIBUTOR | null | see the example in #2759
should certain operations, e.g. `shift` preserve the dtype of the output object,
simulated like this (rather than return an upcasted version)
this obviously is easier if we had an integer NA
would have to add a fillna keyword to shift to make this work
(so we don't have to guess on the fill value for integer/bool types)
```
mask.astype('O').shift().fillna(0).astype(mask.dtype)
```
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} | 1 | "2013-01-28T10:00:50Z" | "2013-02-08T10:59:33Z" | "2013-02-08T10:59:28Z" | NONE | null | Python 2.5 isn't supported ( #2323 )
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https://api.github.com/repos/pandas-dev/pandas/issues/2763 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2763/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2763/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2763/events | https://github.com/pandas-dev/pandas/issues/2763 | 10,385,548 | MDU6SXNzdWUxMDM4NTU0OA== | 2,763 | groupby first and last modify datatype | {
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} | 1 | "2013-01-28T18:06:38Z" | "2013-03-24T19:51:29Z" | "2013-03-24T19:50:25Z" | NONE | null | Using `groupby(level=0).first()` or `groupby(level=0).last()` to eliminate duplicate index entries modifies integer data types to float.
```
import pandas as pd
idx = range(10)
idx.append(9)
df = pd.Series(data=range(11), index=idx, name='IntCol')
print df
print df.dtype
0 0
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
9 10
Name: IntCol
int64
df2 = df.groupby(level=0).first()
print df2
print df2.dtype
0 0
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
float64
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2764 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2764/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2764/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2764/events | https://github.com/pandas-dev/pandas/issues/2764 | 10,391,794 | MDU6SXNzdWUxMDM5MTc5NA== | 2,764 | resample time series with 2 quarters | {
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} | 2 | "2013-01-28T20:49:06Z" | "2016-04-08T13:13:51Z" | "2016-04-08T13:13:51Z" | NONE | null | see this question on so: http://stackoverflow.com/questions/14569223/timegrouper-pandas
``` python
In [213]: idx = pd.date_range('2008-07-01', '2010-12-01', freq='MS')
In [214]: s = pd.Series(np.random.randn(idx.size), index=idx)
In [215]: s.index[0] + pd.datetools.QuarterEnd(startingMonth=12) * 2
Out[215]: <Timestamp: 2008-12-31 00:00:00>
In [216]: s.resample(pd.datetools.QuarterEnd(startingMonth=12) * 2)
Out[216]:
2008-09-30 -0.220156
2009-03-31 -0.392138
2009-09-30 -0.720243
2010-03-31 0.261541
2010-09-30 0.444490
2011-03-31 -0.093383
Freq: 2Q-DEC
```
So adding the offset to the starting point of the time series leads to the time desired, while resampling starts one quarter before.
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} | 0 | "2013-01-28T21:30:54Z" | "2013-02-10T22:38:03Z" | "2013-02-10T22:38:03Z" | CONTRIBUTOR | null | ##### ___setitem___ fails with row slices supported by ___getitem___
``` python
In [27]: df=p.DataFrame(
....: {"alpha": ["a", "b", "c", "d", "e"],
....: "beta":[1,2,3,4,5]},
....: columns=["alpha","beta"],
....: index=[1.0, 2.5, 3, 4.5, 5.0])
In [28]: df[0:1] # does work, slices on rows
Out[28]:
alpha beta
1 a 1
In [29]: df[0:1] = 0 # fails
(exception)
```
##### ___setitem___ fails on boolean indexing unless key is _ndarray_, while ___getitem___ casts appropriately
``` python
In [49]: df=p.DataFrame(
....: {"alpha": ["a", "b", "c", "d", "e"],
....: "beta":[1,2,3,4,5]},
....: columns=["alpha","beta"],
....: index=[1.0, 2.5, 3, 4.5, 5.0])
In [50]: df[np.array([True, False, False, False, True])]
Out[50]:
alpha beta
1 a 1
5 e 5
In [51]: df[[True, False, False, False, True]]
Out[51]:
alpha beta
1 a 1
5 e 5
In [52]: df[np.array([True, False, False, False, True])] = 4 # does work
In [53]: df[[True, False, False, False, True]] = 3 # does not work
(exception)
```
##### ___getitem___ does not reindex boolean _Series_ key but ___setitem___ does
``` python
In [54]: df=p.DataFrame(
....: {"alpha": ["a", "b", "c", "d", "e"],
....: "beta":[1,2,3,4,5]},
....: columns=["alpha","beta"],
....: index=[1.0, 2.5, 3, 4.5, 5.0])
In [55]: s=p.Series([True, True, False, False, False],
....: index=[5.0, 4.5, 3, 2.5, 1.0]) # row-reversed index
In [56]: df[s] # gets first two rows (no reindex)
Out[56]:
alpha beta
1.0 a 1
2.5 b 2
In [57]: df[s] = 1 # assigns last two rows (reindexed)
In [58]: df
Out[58]:
alpha beta
1.0 a 1
2.5 b 2
3.0 c 3
4.5 1 1
5.0 1 1
```
##### ___getitem___ does not allow _DataFrame_ key when columns are _MultiIndex_, but ___setitem___ does
``` python
In [60]: c2=p.MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
....: ['one', 'two', 'three']],
....: labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
....: [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
....: names=['first', 'second'])
In [61]: df2=p.DataFrame(np.random.randn(3, 10),
....: index=['A','B','C'], columns=c2)
In [62]: df2[df2 > 0] # fails
(exception)
In [63]: df2[df2 > 0] = 4 # ok
```
##### ___setitem___ and ___getitem___ reindex _DataFrame_ keys when shapes differ, but do not reindex when shapes are the same and indexes/columns are different
``` python
In [68]: df3=p.DataFrame(np.random.randn(5, 2),
....: index=[1.0, 2.5, 3, 4.5, 5.0])
In [69]: key = (df3 > 0).reindex(df3.index[:-1]) # first four rows
In [70]: df3[key] = 5 # key is reindexed because shape does not match (ok)
In [71]: df3
Out[71]:
0 1
1.0 -1.599142 5.000000
2.5 5.000000 -0.180330
3.0 -0.568206 5.000000
4.5 -0.340465 -0.070105
5.0 -0.474955 1.928842
In [72]: key = key.reindex(key.index[::-1]) # reverse rows in key
In [73]: df3[key] = 4 # key is reindexed because shape does not match (ok)
In [74]: df3
Out[74]:
0 1
1.0 -1.599142 4.000000
2.5 4.000000 -0.180330
3.0 -0.568206 4.000000
4.5 -0.340465 -0.070105
5.0 -0.474955 1.928842
In [75]: df3=p.DataFrame(np.random.randn(5, 2),
....: index=[1.0, 2.5, 3, 4.5, 5.0])
In [76]: key = (df3 > 0) # all rows
In [77]: df3[key] = 5 # no reindex required
In [78]: df3
Out[78]:
0 1
1.0 5.000000 -0.233779
2.5 5.000000 -0.141962
3.0 -0.232551 5.000000
4.5 -1.663034 5.000000
5.0 -0.653200 -0.365681
In [79]: key = key.reindex(key.index[::-1]) # reverse rows in key
In [80]: df3[key] = 4 # key is not reindexed, even though rows are reversed
In [81]: df3
Out[81]:
0 1
1.0 5.000000 -0.233779
2.5 5.000000 4.000000
3.0 -0.232551 4.000000
4.5 4.000000 5.000000
5.0 4.000000 -0.365681
```
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Note, to address " ___getitem___ does not reindex boolean Series key but ___setitem___ does ", I have changed the ___getitem___ behavior rather than the ___setitem___ behavior because it is more consistent with other indexing behavior and does not break any tests...just in case, I have inserted a warning about this happening (which only triggers when a reindex is actually required)
Also, in addition to resolving the bugs, I have refactored the code to make ___getitem___ and ___setitem___ code paths as symmetric as possible, in order to help prevent more issues in the future.
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} | 4 | "2013-01-29T14:40:42Z" | "2013-04-08T07:47:26Z" | "2013-04-08T07:47:26Z" | CONTRIBUTOR | null | The calculation of the week/weekofyear attribute of a Timestamp/DatetimeIndex differs from both python and mx.DateTime which both return `week = 1` for 31-Dec-2013.
``` python
In [25]: import mx.DateTime
...: from datetime import datetime
...: d = datetime(2013,12,31)
...:
In [26]: d.isocalendar()
Out[26]: (2014, 1, 2)
In [27]: mx.DateTime.DateTimeFrom(d).iso_week
Out[27]: (2014, 1, 2)
In [28]: pd.Timestamp(d).week
Out[28]: 53
```
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} | 9 | "2013-01-29T16:58:07Z" | "2013-02-11T03:22:33Z" | "2013-02-11T03:22:33Z" | NONE | null | Cleanup code exists in the `tearDown` method, but a `KeyboardInterrupt`
either doesn't run the `tearDown`, or maybe the file handle isn't closed so
the `os.remove` fails.
One example which left behind a file in the root of the source tree.
``` python
λ nosetests pandas/io/tests/test_pytables.py
.S......SSSS..^CTraceback (most recent call last):
File "/usr/local/bin/nosetests", line 9, in <module>
load_entry_point('nose==1.2.1', 'console_scripts', 'nosetests-2.7')()
File "/usr/local/lib/python2.7/dist-packages/nose/core.py", line 118, in __init__
**extra_args)
File "/usr/lib/python2.7/unittest/main.py", line 95, in __init__
self.runTests()
File "/usr/local/lib/python2.7/dist-packages/nose/core.py", line 197, in runTests
result = self.testRunner.run(self.test)
File "/usr/local/lib/python2.7/dist-packages/nose/core.py", line 61, in run
test(result)
File "/usr/local/lib/python2.7/dist-packages/nose/suite.py", line 176, in __call__
return self.run(*arg, **kw)
File "/usr/local/lib/python2.7/dist-packages/nose/suite.py", line 223, in run
test(orig)
File "/usr/local/lib/python2.7/dist-packages/nose/suite.py", line 176, in __call__
return self.run(*arg, **kw)
File "/usr/local/lib/python2.7/dist-packages/nose/suite.py", line 223, in run
test(orig)
File "/usr/local/lib/python2.7/dist-packages/nose/suite.py", line 176, in __call__
return self.run(*arg, **kw)
File "/usr/local/lib/python2.7/dist-packages/nose/suite.py", line 223, in run
test(orig)
File "/usr/local/lib/python2.7/dist-packages/nose/case.py", line 45, in __call__
return self.run(*arg, **kwarg)
File "/usr/local/lib/python2.7/dist-packages/nose/case.py", line 133, in run
self.runTest(result)
File "/usr/local/lib/python2.7/dist-packages/nose/case.py", line 151, in runTest
test(result)
File "/usr/lib/python2.7/unittest/case.py", line 396, in __call__
return self.run(*args, **kwds)
File "/usr/lib/python2.7/unittest/case.py", line 332, in run
testMethod()
File "/home/user1/src/pandas/pandas/io/tests/test_pytables.py", line 1690, in test_coordinates
self.store.append('df1', df1, data_columns=['A', 'B'])
File "/home/user1/src/pandas/pandas/io/pytables.py", line 532, in append
self._write_to_group(key, value, table=True, append=True, **kwargs)
File "/home/user1/src/pandas/pandas/io/pytables.py", line 788, in _write_to_group
s.write(obj = value, append=append, complib=complib, **kwargs)
File "/home/user1/src/pandas/pandas/io/pytables.py", line 2502, in write
self.set_attrs()
File "/home/user1/src/pandas/pandas/io/pytables.py", line 1984, in set_attrs
self.attrs.table_type = self.table_type
File "/usr/local/lib/python2.7/dist-packages/tables/attributeset.py", line 444, in __setattr__
self._g__setattr(name, value)
File "/usr/local/lib/python2.7/dist-packages/tables/attributeset.py", line 386, in _g__setattr
self._g_setAttr(self._v_node, name, stvalue)
File "hdf5Extension.pyx", line 419, in tables.hdf5Extension.AttributeSet._g_setAttr (tables/hdf5Extension.c:3928)
File "/usr/local/lib/python2.7/dist-packages/tables/atom.py", line 368, in from_dtype
return class_.from_kind('string', itemsize, dtype.shape, dflt)
File "/usr/local/lib/python2.7/dist-packages/tables/atom.py", line 456, in from_kind
kwargs['itemsize'] = itemsize
KeyboardInterrupt
Closing remaining open files: __26Weihln5J__.h5... done
```
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} | 34 | "2013-01-29T18:36:11Z" | "2017-12-04T15:32:22Z" | "2017-12-04T15:32:21Z" | NONE | null | Using pandas 0.10. If we create a Dataframe with a multi-index, then delete all the rows with value X, we'd expect the index to no longer show value X. But it does.
Note the apparent inconsistency between "index" and "index.levels" -- one shows the values have been deleted but the other doesn't.
``` python
import pandas
x = pandas.DataFrame([['deleteMe',1, 9],['keepMe',2, 9],['keepMeToo',3, 9]], columns=['first','second', 'third'])
x = x.set_index(['first','second'], drop=False)
x = x[x['first'] != 'deleteMe'] #Chop off all the 'deleteMe' rows
print x.index #Good: Index no longer has any rows with 'deleteMe'. But....
print x.index.levels #Bad: index still shows the "deleteMe" values are there. But why? We deleted them.
x.groupby(level='first').sum() #Bad: it's creating a dummy row for the rows we deleted!
```
We don't want the deleted values to show up in that groupby. Can we eliminate them?
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https://travis-ci.org/jreback/pandas/jobs/4457513
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} | 8 | "2013-01-30T03:48:49Z" | "2013-02-15T00:15:55Z" | "2013-02-15T00:15:55Z" | NONE | null | I have a DataFrame with a DatetimeIndex and two float32 columns.
``` ipython
In [35]: %timeit a.groupby(level=0).last()
1 loops, best of 3: 2.11 s per loop
In [36]: a = a.astype(np.float64)
In[37]: %timeit a.groupby(level=0).last()
1000 loops, best of 3: 911 us per loop
```
Either way, the result of the groupby is all float64s. I would lilke to preserve float32 dtypes if possible.
Also, there are other operations (resample, shift) that are also very slow on float32 data but I'm pretty sure this is related.
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} | 39 | "2013-01-30T04:15:58Z" | "2017-12-10T16:04:25Z" | "2013-03-10T19:48:59Z" | NONE | null | HDFstore is working much better with the latest release, but I am encountering a new error I wanted to report:
df = get_joined_data(data_prefix, data_rev_prefix, date_prefix, store_filename)
File "XXXXXX", line 739, in get_joined_data
write_dataframe("joined_{0}".format(date_prefix), df, store)
File "XXXXXXX", line 55, in write_dataframe
store[name] = df
File "/Library/Python/2.7/site-packages/pandas/io/pytables.py", line 218, in **setitem**
self.put(key, value)
File "/Library/Python/2.7/site-packages/pandas/io/pytables.py", line 458, in put
self._write_to_group(key, value, table=table, append=append, *_kwargs)
File "/Library/Python/2.7/site-packages/pandas/io/pytables.py", line 788, in _write_to_group
s.write(obj = value, append=append, complib=complib, *_kwargs)
File "/Library/Python/2.7/site-packages/pandas/io/pytables.py", line 1837, in write
self.write_array('block%d_values' % i, blk.values)
File "/Library/Python/2.7/site-packages/pandas/io/pytables.py", line 1627, in write_array
vlarr.append(value)
File "/Library/Python/2.7/site-packages/tables-2.4.0-py2.7-macosx-10.8-intel.egg/tables/vlarray.py", line 480, in append
self._append(nparr, nobjects)
File "hdf5Extension.pyx", line 1499, in tables.hdf5Extension.VLArray._append (tables/hdf5Extension.c:13764)
OverflowError: value too large to convert to int
Not at all sure this is an actual pandas issue, but thought I would report it nonetheless.
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} | 1 | "2013-01-30T14:25:05Z" | "2013-02-10T22:37:40Z" | "2013-02-10T22:37:40Z" | NONE | null | The following code causes segmentation fault (tried with pandas 0.10.1):
pandas.DataFrame(columns=["x"]).x.fillna(method="pad", inplace=1)
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https://api.github.com/repos/pandas-dev/pandas/issues/2776 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2776/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2776/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2776/events | https://github.com/pandas-dev/pandas/pull/2776 | 10,467,836 | MDExOlB1bGxSZXF1ZXN0MzkxMDYzNA== | 2,776 | BUG: Various inconsistencies in DataFrame getitem/setitem behavior (fixes #2765) | {
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} | 2 | "2013-01-30T17:35:24Z" | "2014-06-25T19:20:39Z" | "2013-02-10T21:25:29Z" | CONTRIBUTOR | null | fixes #2765
Note, to address `__getitem__` does not reindex boolean Series key but `__setitem__` does ", I have changed the `__getitem__` behavior rather than the `__setitem__` behavior because it is more consistent with other indexing behavior and does not break any tests...just in case, I have inserted a warning about this happening (which only triggers when a reindex is actually required)
Also, in addition to resolving the bugs, I have refactored the code to make `__getitem__` and `__setitem__` code paths as symmetric as possible, in order to help prevent more issues in the future.
Unit test coverage added, and performance tests show no impact
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} | 2 | "2013-01-30T17:45:07Z" | "2014-06-26T00:45:12Z" | "2013-02-11T00:08:51Z" | CONTRIBUTOR | null | fixes #2727: `_NDFrameIndexer._convert_to_indexer` modified to match behavior in `_NDFrameIndexer._get_slice_axis`, new tests added to existing unit test
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} | 12 | "2013-01-30T18:10:19Z" | "2014-06-16T15:02:12Z" | "2013-02-10T16:43:27Z" | CONTRIBUTOR | null | fixes #2775
just added a check for zero-length data to the backfill and pad templates
not sure if I should add test coverage? the problem is that the tests will not fail without the fix, but rather segfault, so it might not be a good idea
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} | 3 | "2013-01-30T21:05:16Z" | "2014-06-25T01:21:48Z" | "2013-03-11T16:41:41Z" | NONE | null | I have data for which I know what the data type should be for each column, e.g. float. Occasionally these columns will also have spurious non-float string values due to erroneous data processing/pivoting up stream. I would like read_csv to help me ignore this (non-predictable) spurious data by parsing them as np.nan (missing values). This rules out using read_csv( na_values= ) parameter because I cant predict them before hand.
I wanted to use the read_csv supported converter functions to return an np.float if it can be parsed but it seems that this method is prohibitively slow between 4-10x slower in some examples I have tried.
I would love read_csv's quick parser to have a "force_dtype=True" option where no matter what string is in the column the specified dtype is always returned or np.nan is used to indicate an impossible parse and therefore a missing value is placed instead.
```
import numpy as np
import pandas as pd
import StringIO
data = 'a,b,c\n1.1,2.2,3.3\n2.2,garb,4.4\n3.3,4.4,5.5'
data = data+data[5:]*3000
def converter(num):
try:
return np.float(num)
except:
return np.nan
%time df = pd.read_csv(StringIO.StringIO((data+data[5:]*30)))
df.dtypes
%time df2 = pd.read_csv(StringIO.StringIO((data+data[5:]*30)), converters={'b':converter})
df2.dtypes
%time df3 = pd.read_csv(StringIO.StringIO((data+data[5:]*30)), dtype=np.float)
df3.dtypes
In [7]: %time df = pd.read_csv(StringIO.StringIO((data+data[5:]*30)))
CPU times: user 0.12 s, sys: 0.00 s, total: 0.12 s
Wall time: 0.12 s <-- Quick native CSV parser
In [8]: df.dtypes <-- Returns column 'b' as float and string without the ability to convert to a single type.
Out[8]: a float64
b object
c float64
In [9]: %time df2 = pd.read_csv(StringIO.StringIO((data+data[5:]*30)), converters={'b':converter})
CPU times: user 0.42 s, sys: 0.00 s, total: 0.42 s
Wall time: 0.42 s <-- Using converter function slows the parsing down by 4X (this is only for a one-column converter)
In [10]: df2.dtypes <-- This time only np.float types are returned, and np.nan is used in place for any spurious values in column 'b'
Out[10]: a float64
b float64
c float64
In [11]: %time df3 = pd.read_csv(StringIO.StringIO((data+data[5:]*30)), dtype=np.float)
AttributeError: 'NoneType' object has no attribute 'dtype' <-- Trying to force np.float on the columns results in un-parseable exception for column 'b' :(
```
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} | 1 | "2013-01-30T21:12:44Z" | "2014-06-24T08:47:20Z" | "2013-02-10T22:33:39Z" | CONTRIBUTOR | null | closes #2681
Added support for `df.ix[start_time:end_time]`, where `df.index` is a `DateTimeIndex`, both on the `__getitem__` and `__setitem__` side; test coverage in test_frame.py
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} | 5 | "2013-01-31T11:06:47Z" | "2013-03-28T05:40:23Z" | "2013-03-28T05:40:23Z" | CONTRIBUTOR | null | Unless I'm mistaken I think the following unit-test should pass:
``` python
def test_period_sorting():
periods = [pd.Period('2008-04', 'M'),
pd.Period('2008-02', 'M'),
pd.Period('2008-01', 'M'),
pd.Period('2008-05', 'M'),
pd.Period('2008-06', 'M'),
pd.Period('2008-03', 'M'),
pd.Period('2008-04', 'M'),
pd.Period('2008-02', 'M'),
pd.Period('2008-01', 'M'),
pd.Period('2008-05', 'M'),
pd.Period('2008-06', 'M'),
pd.Period('2008-03', 'M'),
pd.Period('2008-04', 'M'),
pd.Period('2008-02', 'M'),
pd.Period('2008-01', 'M'),
pd.Period('2008-05', 'M'),
pd.Period('2008-06', 'M'),
pd.Period('2008-03', 'M')]
assert sorted([p.to_timestamp() for p in periods]) \
== [p.to_timestamp() for p in sorted(periods)]
#
```
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} | 2 | "2013-01-31T13:27:26Z" | "2013-02-10T02:11:03Z" | "2013-02-10T02:11:03Z" | NONE | null | Slicing a TimeSeries object with a PeriodIndex fails with an index out of bounds error
Example
dates = pd.period_range(start='01-Apr-11',end='31-Mar-12 23:00',freq='h')
ts = pd.TimeSeries(0,dates)
ts[ts.index[4:10]]
Traceback (most recent call last):
File "<ipython-input-2-c28eee792d28>", line 3, in <module>
ts[ts.index[4:10]]
File "C:\dev\bin\Python27\lib\site-packages\pandas\core\series.py", line 506, in **getitem**
return self._get_with(key)
File "C:\dev\bin\Python27\lib\site-packages\pandas\core\series.py", line 544, in _get_with
return self._get_values(key)
File "C:\dev\bin\Python27\lib\site-packages\pandas\core\series.py", line 575, in _get_values
return self.values[indexer]
IndexError: index 361564 out of bounds 0<=index<8784
It appears the PeriodIndex is being inferred as 'integer' in series._get_with() using lib.infer_dtype() whereas self.index.inferred_type returns 'period'
```
if isinstance(key, Index):
key_type = lib.infer_dtype(key.values)
else:
key_type = lib.infer_dtype(key)
if key_type == 'integer':
if self.index.inferred_type == 'integer':
return self.reindex(key)
else:
return self._get_values(key)
elif key_type == 'boolean':
return self._get_values(key)
```
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``` python
import numpy as np
from pandas import DataFrame
x = DataFrame(randn(3, 2))
xm = x.mean(axis=0) # broadcast along the rows
xc1 = x - xm
xc2 = x.sub(xm, axis=1) # broadcast along rows with axis == 1??
assert np.array_equal(xc1, xc2)
```
This seems a bit unintuitive.
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} | 26 | "2013-01-31T17:00:58Z" | "2013-02-18T23:07:47Z" | "2013-02-18T23:07:47Z" | NONE | null | After a long run of extracting features for some random forest action I ran into this when serializing the features:
Traceback (most recent call last):
File "XXXXX.py", line 1043, in <module>
write_dataframe("features", all_df, store)
File "XXXXX.py", line 55, in write_dataframe
store[name] = df
File "/Library/Python/2.7/site-packages/pandas/io/pytables.py", line 218, in **setitem**
self.put(key, value)
File "/Library/Python/2.7/site-packages/pandas/io/pytables.py", line 458, in put
self._write_to_group(key, value, table=table, append=append, *_kwargs)
File "/Library/Python/2.7/site-packages/pandas/io/pytables.py", line 788, in _write_to_group
s.write(obj = value, append=append, complib=complib, *_kwargs)
File "/Library/Python/2.7/site-packages/pandas/io/pytables.py", line 1837, in write
self.write_array('block%d_values' % i, blk.values)
File "/Library/Python/2.7/site-packages/pandas/io/pytables.py", line 1639, in write_array
self.handle.createArray(self.group, key, value)
File "/Library/Python/2.7/site-packages/tables-2.4.0-py2.7-macosx-10.8-intel.egg/tables/file.py", line 780, in createArray
object=object, title=title, byteorder=byteorder)
File "/Library/Python/2.7/site-packages/tables-2.4.0-py2.7-macosx-10.8-intel.egg/tables/array.py", line 167, in __init__
byteorder, _log)
File "/Library/Python/2.7/site-packages/tables-2.4.0-py2.7-macosx-10.8-intel.egg/tables/leaf.py", line 263, in __init__
super(Leaf, self).**init**(parentNode, name, _log)
File "/Library/Python/2.7/site-packages/tables-2.4.0-py2.7-macosx-10.8-intel.egg/tables/node.py", line 250, in __init__
self._v_objectID = self._g_create()
File "/Library/Python/2.7/site-packages/tables-2.4.0-py2.7-macosx-10.8-intel.egg/tables/array.py", line 200, in _g_create
nparr, self._v_new_title, self.atom)
File "hdf5Extension.pyx", line 884, in tables.hdf5Extension.Array._createArray (tables/hdf5Extension.c:8498)
tables.exceptions.HDF5ExtError: Problems creating the Array.
The error is pretty undefined, I know the table I was writing was big, >17000 columns by > 20000 rows. There are lots of np.nan's in the columns.
Since I seem to be one of the few who are serializing massive sets, and I have a 64GB RAM machine sitting next to me, are there some test cases that I can run, or write that would help? Thinking setting up large mixed dataframes etc...
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} | 3 | "2013-01-31T22:23:13Z" | "2014-06-18T19:02:10Z" | "2013-02-09T23:04:59Z" | CONTRIBUTOR | null | fixes #2782
simple fix...can't see any reason why the original code was doing what it was, especially since indices are immutable
added test coverage in test_series.py
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} | 4 | "2013-01-31T22:36:00Z" | "2013-05-03T17:21:26Z" | "2013-04-01T16:15:49Z" | CONTRIBUTOR | null | Using pandas master/github. In this example, we get 4 times the same column after applying an element-wise operation:
```
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: df = pd.DataFrame(np.random.random((3,4)))
In [4]: cols = pd.Index(['a','a','a','a'])
In [5]: df.columns = cols
In [6]: df.applymap(str)
Out[6]:
a a a a
0 0.320051885413 0.320051885413 0.320051885413 0.320051885413
1 0.967238549103 0.967238549103 0.967238549103 0.967238549103
2 0.913201809648 0.913201809648 0.913201809648 0.913201809648
```
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} | 0 | "2013-02-01T00:45:30Z" | "2013-03-22T14:39:26Z" | "2013-03-22T14:39:26Z" | NONE | null | Attempting to retrieve the resolution attribute throws this exception:
``` python
34 @classmethod
35 def get_str(cls, reso):
36 return {RESO_US : 'microsecond',
37 RESO_SEC : 'second',
38 RESO_MIN: 'minute',
NameError: global name 'RESO_US' is not defined
```
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} | 0 | "2013-02-01T02:45:18Z" | "2013-02-10T02:11:09Z" | "2013-02-10T02:11:09Z" | CONTRIBUTOR | null | `__getitem__` with date string key interprets a string label in the index's timezone when none provided, but `__getitem__` with date string slices and `__setitem__` with string label/slices do not
``` python
In [1]: import pandas as p
In [2]: import numpy as np
In [3]: index=p.date_range("2012-01-01", "2012-01-02",
...: freq='60min', tz='US/Eastern')
In [4]: ts=p.Series(np.random.randn(len(index)), index)
In [5]: ts["2012-01-01 15:00:00":"2012-01-02 01:00:00"] \
...: # off by five hours
Out[5]:
2012-01-01 10:00:00-05:00 -1.294343
2012-01-01 11:00:00-05:00 -0.091218
2012-01-01 12:00:00-05:00 0.049017
2012-01-01 13:00:00-05:00 -1.068893
2012-01-01 14:00:00-05:00 -0.470317
2012-01-01 15:00:00-05:00 0.894762
2012-01-01 16:00:00-05:00 0.223070
2012-01-01 17:00:00-05:00 -0.414907
2012-01-01 18:00:00-05:00 -0.925703
2012-01-01 19:00:00-05:00 0.879205
2012-01-01 20:00:00-05:00 -1.580123
Freq: 60T
In [6]: ts["2012-01-01 20:00:00"] # ok
Out[6]: -1.5801231823927993
In [7]: ts["2012-01-01 15:00:00"] # ok
Out[7]: 0.89476211630990787
In [8]: ts["2012-01-01 20:00:00"] = 1 # off by five hours
In [9]: ts["2012-01-01 20:00:00"]
Out[9]: -1.5801231823927993
In [10]: ts["2012-01-01 15:00:00"]
Out[10]: 1.0
In [11]: ts["2012-01-01 20:00:00-05:00"] = 1 # ok
In [12]: ts["2012-01-01 20:00:00"]
Out[12]: 1.0
```
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} | 1 | "2013-02-01T07:16:32Z" | "2013-02-10T02:11:16Z" | "2013-02-10T02:11:16Z" | CONTRIBUTOR | null | `Timestamp` constructor raises an exception when given iso8601 string with UTC offset and a desired timezone. When given `datetime` object with timezone and a desired timezone, it gives a correct string but incorrect internal timestamp value.
``` python
In [1]: from pandas import Timestamp as ts, DataFrame as df
In [2]: from datetime import datetime as dt
In [3]: from pytz import timezone as tz
In [4]: UTC='UTC'
In [5]: EST='US/Eastern'
In [6]: CST='US/Central'
In [7]: # all the following should have the same timestamp value
In [8]: # (commented lines throw exceptions!!)
In [9]: tss=[ts("2012-01-02 01:00:00+00:00"),
...: # ts("2012-01-02 01:00:00+00:00", tz=EST),
...: ts("2012-01-01 20:00:00", tz=EST),
...: ts(dt(2012,1,1,20,0,0,tzinfo=tz(EST))),
...: ts(dt(2012,1,1,19,0,0,tzinfo=tz(CST))),
...: ts(dt(2012,1,1,20,0,0,tzinfo=tz(EST)), tz=EST),
...: ts(dt(2012,1,1,19,0,0,tzinfo=tz(CST)), tz=EST),
...: ts("2012-01-01 20:00:00-05:00"),
...: ts("2012-01-01 19:00:00-06:00"),
...: # ts("2012-01-01 20:00:00-05:00", tz=EST),
...: # ts("2012-01-01 19:00:00-06:00", tz=EST),
...: ts(1325466000000000000, tz=UTC),
...: ts(1325466000000000000, tz=EST)]
In [10]: strs = [str(ts) for ts in tss]
In [11]: vals = [(ts.value/1000000000000) for ts in tss]
In [12]: df({'strs' : strs, 'vals' : vals})
Out[12]:
strs vals
0 2012-01-02 01:00:00+00:00 1325466
1 2012-01-01 20:00:00-05:00 1325466
2 2012-01-01 20:00:00-05:00 1325466
3 2012-01-01 19:00:00-06:00 1325466
4 2012-01-01 20:00:00-05:00 1325448 <-- bad value!!
5 2012-01-01 20:00:00-05:00 1325448 <-- bad value!!
6 2012-01-01 20:00:00-05:00 1325466
7 2012-01-01 19:00:00-06:00 1325466
8 2012-01-02 01:00:00+00:00 1325466
9 2012-01-01 20:00:00-05:00 1325466
```
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} | 1 | "2013-02-02T12:26:14Z" | "2016-08-16T00:46:47Z" | "2016-08-16T00:46:47Z" | CONTRIBUTOR | null | example of impl. I think this is pretty straightforward; what is hard is align (well not that hard but needs to accept a lot of different cases)
http://stackoverflow.com/questions/14650341/boolean-mask-in-pandas-panel
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} | 1 | "2013-02-02T17:59:48Z" | "2016-04-09T23:12:07Z" | "2016-04-09T23:12:07Z" | NONE | null | Refer to https://github.com/pydata/pandas/issues/1889#issuecomment-11601189
And below is the completed code to do that. Can put it into the codebase and make it as the default option to display dataframe ?
def _sw(df, up_rows=10, down_rows=5, left_cols=4, right_cols=3):
''' display df data at four corners
A,B (up_pt)
C,D (down_pt)
parameters :
up_rows=10, down_rows=5, left_cols=4, right_cols=3
sample:
df = pd.DataFrame(np.random.randn(20,10), columns=list('ABCDEFGHIJKLMN')[0:10])
df.sw(5,2,3,2)
df1 = df.set_index(['A','B'], drop=True, inplace=False)
df1.sw(5,2,3,2)
'''
```
ncol, nrow = len(df.columns), len(df)
# handling columns
if ncol <= (left_cols + right_cols) :
up_pt = df.ix[0:up_rows, :] # screen width can contain all columns
down_pt = df.ix[-down_rows:, :]
else: # screen width can not contain all columns
pt_a = df.ix[0:up_rows, 0:left_cols]
pt_b = df.ix[0:up_rows, -right_cols:]
pt_c = df[-down_rows:].ix[:,0:left_cols]
pt_d = df[-down_rows:].ix[:,-right_cols:]
up_pt = pt_a.join(pt_b, how='inner')
down_pt = pt_c.join(pt_d, how='inner')
up_pt.insert(left_cols, '..', '..')
down_pt.insert(left_cols, '..', '..')
overlap_qty = len(up_pt) + len(down_pt) - len(df)
down_pt = down_pt.drop(down_pt.index[range(overlap_qty)]) # remove overlap rows
dt_str_list = down_pt.to_string().split('\n') # transfer down_pt to string list
# Display up part data
print up_pt
start_row = (1 if df.index.names[0] is None else 2) # start from 1 if without index
# Display omit line if screen height is not enought to display all rows
if overlap_qty < 0:
print "." * len(dt_str_list[start_row])
# Display down part data row by row
for line in dt_str_list[start_row:]:
print line
# Display foot note
print "\n"
print "Index :",df.index.names
print "Column:",",".join(list(df.columns.values))
print "row: %d col: %d"%(len(df), len(df.columns))
print "\n"
return None
```
DataFrame.sw = _sw #add a method to DataFrame class
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I've added some missing indentation, which fixes the issue for me.
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https://api.github.com/repos/pandas-dev/pandas/issues/2793 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2793/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2793/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2793/events | https://github.com/pandas-dev/pandas/issues/2793 | 10,581,522 | MDU6SXNzdWUxMDU4MTUyMg== | 2,793 | BUG: DataFrame inplace where doesn't work for mixed datatype frames | {
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``` python
In [1]: import pandas as p
In [2]: import numpy as np
In [3]: df=p.DataFrame({'a': [1.0, 2.0, 3.0, 4.0],
...: 'b': [4.0, 3.0, 2.0, 1.0]}) # single dtype
In [4]: df.where(df > 2, np.nan)
Out[4]:
a b
0 NaN 4
1 NaN 3
2 3 NaN
3 4 NaN
In [5]: df.where(df > 2, np.nan, inplace=True)
In [6]: df
Out[6]:
a b
0 NaN 4
1 NaN 3
2 3 NaN
3 4 NaN
In [7]: df=p.DataFrame({'a': [1, 2, 3, 4],
...: 'b': [4.0, 3.0, 2.0, 1.0]}) # mixed dtypes
In [8]: df.where(df > 2, np.nan) # ok
Out[8]:
a b
0 NaN 4
1 NaN 3
2 3 NaN
3 4 NaN
In [9]: df.where(df > 2, np.nan, inplace=True) # not ok
In [10]: df
Out[10]:
a b
0 1 4
1 2 3
2 3 2
3 4 1
```
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} | 22 | "2013-02-03T23:49:59Z" | "2013-02-15T00:14:47Z" | "2013-02-15T00:14:47Z" | CONTRIBUTOR | null | Should pandas preserve the input dtype on when doing
boolean indexing, if possible?
Its a pretty limited case, in that you have to have all values in a column non-null
and they all have to be integers (just casted as floats)
this is actually a little tricky to implement as this has to be done column by column (and its possibly that blocks have to be split)
the question here is will this be not what the user expects? (currently all dtypes
on boolean output operations are cast to float64/object)
```
In [24]: df = pd.DataFrame(dict(
a = pd.Series([1]*3,dtype='int32'),
b = pd.Series([1]*3,dtype='float32')),
index=range(3))
In [25]: df.ix[2,1] = 0
In [26]: df
Out[26]:
a b
0 1 1
1 1 1
2 1 0
In [27]: df.dtypes
Out[27]:
a int32
b float32
Dtype: object
In [28]: df[df>0]
Out[28]:
a b
0 1 1
1 1 1
2 1 NaN
##### if it is possible to preserve column a as the original int input dtype
which in this case it is #####
In [29]: df[df>0].dtypes
Out[29]:
a float64
b float32
Dtype: object
### possible output ###
Out[29]:
a int32
b float32
Dtype: object
```
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} | 10 | "2013-02-04T10:25:59Z" | "2014-06-16T03:40:19Z" | "2013-02-10T21:15:18Z" | CONTRIBUTOR | null | Added functionality of listing stock index components via query to yahoo finance
API. Expanded existing functionality of other yahoo finance features in
`pandas.io.data.py`, in detail:
- Created new function `get_component_yahoo()`, allowing for query of stock index
components. Returns DataFrame containing list of component information for
index represented by `idx_sym` from yahoo. Result includes component symbol
(ticker), exchange, and full name.
- Refactored `get_data_yahoo`, now allows for multiple stock symbols with input
of list-like object (tuple, list, Series), or DataFrame with stock symbols as index.
Includes convenience options for adjusting price and adding return index.
- Refactored `get_quote_yahoo`. Can now accept a single symbol (str) along with
list-like object (list, tuple, Series).
- Expanded test coverage to the new features.
_EDIT:_ A few typos.
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https://api.github.com/repos/pandas-dev/pandas/issues/2796 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2796/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2796/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2796/events | https://github.com/pandas-dev/pandas/issues/2796 | 10,606,435 | MDU6SXNzdWUxMDYwNjQzNQ== | 2,796 | ImportError: cannot import name Series | {
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I have python installs on both my mac and WIndows machine. cant be sure it i have used pandas from command line python in both as most of my stuff has been running in ipython so far. when i do import pandas or any other option (from pandas import *, form pandas import DataFrame etc. I get the following error message:
> > > import pandas
> > > Traceback (most recent call last):
> > > File "<stdin>", line 1, in <module>
> > > File "pandas.py", line 4, in <module>
> > > from pandas import Series
> > > ImportError: cannot import name Series
same message on mac and Windows 7 machines.. pandas imports fine in idle, ipython ipython notebook. Using python 2.7.3 and pandas 10.0.1. Checked which
$ which python
/Library/Frameworks/Python.framework/Versions/Current/bin/python
$ which ipython
/Library/Frameworks/Python.framework/Versions/Current/bin/ipython
so both pointing at same place
did pip uninstall end re-install of pandas... problem seems constant.
Any suggestions?
40 minutes later another uninstall and install from pip and ipython can now also not import pandas... same error
resolved...was testing plotting ... calling your own script the same name as one you are importing is not a good idea... obviously... my bad
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} | 0 | "2013-02-04T19:06:25Z" | "2013-03-28T05:51:14Z" | "2013-03-28T05:51:14Z" | CONTRIBUTOR | null | ``` python
In [24]: dates = pd.date_range('01-Jan-2013', '01-Jan-2014', freq='MS')[0:-1]
...: s1 = pd.Series(randn(len(dates)), index=dates, name='value')
...: s2 = pd.Series(randn(len(dates)), index=dates, name='value')
In [31]: df = pd.concat([s1, s2], axis=1, ignore_index=True)
Traceback (most recent call last):
File "<ipython-input-31-66b3acfe3258>", line 1, in <module>
df = pd.concat([s1, s2], axis=1, ignore_index=True)
File "c:\dev\code\pandas\pandas\tools\merge.py", line 895, in concat
return op.get_result()
File "c:\dev\code\pandas\pandas\tools\merge.py", line 972, in get_result
data = dict(zip(self.new_axes[1], self.objs))
TypeError: zip argument #1 must support iteration
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2798 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2798/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2798/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2798/events | https://github.com/pandas-dev/pandas/issues/2798 | 10,664,387 | MDU6SXNzdWUxMDY2NDM4Nw== | 2,798 | BUG: Pandas treats NaN and None differently when multiplying booleans | {
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} | 1 | "2013-02-05T19:47:00Z" | "2013-03-28T12:15:05Z" | "2013-03-28T12:15:05Z" | NONE | null | Using pandas 0.10.
Pandas throws error when multiplying a series with booleans and a series with None. However, it works fine when you reverse the order. Also, it works fine if you use NaN instead of None. But isn't pandas supposed to treat NaN and None interchangably?
import pandas
import numpy as np
nans = pandas.Series([np.NaN, np.NaN])
nones = pandas.Series([None, None])
bools= pandas.Series([True,False])
x = nans \* bools #works fine
x = bools \* nans #works fine
x = nones \* bools #works fine
x = bools \* nones #ERROR! THIS BLOWS UP
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https://api.github.com/repos/pandas-dev/pandas/issues/2799 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2799/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2799/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2799/events | https://github.com/pandas-dev/pandas/pull/2799 | 10,666,053 | MDExOlB1bGxSZXF1ZXN0NDAwMTM0OQ== | 2,799 | Change wording in `corr` doc string | {
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} | 1 | "2013-02-05T20:28:41Z" | "2013-02-08T11:20:30Z" | "2013-02-08T11:20:30Z" | CONTRIBUTOR | null | Not sure if this is the best option, another one might be:
"Compute correlation between two Series"
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https://api.github.com/repos/pandas-dev/pandas/issues/2800 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2800/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2800/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2800/events | https://github.com/pandas-dev/pandas/issues/2800 | 10,668,017 | MDU6SXNzdWUxMDY2ODAxNw== | 2,800 | Series with dtype='M8[us]' does not create NaT values | {
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} | 3 | "2013-02-05T21:17:38Z" | "2013-09-13T00:05:49Z" | "2013-09-13T00:05:49Z" | NONE | null | Using Pandas 0.10.1, python 2.7, and Numpy 1.6.2:
```
>>> npa = numpy.ndarray(1, dtype=numpy.int64)
>>> npa[0] = -9223372036854775808
>>> type(pandas.Series(npa, dtype='M8[ns]')[0])
<class 'pandas.tslib.NaTType'>
>>> type(pandas.Series(npa, dtype='M8[us]')[0])
<class 'pandas.tslib.Timestamp'>
```
The latter should also have class 'pandas.tslib.NaTType', right?
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