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} | 3 | 2013-08-28T19:23:36Z | 2013-09-07T00:02:10Z | 2013-09-07T00:02:10Z | NONE | null | After selection DataFrame leave in the new DataFrame the labels set from original one, include those that not selected (see 185 & 186 vs 187 & 188 - the value 3 shouldn't be in the levels sets):
In [179]: tdf = pd.DataFrame([[1,1,1],[2,2,2],[3,3,3]])
In [180]: tdf.set_index([0,1], inplace=True)
In [181]: tdf
Out[181]:
2
0 1
1 1 1
2 2 2
3 3 3
In [182]: tdf12 = tdf.loc[1:2]
In [183]: tdf12
Out[183]:
2
0 1
1 1 1
2 2 2
In [184]: tdf12.index
Out[184]:
MultiIndex
[(1, 1), (2, 2)]
In [185]: tdf12.index.levels[0]
Out[185]: Int64Index([1, 2, 3], dtype=int64)
In [186]: tdf12.index.levels[1]
Out[186]: Int64Index([1, 2, 3], dtype=int64)
In [187]: tdf12.index.get_level_values(0)
Out[187]: Int64Index([1, 2], dtype=int64)
In [188]: tdf12.index.get_level_values(1)
Out[188]: Int64Index([1, 2], dtype=int64)
In [189]: tdf12.index.labels
Out[189]: [array([0, 1]), array([0, 1])]
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} | 25 | 2013-08-28T20:49:09Z | 2013-10-24T13:39:19Z | 2013-10-24T13:39:19Z | CONTRIBUTOR | null | Seems to lose a row when passing a list as header, check it out:
```
>>> from StringIO import StringIO
>>> import pandas
>>> text = """
a a a b c c
q r s t u v
1 2 3 4 5 6
1 2 3 4 5 6
"""
>>> # expected behavior without header
>>> pandas.read_csv(StringIO(text.strip()), sep='\s+')
a a.1 a.2 b c c.1
0 q r s t u v
1 1 2 3 4 5 6
2 1 2 3 4 5 6
>>> #drops row with specified header
>>> pandas.read_csv(StringIO(text.strip()), sep='\s+', header=[0, 1])
(a, q) (a, r) (a, s) (b, t) (c, u) (c, v)
0 1 2 3 4 5 6
>>> pandas.read_csv(StringIO(text.strip()), sep='\s+', header=[0, 1], tupleize_cols=False)
a b c
q r s t u v
0 1 2 3 4 5 6
```
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} | 22 | 2013-08-28T22:02:05Z | 2016-10-12T23:04:09Z | 2013-10-21T00:41:26Z | NONE | null | Noticed that if export a df to json file and then reload the data, ordering is not kept.
``` python
import pandas as pd
df.to_json('test.json')
newdf = pd.read_json('test.json')
print df.index
```
gives:
``` python
1
10
100
....
```
instead of
``` python
1
2
3
....
```
it suffices to call `newdf = pd.read_json('test.json').sort()` to have the what I'd expect.
Is this intended behaviour or bug?
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import pandas as pd, numpy
df = pd.DataFrame(numpy.empty((10,10)))
df.to_hdf('testme.h5','data',fmt='t')
store = pd.HDFStore('testme.h5')
In [7]: store
Out[7]:
<class 'pandas.io.pytables.HDFStore'>
File path: testme.h5
/data frame (shape->[10,10])
store.put('data1',df,fmt='t')
In [10]: store
Out[10]:
<class 'pandas.io.pytables.HDFStore'>
File path: testme.h5
/data frame (shape->[10,10])
/data1 frame (shape->[10,10])
store.append('data2',df)
In [12]: store
Out[12]:
<class 'pandas.io.pytables.HDFStore'>
File path: testme.h5
/data frame (shape->[10,10])
/data1 frame (shape->[10,10])
/data2 frame_table (typ->appendable,nrows->10,ncols->10,indexers->[index])
In [13]: pd.__version__
Out[13]: '0.12.0'
```
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} | 4 | 2013-08-29T06:49:08Z | 2013-09-07T21:28:09Z | 2013-09-07T21:28:09Z | NONE | null | The below code-sample gives a big performance drop on plotting in pandas 0.12, compared to pandas 0.11:
In Pandas 0.12 : "Ran in 0:00:29.542475 secs"
In Pandas 0.11 : "Ran in 0:00:06.653506 secs"
It only happens on a date-indexed DataFrame:
from pandas import *
from numpy.random import randn
N = 10000
M = 25
df = DataFrame(randn(N,M), index=date_range('1/1/1975', periods=N))
t0 = datetime.now()
df.plot()
print("Ran in %s secs" % (datetime.now() - t0))
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} | 37 | 2013-08-29T12:40:13Z | 2014-06-20T11:17:26Z | 2013-10-02T20:59:51Z | CONTRIBUTOR | null | Fix to issue #4230 which allows to localize an index which is
implicitly in a tz (e.g., reading from a file) by passing imply_dst to
tz_localize.
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} | 6 | 2013-08-30T05:10:35Z | 2014-02-17T02:35:55Z | 2014-02-17T02:35:55Z | MEMBER | null | cc @Komnomnomnom
I noticed a discrepancy between `JSON.stringify` in Chrome and both the built-in `json` package (python 2.7 at least) and `ultrajson`. In short the value `4.0`, say, encodes as `"4"` in Chrome and `"4.0"` in the others. In JavaScript-land, this has no practical impact, but I thought I'd bring it up in case others felt we should tweak ultrajson to conform.
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} | 0 | 2013-08-30T14:56:41Z | 2013-10-15T12:42:35Z | 2013-10-15T12:42:35Z | CONTRIBUTOR | null | This happens with and empty series with numpy arrays for both the values and the index.
``` python
>>> import pandas as pd
>>> import numpy as np
>>>
>>> with pd.get_store('foo.h5') as store:
... s = pd.Series(np.array([], dtype=np.int64), index=np.array([], dtype=np.int64))
... store['s'] = s
... s = store['s']
...
Traceback (most recent call last):
File "<stdin>", line 4, in <module>
File "/users/is/pross/workspace/pandas/git/pandas/pandas/io/pytables.py", line 349, in __getitem__
return self.get(key)
File "/users/is/pross/workspace/pandas/git/pandas/pandas/io/pytables.py", line 507, in get
return self._read_group(group)
File "/users/is/pross/workspace/pandas/git/pandas/pandas/io/pytables.py", line 1093, in _read_group
return s.read(**kwargs)
File "/users/is/pross/workspace/pandas/git/pandas/pandas/io/pytables.py", line 2247, in read
return Series(values, index=index, name=self.name)
File "/users/is/pross/workspace/pandas/git/pandas/pandas/core/series.py", line 657, in __init__
data = SingleBlockManager(data, index, fastpath=True)
File "/users/is/pross/workspace/pandas/git/pandas/pandas/core/internals.py", line 2942, in __init__
block = make_block(block, axis, axis, ndim=1, fastpath=True)
File "/users/is/pross/workspace/pandas/git/pandas/pandas/core/internals.py", line 1535, in make_block
return klass(values, items, ref_items, ndim=ndim, fastpath=fastpath, placement=placement)
File "/users/is/pross/workspace/pandas/git/pandas/pandas/core/internals.py", line 62, in __init__
% (len(items), len(values)))
ValueError: Wrong number of items passed 1, indices imply 0
```
I have fixed this locally and will put in a PR.
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} | 9 | 2013-08-30T15:12:15Z | 2014-06-23T14:43:41Z | 2013-10-15T12:42:36Z | CONTRIBUTOR | null | closes #4708 by spotting when the sentinel single value has been written. This was not being done for the index which resulted in a values/index length miss-match.
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} | 6 | 2013-08-30T15:52:54Z | 2013-08-31T20:57:53Z | 2013-08-31T20:00:14Z | NONE | null | ```
import pandas as pd, numpy
r = numpy.empty((3,4))
index = pd.MultiIndex.from_tuples([('A','a'), ('A','b'), ('B','a'), ('B','b')])
df = pd.DataFrame(r, columns=index)
store = pd.HDFStore('df.h5')
store.put('data',df) #->OK
store.put('data1',df,table=True) #-> Ok
store.put('data2',df,table=True,data_columns=['A']) #-> Ok
store.put('data3',df,table=True,data_columns=True) #-> raises hell
#TypeError: not all arguments converted during string formatting
store['data']['A'] #->OK
store['data1']['A'] #-> KeyError KeyError: u'no item named A'
In [43]: df = store['data']
In [44]: df
Out[44]:
A B
a b a b
0 2 -1.727234e-77 2.964394e-323 0.000000e+00
1 0 0.000000e+00 0.000000e+00 0.000000e+00
2 0 0.000000e+00 0.000000e+00 8.344027e-309
In [45]: df = store['data1']
In [46]: df
Out[46]:
(A, a) (A, b) (B, a) (B, b)
0 2 -1.727234e-77 2.964394e-323 0.000000e+00
1 0 0.000000e+00 0.000000e+00 0.000000e+00
2 0 0.000000e+00 0.000000e+00 8.344027e-309
pd.__version__ -> '0.12.0'
```
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} | [] | closed | false | null | [] | null | 2 | 2013-08-30T17:51:02Z | 2013-08-30T19:12:54Z | 2013-08-30T19:12:54Z | NONE | null | I have followed earlier posts for installing pandas. I have been having trouble installing it. Below are some details.
Mac Lion 10.7.5
Installed Cython (using pip install, which utilized llvm-gcc-4.2, for example)
Trying to install pandas using pip install from distribution or from git fails giving error:
######
pandas/index.c: In function ‘__pyx_f_8datetime__dts_to_pydatetime’:
pandas/index.c:10644: warning: implicit conversion shortens 64-bit value into a 32-bit value
lipo: can't open input file: /var/tmp//ccJ7Amtv.out (No such file or directory)
error: command 'llvm-gcc-4.2' failed with exit status 1
---
Cleaning up...
######
Downloading source and installing by running python setup.py install gives error
######
File "setup.py", line 91, in build_extensions
numpy_incl = pkg_resources.resource_filename('numpy', 'core/include')
######
Any help ?
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} | 2 | 2013-08-30T22:01:48Z | 2013-09-01T21:03:40Z | 2013-09-01T21:03:40Z | CONTRIBUTOR | null | closes #4563
Why does read_excel take a `kind` parameter at all? It gets passed to the `ExcelFile` constructor, set on the object and then (as far as I can tell) never gets used for anything.
This should either be removed from the signature (which doesn't affect anything, because it will be accepted into kwds anyways) or added into the docs and then actually used for something.
if we remove it, just need to add a:
``` python
# Remove this branch in 0.14
if 'kind' in kwargs:
warnings.warn("Kind is deprecated and will be removed in a future version of pandas")
kwargs.pop('kind')
```
Was not previously being used for anything anyways.
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} | 4 | 2013-08-30T22:19:44Z | 2014-06-13T16:05:16Z | 2013-09-01T21:03:40Z | CONTRIBUTOR | null | Pops kwarg and warns if you pass it (rather than passing on).
Fixes #4712
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https://api.github.com/repos/pandas-dev/pandas/issues/4714 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4714/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4714/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4714/events | https://github.com/pandas-dev/pandas/pull/4714 | 18,823,104 | MDExOlB1bGxSZXF1ZXN0ODAwMzQyNw== | 4,714 | API: for HDFStore, add the keyword dropna=True to append to change whether to write ALL nan rows to the store (GH4625) | {
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} | [] | closed | false | null | [] | null | 0 | 2013-08-31T14:09:16Z | 2014-07-16T08:25:38Z | 2013-08-31T14:09:35Z | CONTRIBUTOR | null | closes #4625
also settable via the option io.hdf.dropna_table
retains backward compat
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https://api.github.com/repos/pandas-dev/pandas/issues/4715 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4715/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4715/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4715/events | https://github.com/pandas-dev/pandas/pull/4715 | 18,823,458 | MDExOlB1bGxSZXF1ZXN0ODAwMzU3MQ== | 4,715 | API: change nomeclature in HDFStore to use format=fixed(f) | table(t) | {
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} | 0 | 2013-08-31T14:46:30Z | 2014-07-16T08:25:40Z | 2013-08-31T14:57:45Z | CONTRIBUTOR | null | see #4645
Storer has been renamed to Fixed (and all docs changed)
added option: `io.hdf.default_format` to allow the overriding of the default format
format is now specified as: `format=` with a value of (`fixed(f)` or `table(t)`)
should be a bit more intuitve
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} | 0 | 2013-08-31T19:51:41Z | 2014-06-16T09:10:52Z | 2013-08-31T20:00:14Z | CONTRIBUTOR | null | closes #4710
API: raise on trying to use a multi-index with data_columns on the same axis
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} | 20 | 2013-08-31T23:03:05Z | 2013-10-28T01:55:09Z | 2013-10-28T01:54:39Z | CONTRIBUTOR | null | E.g. for reindex on panel
```
elp on method reindex in module pandas.core.generic:
reindex(self, *args, **kwargs) method of pandas.core.panel.Panel instance
Conform DataFrame to new index with optional filling logic, placing
NA/NaN in locations having no value in the previous index. A new object
is produced unless the new index is equivalent to the current one and
copy=False
Parameters
----------
axes : array-like, optional (can be specified in order, or as keywords)
New labels / index to conform to. Preferably an Index object to
avoid duplicating data
method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
Method to use for filling holes in reindexed DataFrame
pad / ffill: propagate last valid observation forward to next valid
backfill / bfill: use NEXT valid observation to fill gap
copy : boolean, default True
Return a new object, even if the passed indexes are the same
level : int or name
Broadcast across a level, matching Index values on the
passed MultiIndex level
fill_value : scalar, default np.NaN
Value to use for missing values. Defaults to NaN, but can be any
"compatible" value
limit : int, default None
Maximum size gap to forward or backward fill
takeable : boolean, default False
treat the passed as positional values
Examples
--------
```
Functions that need parameters list/explanation:
- [x] swapaxes
- [x] pop [what does 'items' mean?]
Functions that only need list of axes:
- [x] filter [need to check if it should be defined for Series at all]
- [x] fillna [Series, Frame]
- [x] reindex_axis [needs class name + axes]
- [x] rename [might need a bit more logic to do docstring + note that args interpreted like axes??]
- [x] transpose
Functions needing a little work:
- [x] reindex
- needs to have panel check that both major and major_axis aren't passed, same with minor and minor_axis (like `SparsePanel` does)
- would be better if there were individual functions for Series Frame and Panel that explicitly enumerate axes in type signature.
Functions that may not need to be changed:
- [x] align
Functions that could use slight wording tweaks:
- [x] truncate
- [x] where
- [x] mask
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} | [] | closed | false | null | [] | null | 2 | 2013-08-31T23:24:53Z | 2014-06-12T13:18:24Z | 2013-09-01T01:21:37Z | CONTRIBUTOR | null | Fixes #4628
- MultiIndex now responds correctly to `rename` (synonym for
`set_names`)
- `set_names` checks that input is list like.
- Cleaner error message for immutable ops.
- Added in previously missing tests for MultiIndex immutable metadata.
- Altered `is_list_like` to check for binary_types as well.
For now, this is the behavior for Index.rename. It's simple and clear and lets existing code just work instead of dealing with typechecks:
``` python
In [2]: ind = Index(range(10))
In [3]: ind = ind.rename((u'A', u'B'))
In [4]: ind.name
Out[4]: (u'A', u'B')
In [5]: ind.names
Out[5]: FrozenList([(u'A', u'B')])
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4719 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4719/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4719/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4719/events | https://github.com/pandas-dev/pandas/pull/4719 | 18,829,971 | MDExOlB1bGxSZXF1ZXN0ODAwNTk4MQ== | 4,719 | DOC: Updates to CONTRIBUTING.md | {
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} | 0 | 2013-08-31T23:28:43Z | 2014-07-16T08:25:51Z | 2013-09-01T18:45:34Z | CONTRIBUTOR | null | Some updates that are probably necessary - figured I'd put this in a PR first then merge in a bit.
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https://api.github.com/repos/pandas-dev/pandas/issues/4720 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4720/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4720/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4720/events | https://github.com/pandas-dev/pandas/issues/4720 | 18,830,694 | MDU6SXNzdWUxODgzMDY5NA== | 4,720 | ENH: Question: pivot_table() column order and hierarchy of index | {
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} | [] | closed | false | null | [] | null | 5 | 2013-09-01T00:58:40Z | 2013-09-03T10:56:22Z | 2013-09-03T10:56:12Z | NONE | null | I am trying to use the pivot_table() method. Here is the sample code:
```
In [15]: import pandas as pd
In [16]: import numpy as np
In [17]: df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 6,
'B' : ['A', 'B', 'C'] * 8,
'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 4,
'D' : np.random.randn(24),
'E' : np.random.randn(24)})
In [18]: df
Out[18]:
A B C D E
0 one A foo -1.087882 -1.148548
1 one B foo 0.198324 -1.124947
2 two C foo -0.685986 -0.070714
3 three A bar 1.443773 -1.453439
4 one B bar -0.156894 0.223677
5 one C bar 0.571895 2.437650
6 two A foo 0.594358 1.010818
7 three B foo 0.897380 0.901461
8 one C foo 1.216059 -0.204750
9 one A bar -0.982300 -0.413738
10 two B bar 1.415841 -0.624734
11 three C bar -2.409420 2.532603
12 one A foo -2.215678 -1.329448
13 one B foo 0.157040 0.674976
14 two C foo 0.260635 0.741918
15 three A bar 0.369208 -0.133038
16 one B bar -0.334977 0.982732
17 one C bar 0.262204 -0.534082
18 two A foo 0.761802 0.221214
19 three B foo 0.229975 -1.616976
20 one C foo -0.288168 -0.465998
21 one A bar -0.510864 -0.734697
22 two B bar 0.165877 -0.412671
23 three C bar 0.714225 0.664559
In [19]: pd.pivot_table(df, values=['E','D'], rows=['A'], cols=['C','B'],aggfunc='sum')
Out[19]:
D E \
C bar foo bar
B A B C A B C A
A
one -1.493164 -0.491871 0.834099 -3.303559 0.355364 0.927891 -1.148435
three 1.812980 NaN -1.695195 NaN 1.127355 NaN -1.586477
two NaN 1.581718 NaN 1.356160 NaN -0.425351 NaN
C foo
B B C A B C
A
one 1.206409 1.903568 -2.477996 -0.449970 -0.670747
three NaN 3.197161 NaN -0.715515 NaN
two -1.037405 NaN 1.232031 NaN 0.671204
In [20]:
The output I would like to see instead is (pivoted using Excel)
bar
A B C
Sum of E Sum of D Sum of E Sum of D Sum of E Sum of D \
one -1.148435 -1.493164 1.206409 -0.491871 1.903568 0.834099
three -1.586477 1.812981 3.197162 -1.695195
two -1.037405 1.581718
foo
A B C
Sum of E Sum of D Sum of E Sum of D Sum of E Sum of D
-2.477996 -3.30356 -0.449971 0.355364 -0.670748 0.927891
-0.715515 1.127355
1.232032 1.35616 0.671204 -0.425351
```
1. The summation column are under the column index under Excel, while in pivot_table() they are above the column indexes. So on the columns are group by column indexes while under pandas they are grouped by the values.
2. Under Excel the values order is maintained. E and then D while in pivot_table, it is alpha sorted, first D and then E (specification has it set as E and D).
How can I mimic Excel pivoting?
Thanks
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} | 1 | 2013-09-01T02:13:11Z | 2014-06-25T23:05:47Z | 2013-09-02T02:11:31Z | CONTRIBUTOR | null | Related to #3954.
Cleanups to Exceptions in core/common, io/excel, core/format. A few places I added `# pragma: no cover`, because it looked like the code was generally unreachable. A few cleanups in tests/test_common and better error message in `_mut_exclusive` (which needs to be used in Panel.reindex later on).
Specific places to look at (you can also grep for `# pragma: no cover` in the diff):
- core/common - lines 860 and 1162
- core/format - lines 359 and 823
- io/excel - lines 73, 382
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} | 29 | 2013-09-01T08:47:05Z | 2014-06-20T14:26:19Z | 2013-09-07T21:27:23Z | MEMBER | null | See discussion in #4705.
This improves the speed of plotting timeseries with PeriodIndex (or with DatetimeIndex that is converted to a PeriodIndex during plotting) drastically: from 30 s in 0.12 (but 7 s in 0.11) to 0.2 s with this commit for the plotting of following data:
``` python
N = 10000
M = 25
df = pd.DataFrame(np.random.randn(N,M), index=pd.date_range('1/1/1975', periods=N))
df.plot()
```
Some questions I still have:
- I added a check for `PeriodIndex`, but I left the original `if isinstance(values, Index)` in place. Is this needed? Is it possible that another index than PeriodIndex ends up in PeriodConverter?
- @jreback You asked in the issue to do some tests (to at least ensure that their is a valid plot). Do you just mean by plotting it and looking if it seems OK? This seems OK. Or can you do some tests for plotting in the testing infrastructure?
- Would it be interesting to add a performance test for this case? Because it was a big decrease in speed with 0.12 which was not detected.
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https://api.github.com/repos/pandas-dev/pandas/issues/4723 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4723/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4723/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4723/events | https://github.com/pandas-dev/pandas/pull/4723 | 18,834,230 | MDExOlB1bGxSZXF1ZXN0ODAwNzU1OQ== | 4,723 | DOC: small clarification in DataFrame docstring | {
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} | [] | closed | false | null | [] | null | 2 | 2013-09-01T09:00:17Z | 2014-07-16T08:25:56Z | 2013-09-01T12:54:23Z | MEMBER | null | Two small things:
- add that it is actually for the column labels
- fixes small typo (not -> no)
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https://api.github.com/repos/pandas-dev/pandas/issues/4724 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4724/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4724/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4724/events | https://github.com/pandas-dev/pandas/issues/4724 | 18,835,194 | MDU6SXNzdWUxODgzNTE5NA== | 4,724 | BUG: frame_table > 1306 columns leads to corruption in hdf | {
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} | [] | closed | false | null | [] | null | 10 | 2013-09-01T10:46:36Z | 2013-09-03T02:19:14Z | 2013-09-01T12:25:11Z | NONE | null | ```
import pandas as pd, numpy
store = pd.HDFStore('pd.h5','w')
r = numpy.empty((1,1306))
store.put('data1', pd.DataFrame(r), table=True)
r = numpy.empty((1,1307))
store.put('data2', pd.DataFrame(r), table=True)
r = numpy.empty((1,5000))
store.put('data3', pd.DataFrame(r))
print store
store.close()
store = pd.HDFStore('pd.h5')
print store
```
Output:
```
<class 'pandas.io.pytables.HDFStore'>
File path: pd.h5
/data1 frame_table (typ->appendable,nrows->1,ncols->1306,indexers->[index])
/data2 frame_table (typ->appendable,nrows->1,ncols->1307,indexers->[index])
/data3 frame (shape->[1,5000])
<class 'pandas.io.pytables.HDFStore'>
File path: pd.h5
/data1 frame_table (typ->appendable,nrows->1,ncols->1306,indexers->[index])
/data2 [invalid_HDFStore node: object of type 'NoneType' has no len()]
/data2 frame (shape->[1,5000])
/data3
Closing remaining open files: pd.h5... done
```
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} | 2 | 2013-09-01T13:00:21Z | 2013-09-01T13:01:51Z | 2013-09-01T13:01:33Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/18558429/create-a-new-dataframe-column-from-an-existing-one
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} | 2 | 2013-09-01T17:42:42Z | 2013-09-02T13:07:12Z | 2013-09-02T13:07:12Z | CONTRIBUTOR | null | Currently failing test case:
``` python
df = DataFrame([[1,2,'foo','bar']], columns=['a','a','a','a'])
s = Series([1, 2, 'foo', 'bar'], index=['a', 'a', 'a', 'a'], name=0)
assert_series_equal(df.irow(0), s)
```
Here's what's going on right now.
``` python
In [3]: from pandas import *
In [4]: df = DataFrame([[1,2,'foo','bar']], columns=['a','a','a','a'])
In [5]: df
Out[5]:
a a a a
0 1 2 foo bar
In [6]: df.irow(0)
Out[6]:
a bar
a bar
a bar
a bar
Name: 0, dtype: object
In [7]: df.icol(0)
Out[7]:
0 1
Name: a, dtype: int64
In [8]: df.columns
Out[8]: Index([u'a', u'a', u'a', u'a'], dtype=object)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4727 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4727/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4727/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4727/events | https://github.com/pandas-dev/pandas/pull/4727 | 18,841,277 | MDExOlB1bGxSZXF1ZXN0ODAxMDAzNA== | 4,727 | BUG: Fix DataFrame.from_records w/ normal ndarray. | {
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} | [] | closed | false | null | [] | null | 4 | 2013-09-01T19:29:21Z | 2014-06-22T21:30:43Z | 2013-09-01T21:05:27Z | CONTRIBUTOR | null | I haven't looked at the `from_records` code before, which is why I'm spending all this time laying out a relatively minor change. Previously, was causing a TypeError about None not being iterable when arr.dtype.names was None. Now goes to 'last ditch' parsing instead.
This was the original test case (that dates back, in one form or another, to at least 2010), looked like this:
``` python
self.assertRaises(Exception, DataFrame.from_records, np.zeros((2, 3)))
```
Problem is that the actual Exception was a `TypeError: None is not iterable`-style exception, not something internal to the code. I changed this so now an `ndarray` that isn't structured (i.e., has `arr.dtype.names is None`) is treated separately from the structured ndarrays (so it goes into the general case 'last ditch' verison).
Now it basically produces the same thing as passing the array to the DataFrame constructor:
``` python
arr2 = np.zeros((2,3))
tm.assert_frame_equal(DataFrame.from_records(arr2), DataFrame(arr2))
```
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} | 3 | 2013-09-01T21:12:29Z | 2013-09-01T23:21:06Z | 2013-09-01T23:21:06Z | CONTRIBUTOR | null | as @jreback put it: main constructor should dispatch to from_records if either an ndarray has dtype.names or the dtype is a compound type
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} | 0 | 2013-09-01T21:38:12Z | 2014-07-15T01:06:57Z | 2013-09-02T13:07:12Z | CONTRIBUTOR | null | closes #4726
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} | 0 | 2013-09-01T22:02:45Z | 2013-09-17T04:40:23Z | 2013-09-17T04:40:23Z | CONTRIBUTOR | null | Should read_json check for bad keys at the top level and catch them instead of passing them directly to the `DataFrame` constructor?
Currently there is this test case in io/tests/test_json/test_pandas.py that shadows a slightly confusing error message:
``` python
# bad key
json = StringIO('{"badkey":["A","B"],'
'"index":["2","3"],'
'"data":[[1.0,"1"],[2.0,"2"],[null,"3"]]}')
self.assertRaises(TypeError, read_json, json,
orient="split")
```
If you change the `TypeError` to a `ValueError` so the test fails, you get the following traceback:
```
ERROR: pandas.io.tests.test_json.test_pandas:TestPandasContainer.test_frame_from_json_bad_data
/usr/local/bin/vim +256 pandas/io/tests/test_json/test_pandas.py # test_frame_from_json_bad_data
orient="split")
/usr/local/bin/vim +471 python2.7/unittest/case.py # assertRaises
callableObj(*args, **kwargs)
/usr/local/bin/vim +178 pandas/io/json.py # read_json
date_unit).parse()
/usr/local/bin/vim +238 pandas/io/json.py # parse
self._parse_no_numpy()
/usr/local/bin/vim +456 pandas/io/json.py # _parse_no_numpy
self.obj = DataFrame(dtype=None, **decoded)
TypeError: __init__() got an unexpected keyword argument 'badkey'
```
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} | 0 | 2013-09-01T22:34:14Z | 2014-06-19T19:27:10Z | 2014-06-19T19:27:10Z | CONTRIBUTOR | null | Right now, some of the error messages for in pandas/tseries/period.py are not so great:
``` python
In [9]: period = Period('2000-01-03', 'B')
In [10]: period > 1
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-10-df287da31a9f> in <module>()
----> 1 period > 1
/pandas/tseries/period.pyc in f(self, other)
172 return func(self.ordinal, other.ordinal)
173 else:
--> 174 raise TypeError(other)
175
176 f.__name__ = name
TypeError: 1
```
And in the case above, should really return `NotImplemented` instead (so that they end up with an error message like):
``` python
----> 1 1 + period
TypeError: unsupported operand type(s) for +: 'int' and 'Period'
```
Side note - that's only appropriate these are only used as magic methods (so that the Python interpreter converts them into the TypeError shown above...).
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} | 13 | 2013-09-02T01:17:54Z | 2014-06-16T19:56:48Z | 2013-09-06T02:01:08Z | CONTRIBUTOR | null | More specific Exceptions (and better testing for them) through a number of parts of pandas. In particular, in `core/frame` / tests/test_frame and tests/test_multilevel Associated changes in some other parts of pandas to support it (mostly making sparse have the same exceptions as core/frame).
- nearly everything that was a `TypeError` now checks message (to avoid `TypeError` confusion from changes in signature).
- refactored some test methods and changed some places to use `assertRaises` and `assertRaisesRegexp` as context managers.
- core/reshape no longer uses a specific `ReshapeError` (which wasn't really being used anywhere)
- marked guard test cases with `# pragma: no cover` to hopefully make it easier to use coverage.
- new `raise_with_traceback` method in `compat` which allows for 2/3 compatible preservation of traceback.
Obviously some squashing to come.
Related to #3954.
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} | 4 | 2013-09-02T09:11:05Z | 2021-04-11T03:14:36Z | null | NONE | null | related is #6490
I am new to pandas but have run into a problem which I believe is a bug.
I tried to execute the following:
```
df_sum=pd.pivot_table(df,rows=['Konskund_MEAB','ProdID'],cols=['Year'], aggfunc=lambda x: np.average(x['snittpris'],
weights=x['Antal_forsandelser'])
```
This produce a number of errors.
The following commands produce the desired results:
```
rows = ['Konskund_MEAB','ProdID']
cols = ['Year']
g = df.groupby(rows + columns)
s_av = g.apply(lambda x: np.average(x['snittpris'], weights=x['Antal_forsandelser']))
df_av = s_av.unstack(cols)
```
Posted originally on [Stackoverflow](http://stackoverflow.com/questions/18382632/pandas-pivot-table-error-when-executing-type-error).
_Another more knowledgeable user has verified the problem._
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} | 3 | 2013-09-02T19:06:01Z | 2018-07-06T22:00:08Z | 2018-07-06T22:00:07Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/18574108/how-do-convert-a-pandas-dataframe-to-xml
cc @jreback
I don't _think_ there is a standard xml, but perhaps his could be generalised? If not maybe simply adding this recipe to the cookbook is useful.
Solutions for read_xml are hacking tree with from_records
http://stackoverflow.com/a/10948591/1240268
or just as a list http://stackoverflow.com/a/16993660/1240268
too much of a dark art to generalise?
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} | [] | closed | false | null | [] | null | 5 | 2013-09-03T07:41:34Z | 2013-09-03T12:59:34Z | 2013-09-03T12:59:34Z | NONE | null | I will never try PANDAS again, but maybe the solutions for these errors will help someone else who wants to give it a try:
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/tests$ sudo nosetests *
# .........................................................................................................................S.....S.......................................................................................................................................................................................................................................................................................................................................................................SSS.............................................................................................................................................................................................................................S........................S.........................................................................................F.....................................F..............F...F.......F....................................................................SSS...............................FF............................................................................................................................................................................................S........................S............................................................................F.....................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................S......................................................................................................................S.......................................................................................................................................................................................................................................................................................................S...S............SSS.........S............SS...........SS.....SSSS.....S...S............SSS.........S............SS...........SS.....SSSS...............................................................................................................................................S..............................................................................................................................................S...................................................................................................................S..............................................................................................................................................S................................................................................................................................................................................................
## FAIL: test_cast_internals (pandas.tests.test_frame.TestDataFrame)
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/tests/test_frame.py", line 9092, in test_cast_internals
assert_frame_equal(casted, expected)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/util/testing.py", line 250, in assert_frame_equal
assert(left.columns.names == right.columns.names)
AssertionError
#
## FAIL: test_constructor_from_items (pandas.tests.test_frame.TestDataFrame)
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/tests/test_frame.py", line 2765, in test_constructor_from_items
assert_frame_equal(recons, self.frame)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/util/testing.py", line 250, in assert_frame_equal
assert(left.columns.names == right.columns.names)
AssertionError
#
## FAIL: test_constructor_more (pandas.tests.test_frame.TestDataFrame)
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/tests/test_frame.py", line 2565, in test_constructor_more
tm.assert_frame_equal(dm, self.frame)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/util/testing.py", line 250, in assert_frame_equal
assert(left.columns.names == right.columns.names)
AssertionError
#
## FAIL: test_constructor_orient (pandas.tests.test_frame.TestDataFrame)
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/tests/test_frame.py", line 2713, in test_constructor_orient
assert_frame_equal(recons, expected)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/util/testing.py", line 249, in assert_frame_equal
assert(left.index.names == right.index.names)
AssertionError
#
## FAIL: test_constructor_subclass_dict (pandas.tests.test_frame.TestDataFrame)
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/tests/test_frame.py", line 2267, in test_constructor_subclass_dict
assert_frame_equal(self.frame.sort_index(), frame)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/util/testing.py", line 249, in assert_frame_equal
assert(left.index.names == right.index.names)
AssertionError
#
## FAIL: test_getitem_fancy_2d (pandas.tests.test_frame.TestDataFrame)
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/tests/test_frame.py", line 620, in test_getitem_fancy_2d
assert_frame_equal(ix[:, :2], f.reindex(columns=['A', 'B']))
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/util/testing.py", line 250, in assert_frame_equal
assert(left.columns.names == right.columns.names)
AssertionError
#
## FAIL: test_getitem_fancy_boolean (pandas.tests.test_frame.TestDataFrame)
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/tests/test_frame.py", line 1106, in test_getitem_fancy_boolean
assert_frame_equal(result, expected)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/util/testing.py", line 250, in assert_frame_equal
assert(left.columns.names == right.columns.names)
AssertionError
#
## FAIL: test_time_series_plot_color_with_empty_kwargs (pandas.tests.test_graphics.TestDataFrameGroupByPlots)
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/tests/test_graphics.py", line 752, in test_time_series_plot_color_with_empty_kwargs
self.assert_(line_colors == ['b', 'g', 'r'])
AssertionError: False is not true
---
Ran 3382 tests in 341.290s
FAILED (SKIP=46, failures=8)
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/tests$
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https://api.github.com/repos/pandas-dev/pandas/issues/4736 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4736/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4736/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4736/events | https://github.com/pandas-dev/pandas/issues/4736 | 18,921,373 | MDU6SXNzdWUxODkyMTM3Mw== | 4,736 | installation issue - Throws an exception when DataReader can't get a 200 response from | {
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} | [] | closed | false | null | [] | null | 1 | 2013-09-03T17:21:17Z | 2013-09-03T22:20:29Z | 2013-09-03T22:20:29Z | NONE | null | Mac OSX 10.6.8
1) First I installed the latest version of every dependency, every recommended dependency, and every optional dependency listed here (including the BeautifulSoup4 and html5lib combo):
http://pandas.pydata.org/pandas-docs/stable/install.html
...using the standard:
```
$ sudo python setup.py install
```
Then I installed the latest version of pandas, and then I installed the latest version of statsmodels(which depends on pandas) the same way.
2) Then I spent an hour trying to figure out how to run nosetests without getting the message "ran 0 tests":
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/tests$ sudo nosetests *
...which produced this:
## FAIL: test_cast_internals (pandas.tests.test_frame.TestDataFrame)
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/tests/test_frame.py", line 9092, in test_cast_internals
assert_frame_equal(casted, expected)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/util/testing.py", line 250, in assert_frame_equal
assert(left.columns.names == right.columns.names)
AssertionError
#
## FAIL: test_constructor_from_items (pandas.tests.test_frame.TestDataFrame)
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/tests/test_frame.py", line 2765, in test_constructor_from_items
assert_frame_equal(recons, self.frame)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/util/testing.py", line 250, in assert_frame_equal
assert(left.columns.names == right.columns.names)
AssertionError
#
## FAIL: test_constructor_more (pandas.tests.test_frame.TestDataFrame)
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/tests/test_frame.py", line 2565, in test_constructor_more
tm.assert_frame_equal(dm, self.frame)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/util/testing.py", line 250, in assert_frame_equal
assert(left.columns.names == right.columns.names)
AssertionError
#
## FAIL: test_constructor_orient (pandas.tests.test_frame.TestDataFrame)
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/tests/test_frame.py", line 2713, in test_constructor_orient
assert_frame_equal(recons, expected)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/util/testing.py", line 249, in assert_frame_equal
assert(left.index.names == right.index.names)
AssertionError
#
## FAIL: test_constructor_subclass_dict (pandas.tests.test_frame.TestDataFrame)
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/tests/test_frame.py", line 2267, in test_constructor_subclass_dict
assert_frame_equal(self.frame.sort_index(), frame)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/util/testing.py", line 249, in assert_frame_equal
assert(left.index.names == right.index.names)
AssertionError
#
## FAIL: test_getitem_fancy_2d (pandas.tests.test_frame.TestDataFrame)
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/tests/test_frame.py", line 620, in test_getitem_fancy_2d
assert_frame_equal(ix[:, :2], f.reindex(columns=['A', 'B']))
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/util/testing.py", line 250, in assert_frame_equal
assert(left.columns.names == right.columns.names)
AssertionError
#
## FAIL: test_getitem_fancy_boolean (pandas.tests.test_frame.TestDataFrame)
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/tests/test_frame.py", line 1106, in test_getitem_fancy_boolean
assert_frame_equal(result, expected)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/util/testing.py", line 250, in assert_frame_equal
assert(left.columns.names == right.columns.names)
AssertionError
#
## FAIL: test_time_series_plot_color_with_empty_kwargs (pandas.tests.test_graphics.TestDataFrameGroupByPlots)
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/tests/test_graphics.py", line 752, in test_time_series_plot_color_with_empty_kwargs
self.assert_(line_colors == ['b', 'g', 'r'])
AssertionError: False is not true
---
Ran 3382 tests in 341.290s
FAILED (SKIP=46, failures=8)
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.11.0-py2.7-macosx-10.6-intel.egg/pandas/tests$
3) Then I moved the pandas egg out of the site-packages directory, and I installed pip, and I used pip to install pandas:
```
~$ pip install pandas
```
4) Then I ran nosetests like this:
```
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages$ nosetests pandas
```
And that produced one failed test:
## FAIL: Throws an exception when DataReader can't get a 200 response from
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/util/testing.py", line 765, in network_wrapper
return t(_args, *_kwargs)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/io/tests/test_data.py", line 370, in test_fred
15984.1)
AssertionError: DATE
2013-01-01 16535.3
Name: GDP, dtype: float64 != 15984.1
---
Ran 3568 tests in 382.962s
FAILED (SKIP=140, failures=1)
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} | 2 | 2013-09-03T20:13:39Z | 2021-04-11T03:15:33Z | null | CONTRIBUTOR | null | related #2330, #2821
pretty easy to do; doesn't really take much time either (can be done in the block manager)
http://stackoverflow.com/questions/18599579/python-pandas-multi-index
could also easily convert them (which in theory could be expensive so should be a method)
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} | 83 | 2013-09-03T22:01:37Z | 2014-06-17T15:59:26Z | 2013-10-01T00:03:52Z | CONTRIBUTOR | null | related #4657
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} | 17 | 2013-09-03T23:03:21Z | 2014-06-20T00:03:33Z | 2013-09-15T22:08:02Z | CONTRIBUTOR | null | Refactored pandas.io.excel.ExcelWriter to allow other
writer engines and added xlsxwriter as an option.
GitHub issue #4542.
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} | 7 | 2013-09-03T23:35:28Z | 2014-06-22T17:56:54Z | 2013-09-10T06:40:38Z | CONTRIBUTOR | null | closes #2320
Can use ohlc from DataFrame.
cc @jreback
Example:
```
In [24]: df = pd.DataFrame({'PRICE': {Timestamp('2011-01-06 10:59:05', tz=None): 24990,
Timestamp('2011-01-06 12:43:33', tz=None): 25499,
Timestamp('2011-01-06 12:54:09', tz=None): 25499},
'VOLUME': {Timestamp('2011-01-06 10:59:05', tz=None): 1500000000,
Timestamp('2011-01-06 12:43:33', tz=None): 5000000000,
Timestamp('2011-01-06 12:54:09', tz=None): 100000000}})
In [25]: df.resample('H', how='ohlc')
Out[25]:
PRICE VOLUME \
open high low close open high
2011-01-06 10:00:00 24990 24990 24990 24990 1500000000 1500000000
2011-01-06 11:00:00 NaN NaN NaN NaN NaN NaN
2011-01-06 12:00:00 25499 25499 25499 25499 5000000000 5000000000
low close
2011-01-06 10:00:00 1500000000 1500000000
2011-01-06 11:00:00 NaN NaN
2011-01-06 12:00:00 100000000 100000000
```
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} | 1 | 2013-09-03T23:47:50Z | 2013-09-29T17:56:56Z | 2013-09-29T17:56:56Z | CONTRIBUTOR | null | related #2320
`df.groupby('A').describe()`
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} | 3 | 2013-09-04T01:32:22Z | 2016-05-10T11:26:26Z | 2013-09-06T23:56:14Z | NONE | null | New to pandas, so not sure if bug or stupidity, but the behavior is not what I expect. Pandas version is 0.12.0 on Mac OS X with python 2.7.5.
In [12]: df = pd.DataFrame({'a': [3, 3], 'b': [3, 3]})
In [13]: df.plot()
Out[13]: <matplotlib.axes.AxesSubplot at 0x10fb6e2d0>
In [14]: df_two = pd.DataFrame({'a': [None, None], 'b': [None, None]})
In [15]: df_two
Out[15]:
a b
0 None None
1 None None
In [16]: df_two = df_two.fillna(3)
In [17]: df_two
Out[17]:
a b
0 3 3
1 3 3
## In [18]: df_two.plot()
TypeError Traceback (most recent call last)
<ipython-input-18-7a340ea12677> in <module>()
----> 1 df_two.plot()
/usr/local/Cellar/python/2.7.5/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/tools/plotting.pyc in plot_frame(frame, x, y, subplots, sharex, sharey, use_index, figsize, grid, legend, rot, ax, style, title, xlim, ylim, logx, logy, xticks, yticks, kind, sort_columns, fontsize, secondary_y, *_kwds)
1634 logy=logy, sort_columns=sort_columns,
1635 secondary_y=secondary_y, *_kwds)
-> 1636 plot_obj.generate()
1637 plot_obj.draw()
1638 if subplots:
/usr/local/Cellar/python/2.7.5/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/tools/plotting.pyc in generate(self)
852 def generate(self):
853 self._args_adjust()
--> 854 self._compute_plot_data()
855 self._setup_subplots()
856 self._make_plot()
/usr/local/Cellar/python/2.7.5/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/tools/plotting.pyc in _compute_plot_data(self)
947 if is_empty:
948 raise TypeError('Empty {0!r}: no numeric data to '
--> 949 'plot'.format(numeric_data.__class__.**name**))
950
951 self.data = numeric_data
TypeError: Empty 'DataFrame': no numeric data to plot
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} | 7 | 2013-09-04T02:21:00Z | 2013-09-06T23:40:44Z | 2013-09-06T23:40:44Z | MEMBER | null | http://pandas.pydata.org/pandas-docs/dev/missing_data.html#replacing-generic-values
and scroll down a bit. Caught this in a doc build. I'll take a look at it.
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} | [] | closed | false | null | [] | null | 8 | 2013-09-04T10:05:39Z | 2013-09-04T12:52:21Z | 2013-09-04T11:58:11Z | CONTRIBUTOR | null | https://www.dropbox.com/s/rgy9iqt9o8pvfdl/wontplot.pandas
``` python
In [3]: s = pd.load('wontplot.pandas')
In [5]: s.plot()
Traceback (most recent call last):
File "<ipython-input-5-d3eb09d34df4>", line 1, in <module>
s.plot()
File "C:\dev\bin\Python27\lib\site-packages\pandas\tools\plotting.py", line 1727, in plot_series
plot_obj.generate()
File "C:\dev\bin\Python27\lib\site-packages\pandas\tools\plotting.py", line 853, in generate
self._make_plot()
File "C:\dev\bin\Python27\lib\site-packages\pandas\tools\plotting.py", line 1241, in _make_plot
x = self._get_xticks(convert_period=True)
File "C:\dev\bin\Python27\lib\site-packages\pandas\tools\plotting.py", line 1031, in _get_xticks
self.data = self.data.reindex(index=index.order())
File "C:\dev\bin\Python27\lib\site-packages\pandas\core\series.py", line 2638, in reindex
return Series(new_values, index=new_index, name=self.name)
File "C:\dev\bin\Python27\lib\site-packages\pandas\core\series.py", line 489, in __new__
subarr.index = index
File "properties.pyx", line 74, in pandas.lib.SeriesIndex.__set__ (pandas\lib.c:29092)
AssertionError: Index length did not match values
In [6]: s.index.shape
Out[6]: (480,)
In [7]: s.values.shape
Out[7]: (480,)
```
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} | 0 | 2013-09-04T12:18:08Z | 2013-09-13T11:08:18Z | 2013-09-13T11:08:18Z | CONTRIBUTOR | null | Basically, make a clear interface for `ExcelWriter` and allow others to plug into it. Unfortunately, it looks like we're stuck with using `ExcelWriter` directly as a class, so might take a bit more complexity to stick this in (i.e., a metaclass). If we can convert it to a function instead, can make a lot less magic. Check out [this branch](https://github.com/jtratner/pandas/compare/refactor-excel-writer) if you want to see what I'm talking about.
We'd end up with a separate config option for each extension (`io.excel.xlsx.writer`, `io.excel.xls.writer`, `io.excel.xlsm.writer`) (can't do the reverse because `io.excel.writer.xls` would match all the different filetypes).
Client code then just has to implement `save` and `write_cells` and define `supported_extensions` and register their their class with `register_engine` in pandas/io/excel, then it's easily available under `to_excel` as an engine parameter.
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} | 0 | 2013-09-04T12:47:46Z | 2013-09-06T23:09:08Z | 2013-09-06T23:09:08Z | MEMBER | null | When trying to reindex with an index with duplicate values, I get the error message `ValueError: Shape of passed values is (1, 20), indices imply (1, 10)`, while I expected somthing like `Reindexing only valid with uniquely valued Index objects`
I see this with both 0.12 and master.
An example:
``` python
In [2]: pd.__version__
Out[2]: '0.12.0-274-gc472099'
In [3]: s = pd.DataFrame(np.random.randn(10), index=[1,2,3,4,5,1,2,3,4,5])
In [4]: s
Out[4]:
0
1 -0.067753
2 -1.544545
3 0.726160
4 0.562298
5 -0.094550
1 0.753334
2 -0.893318
3 -0.275085
4 2.645206
5 -0.926170
In [5]: s.reindex(index=range(len(s)))
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-6-2085c8b16fe3> in <module>()
----> 1 s.reindex(index=range(len(s)))
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\generic.pyc in reindex(self, *args, **kwargs)
1085
1086 # perform the reindex on the axes
-> 1087 return self._reindex_axes(axes, level, limit, method, fill_value, copy, takeable=takeable)._propogate_attributes(self)
1088
1089 def _reindex_axes(self, axes, level, limit, method, fill_value, copy, takeable=False):
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\frame.pyc in _reindex_axes(self, axes, level, limit, method, fill_value, copy, takeable)
2268 if index is not None:
2269 frame = frame._reindex_index(index, method, copy, level,
-> 2270 fill_value, limit, takeable=takeable)
2271
2272 return frame
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\frame.pyc in _reindex_index(self, new_index, method, copy, level, fill_value, limit, takeable)
2278 takeable=takeable)
2279 return self._reindex_with_indexers({0: [new_index, indexer]},
-> 2280 copy=copy, fill_value=fill_value)
2281
2282 def _reindex_columns(self, new_columns, copy, level, fill_value=NA,
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\generic.pyc in _reindex_with_indexers(self, reindexers, method, fill_value, limit, copy)
1184 indexer = com._ensure_int64(indexer)
1185 new_data = new_data.reindex_indexer(index, indexer, axis=baxis,
-> 1186 fill_value=fill_value)
1187
1188 elif baxis == 0 and index is not None and index is not new_data.axes[baxis]:
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\internals.pyc in reindex_indexer(self, new_axis, indexer, axis, fill_value)
2673 new_axes = list(self.axes)
2674 new_axes[axis] = new_axis
-> 2675 return self.__class__(new_blocks, new_axes)
2676
2677 def _reindex_indexer_items(self, new_items, indexer, fill_value):
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\internals.pyc in __init__(self, blocks, axes, do_integrity_check, fastpath)
1563
1564 if do_integrity_check:
-> 1565 self._verify_integrity()
1566
1567 self._has_sparse = False
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\internals.pyc in _verify_integrity(self)
1826 if not block.is_sparse and block.values.shape[1:] != mgr_shape[1:]:
1827 construction_error(
-> 1828 tot_items, block.values.shape[1:], self.axes)
1829 if len(self.items) != tot_items:
1830 raise AssertionError('Number of manager items must equal union of '
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\internals.pyc in construction_error(tot_items, block_shape, axes)
3063 raise ValueError("Shape of passed values is %s, indices imply %s" % (
3064 tuple(map(int, [tot_items] + list(block_shape))),
-> 3065 tuple(map(int, [len(ax) for ax in axes]))))
3066
3067
ValueError: Shape of passed values is (1, 15), indices imply (1, 10)
In [6]: s.reindex(index=s.index.order())
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-2ce611a93f5d> in <module>()
----> 1 s.reindex(index=s.index.order())
.....
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\internals.py in construction_error(tot_items, block_shape, axes)
3063 raise ValueError("Shape of passed values is %s, indices imply %s" % (
3064 tuple(map(int, [tot_items] + list(block_shape))),
-> 3065 tuple(map(int, [len(ax) for ax in axes]))))
3066
3067
ValueError: Shape of passed values is (1, 20), indices imply (1, 10)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4747 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4747/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4747/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4747/events | https://github.com/pandas-dev/pandas/issues/4747 | 18,979,874 | MDU6SXNzdWUxODk3OTg3NA== | 4,747 | BUG: Passing column list as 'set' with frame_table with multi index causes crash | {
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} | [] | closed | false | null | [] | null | 2 | 2013-09-04T15:53:40Z | 2013-09-04T18:59:49Z | 2013-09-04T17:08:49Z | NONE | null | I hesitate to call this a real bug. I think this is a fault of Python having different syntaxes for set and list add operations, but perhaps people should be aware of this (I was blindsided).
Basically Pandas is appending the row MultiIndex as columns to the columns that we pass. If the columns happen to be a set we get this error (but only for MultiIndexes).
I wonder if a test for set/list would slow things down too much?
```
import pandas as pd, numpy
r = numpy.empty((4,3))
index = pd.MultiIndex.from_tuples([['A','a'],['A','b'],['B','a'],['B','b']],names=['lev1','lev2'])
df = pd.DataFrame(r,columns=['a','b','c'])
df_m = pd.DataFrame(r,index=index,columns=['a','b','c'])
store = pd.HDFStore('settest.h5')
store.put('data',df,table=True,data_columns=True)
store.put('datam',df_m,table=True,data_columns=True)
print store.select('data',where='a<2',columns=set(['a','b'])) #OK
print store.select('datam',where='a<2',columns=set(['a','b'])) #AttributeError: 'set' object has no attribute 'insert'
#Pandas is appending the indexes as columns. I wonder if a test would slow things down.
#The real issue is perhaps Python itself having different syntax for sets and lists
```
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} | 16 | 2013-09-04T19:23:07Z | 2014-06-21T17:17:09Z | 2013-09-06T23:40:44Z | MEMBER | null | Also reverts the `method` parameter of `NDFrame.replace()` deprecation
closes #4743
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} | 1 | 2013-09-04T22:48:52Z | 2014-03-26T16:52:59Z | 2013-09-07T04:23:45Z | NONE | null | Please see http://nbviewer.ipython.org/6443825 . Below is an unformatted version.
pandas' read_csv is supposed to handle short rows by filling them with NaN. It does so most of the time. However, if the first row is short and you specify header=None, then you get an error. This happens whether or not you specify column names.
In [20]:
from pandas import DataFrame, read_csv
This works:
In [21]:
mydata='1,2,3\n1,2\n1,2\n'
read_csv(StringIO.StringIO(mydata),header=None,names=['one','two','three'])
Out[21]:
one two three
0 1 2 3
1 1 2 NaN
2 1 2 NaN
But this doesn't:
In [22]:
mydata='1,2\n1,2,3\n4,5,6\n'
read_csv(StringIO.StringIO(mydata),header=None,names=['one','two','three'])
---
CParserError Traceback (most recent call last)
<ipython-input-22-4dc7e64ee312> in <module>()
1 mydata='1,2\n1,2,3\n4,5,6\n'
----> 2 read_csv(StringIO.StringIO(mydata),header=None,names=['one','two','three'])
/usr/lib/python2.7/dist-packages/pandas/io/parsers.pyc in parser_f(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, nrows, iterator, chunksize, verbose, encoding, squeeze)
397 buffer_lines=buffer_lines)
398
--> 399 return _read(filepath_or_buffer, kwds)
400
401 parser_f.__name__ = name
/usr/lib/python2.7/dist-packages/pandas/io/parsers.pyc in _read(filepath_or_buffer, kwds)
206
207 # Create the parser.
--> 208 parser = TextFileReader(filepath_or_buffer, **kwds)
209
210 if nrows is not None:
/usr/lib/python2.7/dist-packages/pandas/io/parsers.pyc in **init**(self, f, engine, **kwds)
505 self.options['has_index_names'] = kwds['has_index_names']
506
--> 507 self._make_engine(self.engine)
508
509 def _get_options_with_defaults(self, engine):
/usr/lib/python2.7/dist-packages/pandas/io/parsers.pyc in _make_engine(self, engine)
607 def _make_engine(self, engine='c'):
608 if engine == 'c':
--> 609 self._engine = CParserWrapper(self.f, **self.options)
610 else:
611 if engine == 'python':
/usr/lib/python2.7/dist-packages/pandas/io/parsers.pyc in **init**(self, src, *_kwds)
888 # #2442
889 kwds['allow_leading_cols'] = self.index_col is not False
--> 890 self._reader = _parser.TextReader(src, *_kwds)
891
892 # XXX
/usr/lib/python2.7/dist-packages/pandas/_parser.so in pandas._parser.TextReader.__cinit__ (pandas/src/parser.c:3946)()
/usr/lib/python2.7/dist-packages/pandas/_parser.so in pandas._parser.TextReader._get_header (pandas/src/parser.c:5628)()
CParserError: Column names have 3 fields, data has 2 fields
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https://api.github.com/repos/pandas-dev/pandas/issues/4750 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4750/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4750/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4750/events | https://github.com/pandas-dev/pandas/pull/4750 | 19,012,083 | MDExOlB1bGxSZXF1ZXN0ODA5MjAwNw== | 4,750 | CLN: Make ExcelWriter more pluggable | {
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} | 19 | 2013-09-05T00:25:02Z | 2014-06-17T06:40:42Z | 2013-09-13T11:08:18Z | CONTRIBUTOR | null | FIxes #4745 and makes it easier to swap in other `ExcelWriter`s.
Basically, you can subclass `ExcelWriter` (or not), register the engine with `register_engine`, and then either pass it as an `engine` keyword to `read_excel` or set it as the default writer using the config options (i.e., `io.excel.xlsx.writer` or `io.excel.xls.writer`). Engine names are validated by checking that they are already defined and that they are able to read files of that type (that said, if you can set up external config files to be parsed by pandas, this validation should be removed, because it won't work if pandas is loaded before an external dependency [e.g., a plugin that registers a writer class]). When you call `ExcelWriter`, the metaclass swaps in the appropriate engine class automatically [thereby being backwards compatible].
The great thing is that it should be _really_ simple to add additional writers and relatively trivial to add additional readers by following the same format. (if, for example, we wanted to resurrect `openpyxl` for brave souls or something).
Here's the 'interface':
- Mandatory (but not checked at run time):
- `write_cells(self, cells, sheet_name=None, startrow=0, startcol=0)`
--> called to write additional DataFrames to disk
- `supported_extensions` (tuple of supported extensions), used to check
that engine supports the given extension.
- `engine` - string that gives the engine name. Necessary to
instantiate class directly and bypass `ExcelWriterMeta` engine lookup.
- `save(self)` --> called to save file to disk
- Optional:
- `__init__(self, path, **kwargs)` --> always called with path as first
argument.
- `check_extension(cls, ext)` --> called to check that the engine supports a
given extension (and used to validate user selection of engine as option).
This supercedes `supported_exceptions`.
You also need to register the class with `register_engine()`. If you don't subclass `ExcelWriter`,
you will have to implement `check_extensions(ext)` yourself.
There's a tiny bit of metaclass magic to make this backwards-compatible, but it's nothing particularly complicated.
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] | null | 12 | 2013-09-05T01:48:14Z | 2015-08-10T12:29:04Z | 2013-09-05T02:37:09Z | CONTRIBUTOR | null | Fixes #4328.
Now passes `quotechar` to csv's writer. Default is same as csv (`'"'`), but sets to `None` with `quoting=csv.QUOTE_NONE`.
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} | 1 | 2013-09-05T13:39:28Z | 2014-07-16T08:26:35Z | 2013-09-06T22:01:31Z | NONE | null | `get_splitter` was called directly, then again through an internal method. Results from the first call are not used for anything and immediately replaced.
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} | 10 | 2013-09-05T14:44:45Z | 2014-06-27T17:57:48Z | 2013-09-06T23:06:27Z | CONTRIBUTOR | null | fixes a pickle compat issue for < 0.13 when presented with a TimeSeries (was an oversight on the initial PR).
additional tests as well
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} | 2 | 2013-09-05T18:13:02Z | 2014-06-16T16:32:22Z | 2013-09-06T03:11:15Z | CONTRIBUTOR | null | closes #2702
Base use cases (note that a completely changed series will morph to the new dtype if necessary)
```
In [1]: s = Series(list('abc'))
In [2]: s[0:3] = list(range(3))
In [3]: s
Out[3]:
0 0
1 1
2 2
dtype: int64
In [4]: s = Series(list('abc'))
In [5]: s[0:2] = list(range(2))
In [6]: s
Out[6]:
0 0
1 1
2 c
dtype: object
```
This is a bit odd, but allowed
```
In [4]: s[0] = list(range(10))
In [5]: s
Out[5]:
0 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
1 b
2 c
dtype: object
```
These all will raise error messages now
```
In [1]: s = Series(list('abc'))
In [2]: s[0:3] = list(range(27))
ValueError: cannot set using a slice indexer with a different length than the value
In [3]: s[[0,1,2]] = list(range(27))
ValueError: cannot set using a list-like indexer with a different length than the value
In [4]: s[[0,1,2]] = list(range(2))
ValueError: cannot set using a list-like indexer with a different length than the value
```
With a step and a slice indexer (not sure was tested before)
When the length of the slicer matches exactly it works
```
In [5]: s = Series(list('abcdef'))
In [6]: s[0:4:2] = list(range(2))
In [7]: s
Out[7]:
0 0
1 b
2 1
3 d
4 e
5 f
dtype: object
```
Raises when it doesn't
```
In [8]: s[0:4:2] = list(range(27))
ValueError: cannot set using a slice indexer with a different length than the value
```
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} | 1 | 2013-09-05T19:23:07Z | 2014-06-16T12:19:34Z | 2013-09-06T23:09:09Z | CONTRIBUTOR | null | closes #4746
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} | 4 | 2013-09-05T20:33:10Z | 2013-11-27T10:14:52Z | 2013-09-06T08:16:56Z | MEMBER | null | See http://stackoverflow.com/questions/18643030/groupby-and-multi-indexing/18644920#18644920
Basically, indexing a DateTimeIndex with a string does work in a single index, but not always in a MultiIndex:
```
In [7]: df= pd.DataFrame({'ACCOUNT':["ACCT1", "ACCT1", "ACCT1", "ACCT2"],
...: 'TICKER':["ABC", "MNP", "XYZ", "XYZ"],
...: 'val':[1,2,3,4]},
...: index=pd.date_range("2013-06-19 09:30:00", periods=4, freq='5T'))
In [8]: df_multi = df.set_index(['ACCOUNT', 'TICKER'], append=True)
In [9]: df
Out[9]:
ACCOUNT TICKER val
2013-06-19 09:30:00 ACCT1 ABC 1
2013-06-19 09:35:00 ACCT1 MNP 2
2013-06-19 09:40:00 ACCT1 XYZ 3
2013-06-19 09:45:00 ACCT2 XYZ 4
In [10]: df_multi
Out[10]:
val
ACCOUNT TICKER
2013-06-19 09:30:00 ACCT1 ABC 1
2013-06-19 09:35:00 ACCT1 MNP 2
2013-06-19 09:40:00 ACCT1 XYZ 3
2013-06-19 09:45:00 ACCT2 XYZ 4
```
Accessing a row with string in single index works:
```
In [11]: df.loc['2013-06-19 09:30:00']
Out[11]:
ACCOUNT ACCT1
TICKER ABC
val 1
Name: 2013-06-19 09:30:00, dtype: object
```
In a MultiIndex it doesn't work:
```
In [12]: df_multi.loc[('2013-06-19 09:30:00', 'ACCT1', 'ABC')]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last) <ipython-input-12-cbb2ba302ffa> in <module>()
----> 1 df_multi.loc[('2013-06-19 09:30:00', 'ACCT1', 'ABC')]
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\indexing.pyc in __getitem__(self, key)
722 def __getitem__(self, key):
723 if type(key) is tuple:
--> 724 return self._getitem_tuple(key)
725 else:
726 return self._getitem_axis(key, axis=0)
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\indexing.pyc in _getitem_tuple(self, tup)
285
286 # no multi-index, so validate all of the indexers
--> 287 self._has_valid_tuple(tup)
288
289 # ugly hack for GH #836
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\indexing.pyc in _has_valid_tuple(self, key)
717 if i >= self.obj.ndim:
718 raise ValueError('Too many indexers')
--> 719 if not self._has_valid_type(k,i):
720 raise ValueError("Location based indexing can only have [%s] types" % self._valid_types)
721
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\indexing.pyc in _has_valid_type(self, key, axis)
792
793 if not key in ax:
--> 794 raise KeyError("the label [%s] is not in the [%s]" % (key,self.obj._get_axis_name(axis)))
795
796 return True
KeyError: 'the label [ACCT1] is not in the [columns]'
```
But when only accessing the first two levels of the three levels of the MultiIndex, it does work:
```
In [13]: df_multi.loc[('2013-06-19 09:30:00', 'ACCT1')]
Out[13]:
val
TICKER
ABC 1
```
And when using a Timestamp it also works (as it should be):
```
In [14]: df_multi.loc[(pd.Timestamp('2013-06-19 09:30:00', tz=None), 'ACCT1', 'ABC')]
Out[14]:
val 1
Name: (2013-06-19 09:30:00, ACCT1, ABC), dtype: int64
```
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} | 0 | 2013-09-06T01:27:20Z | 2013-09-15T05:01:34Z | 2013-09-15T05:01:34Z | CONTRIBUTOR | null | Fix this Exception to be better (probably the same `tuple-index` error or something...)
``` python
>>> import pandas.util.testing as tm
>>> ts = tm.makeTimeSeries()
>>> ts[:, 2] = None
Traceback (most recent call last):
File "<console>", line 1, in <module>
File "pandas/core/series.py", line 1061, in __setitem__
self._set_with(key, value)
File "pandas/core/series.py", line 1103, in _set_with
self._set_labels(key, value)
File "pandas/core/series.py", line 1110, in _set_labels
indexer = self.index.get_indexer(key)
File "pandas/core/index.py", line 920, in get_indexer
return this.get_indexer(target, method=method, limit=limit)
File "pandas/core/index.py", line 935, in get_indexer
indexer = self._engine.get_indexer(target.values)
File "index.pyx", line 267, in pandas.index.IndexEngine.get_indexer (pandas/index.c:5079)
File "hashtable.pyx", line 722, in pandas.hashtable.PyObjectHashTable.lookup (pandas/hashtable.c:11394)
TypeError: unhashable type
```
Compare to this exception for **getitem** for the same thing:
``` python
>>> ts[:, 2]
Traceback (most recent call last):
File "<console>", line 1, in <module>
File "pandas/core/series.py", line 944, in __getitem__
return self._get_with(key)
File "pandas/core/series.py", line 961, in _get_with
return self._get_values_tuple(key)
File "pandas/core/series.py", line 1007, in _get_values_tuple
raise ValueError('Can only tuple-index with a MultiIndex')
ValueError: Can only tuple-index with a MultiIndex
```
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} | 4 | 2013-09-06T02:26:58Z | 2014-07-16T08:26:47Z | 2013-09-06T02:49:12Z | CONTRIBUTOR | null | Moves print_versions under util so that it can be called from buildbot
(and so we can tell people to run `from pandas.util.print_versions import show_versions; show_versions()`
when trying to debug)
cc @yarikoptic
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} | 0 | 2013-09-06T03:08:12Z | 2014-07-02T05:01:36Z | 2013-09-06T08:16:56Z | CONTRIBUTOR | null | closes #4758
```
In [2]: df = DataFrame({'ACCOUNT':["ACCT1", "ACCT1", "ACCT1", "ACCT2"],
...: 'TICKER':["ABC", "MNP", "XYZ", "XYZ"],
...: 'val':[1,2,3,4]},
...: index=date_range("2013-06-19 09:30:00", periods=4, freq='5T'))
In [3]: df_multi = df.set_index(['ACCOUNT', 'TICKER'], append=True)
In [4]: df_multi.loc[(pd.Timestamp('2013-06-19 09:30:00', tz=None), 'ACCT1', 'ABC')]
Out[4]:
val 1
Name: (2013-06-19 09:30:00, ACCT1, ABC), dtype: int64
In [5]: df_multi.loc[('2013-06-19 09:30:00', 'ACCT1', 'ABC')]
Out[5]:
val 1
Name: (2013-06-19 09:30:00, ACCT1, ABC), dtype: int64
```
This is quite difficult to do, a partial selection on a single indexer, so KeyError for now
```
In [6]: df_multi.loc[('2013-06-19', 'ACCT1', 'ABC')]
KeyError: 'the label [ACCT1] is not in the [columns]'
```
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} | 1 | 2013-09-06T04:46:59Z | 2014-07-16T08:26:51Z | 2013-09-06T04:50:46Z | CONTRIBUTOR | null | Just a quick typo fix.
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} | 1 | 2013-09-06T18:04:18Z | 2013-09-06T20:07:10Z | 2013-09-06T20:07:10Z | MEMBER | null | ```
In [5]: s = Series(list('abc'))
In [6]: s.isin('a')
Out[6]:
0 True
1 False
2 False
dtype: bool
In [5]: s = Series(list('abc'))
In [6]: s.isin('a')
Out[6]:
0 True
1 False
2 False
dtype: bool
In [7]: s = Series(['aaa', 'b', 'c'])
In [8]: s.isin('aaa')
Out[8]:
0 False
1 False
2 False
dtype: bool
```
I would expect:
```
In [7]: s = Series(['aaa', 'b', 'c'])
In [8]: s.isin('aaa')
Out[8]:
0 True
1 False
2 False
dtype: bool
```
or a raise....but raising seems inconsistent with the fact that single strings work....
this is happening bc the iterable passed in is turned into a set which is why a single string works
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} | 10 | 2013-09-06T18:56:25Z | 2013-09-18T12:29:24Z | 2013-09-18T12:29:24Z | CONTRIBUTOR | null | cc @Komnomnomnom
was reported by Gagi, but I reproduced this on building on AMD64
This is current master
can you take a look?
thanks
```
building 'pandas.json' extension
creating build\temp.win-amd64-2.7\Release\pandas\src\ujson
creating build\temp.win-amd64-2.7\Release\pandas\src\ujson\python
creating build\temp.win-amd64-2.7\Release\pandas\src\ujson\lib
C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\Bin\amd64\cl.exe /c /nologo /Ox /MD /W3 /GS- /DNDEBUG -Ipandas/src/ujson/python -Ipandas/src/ujson/lib -Ipandas/src/datetime -Ipandas/src/klib -Ipandas/src -IC:\pyth
on27\lib\site-packages\numpy\core\include -IC:\python27\include -IC:\python27\PC /Tcpandas/src/ujson/python/ujson.c /Fobuild\temp.win-amd64-2.7\Release\pandas/src/ujson/python/ujson.obj -D_GNU_SOURCE
ujson.c
C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\Bin\amd64\cl.exe /c /nologo /Ox /MD /W3 /GS- /DNDEBUG -Ipandas/src/ujson/python -Ipandas/src/ujson/lib -Ipandas/src/datetime -Ipandas/src/klib -Ipandas/src -IC:\pyth
on27\lib\site-packages\numpy\core\include -IC:\python27\include -IC:\python27\PC /Tcpandas/src/ujson/python/objToJSON.c /Fobuild\temp.win-amd64-2.7\Release\pandas/src/ujson/python/objToJSON.obj -D_GNU_SOURCE
objToJSON.c
c:\python27\lib\site-packages\numpy\core\include\numpy\npy_deprecated_api.h(8) : Warning Msg: Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
pandas/src/ujson/python/objToJSON.c(249) : warning C4267: 'function' : conversion from 'size_t' to 'int', possible loss of data
pandas/src/ujson/python/objToJSON.c(274) : error C2143: syntax error : missing ';' before 'type'
pandas/src/ujson/python/objToJSON.c(275) : error C2275: 'PyDatetimeScalarObject' : illegal use of this type as an expression
C:\python27\lib\site-packages\numpy\core\include\numpy/arrayscalars.h(123) : see declaration of 'PyDatetimeScalarObject'
pandas/src/ujson/python/objToJSON.c(275) : error C2065: 'obj' : undeclared identifier
pandas/src/ujson/python/objToJSON.c(277) : error C2065: 'obj' : undeclared identifier
pandas/src/ujson/python/objToJSON.c(277) : error C2223: left of '->obval' must point to struct/union
pandas/src/ujson/python/objToJSON.c(277) : error C2065: 'obj' : undeclared identifier
pandas/src/ujson/python/objToJSON.c(277) : error C2223: left of '->obmeta' must point to struct/union
pandas/src/ujson/python/objToJSON.c(277) : error C2065: 'dts' : undeclared identifier
pandas/src/ujson/python/objToJSON.c(277) : warning C4047: 'function' : 'npy_datetime' differs in levels of indirection from 'int *'
pandas/src/ujson/python/objToJSON.c(277) : warning C4024: 'pandas_datetime_to_datetimestruct' : different types for formal and actual parameter 1
pandas/src/ujson/python/objToJSON.c(277) : error C2198: 'pandas_datetime_to_datetimestruct' : too few arguments for call
pandas/src/ujson/python/objToJSON.c(278) : error C2065: 'dts' : undeclared identifier
pandas/src/ujson/python/objToJSON.c(278) : warning C4133: 'function' : incompatible types - from 'int *' to 'pandas_datetimestruct *'
pandas/src/ujson/python/objToJSON.c(356) : warning C4244: '=' : conversion from 'npy_intp' to 'int', possible loss of data
error: command '"C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\Bin\amd64\cl.exe"' failed with exit status 2
PS H:\jreback\pandas>
```
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} | 2 | 2013-09-06T19:08:12Z | 2014-06-22T20:42:31Z | 2013-09-06T20:07:10Z | MEMBER | null | closes #4763
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} | 0 | 2013-09-06T23:00:49Z | 2014-06-18T19:30:24Z | 2013-09-07T00:10:52Z | CONTRIBUTOR | null | related to #3777
This shows enlarging (and setting inplace)
```
In [8]: df = DataFrame(np.random.randint(5,10,size=9).reshape(3, 3),
...: ...: columns=list('abc'),
...: ...: index=[[4,4,8],[8,10,12]])
In [9]:
In [9]: df
Out[9]:
a b c
4 8 8 8 8
10 9 7 6
8 12 8 7 9
In [10]: df.loc[4,'d'] = [0,1.]
In [11]: df
Out[11]:
a b c d
4 8 8 8 8 0
10 9 7 6 1
8 12 8 7 9 NaN
In [12]: df.loc[4,'d'] = [3,4]
In [13]: df
Out[13]:
a b c d
4 8 8 8 8 3
10 9 7 6 4
8 12 8 7 9 NaN
```
Invalid assignments
```
In [10]: df.loc[4,'d'] = [3]
ValueError: cannot set using a multi-index selection indexer with a different length than the value
In [11]: df.loc[4,'d'] = [3,4,5]
ValueError: cannot set using a multi-index selection indexer with a different length than the value
```
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} | 0 | 2013-09-07T00:05:13Z | 2013-09-07T00:20:06Z | 2013-09-07T00:20:06Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/18646668/pandas-hdfstore-duplicate-items-error/18668217#18668217
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} | 0 | 2013-09-07T00:10:23Z | 2014-06-12T07:59:14Z | 2013-09-07T00:20:06Z | CONTRIBUTOR | null | closes #4767
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} | 39 | 2013-09-07T04:14:16Z | 2015-08-23T12:59:28Z | 2013-10-03T02:26:05Z | MEMBER | null | closes #4697 (refactor issue) (REF/ENH)
closes #4700 (header inconsistency issue) (API)
closes #5029 (comma issue, added this data set, ordering issue) (BUG)
closes #5048 (header type conversion issue) (BUG)
closes #5066 (index_col issue) (BUG)
- [x] figure out `skiprows`, `header`, and `index_col` interaction (a somewhat longstanding `MultiIndex` sorting issue, I just took the long way to get there :))
- ~~spam url not working anymore~~ (US gov "shutdown" is responsible for this, it correctly skips)
- ~~table ordering doc blurb/HTML gotchas~~ (was an actual "bug", now fixed in this PR)
- ~~add tests for rows with a different length~~ (this is already done by the existing tests)
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} | 0 | 2013-09-07T14:33:38Z | 2013-09-25T23:53:33Z | 2013-09-07T20:57:14Z | CONTRIBUTOR | null | Addmitedly an odd case....
```
In [5]: df = concat([DataFrame(np.random.randn(10,4),columns=['A','A','B','B']),
...: DataFrame(np.random.randint(0,10,size=20).reshape(10,2),columns=['A','C'])],
...: axis=1)
In [6]: df
Out[6]:
A A B B A C
0 -0.931048 1.068066 -0.006521 -0.031878 4 7
1 2.440559 1.279710 -0.272613 -1.387456 8 4
2 -0.220729 0.730665 -0.999338 -1.063784 1 1
3 -0.395295 -0.868506 -1.945702 1.531287 4 1
4 0.218480 -1.907875 0.354556 -0.277721 9 3
5 -0.518193 -1.292118 -0.250087 0.750773 3 8
6 1.908601 -1.736724 -1.156278 0.552929 3 7
7 -0.996528 0.081386 -1.030960 0.959769 7 1
8 0.349220 0.904122 -0.033104 -1.469096 1 4
9 0.798829 -0.882321 -1.969144 -0.237184 4 7
In [7]: concat([df,df],axis=1)
Out[7]:
A A B B A C A A B B A C
0 -0.931048 1.068066 -0.006521 -0.031878 4 7 -0.931048 1.068066 -0.006521 -0.031878 4 7
1 2.440559 1.279710 -0.272613 -1.387456 8 4 2.440559 1.279710 -0.272613 -1.387456 8 4
2 -0.220729 0.730665 -0.999338 -1.063784 1 1 -0.220729 0.730665 -0.999338 -1.063784 1 1
3 -0.395295 -0.868506 -1.945702 1.531287 4 1 -0.395295 -0.868506 -1.945702 1.531287 4 1
4 0.218480 -1.907875 0.354556 -0.277721 9 3 0.218480 -1.907875 0.354556 -0.277721 9 3
5 -0.518193 -1.292118 -0.250087 0.750773 3 8 -0.518193 -1.292118 -0.250087 0.750773 3 8
6 1.908601 -1.736724 -1.156278 0.552929 3 7 1.908601 -1.736724 -1.156278 0.552929 3 7
7 -0.996528 0.081386 -1.030960 0.959769 7 1 -0.996528 0.081386 -1.030960 0.959769 7 1
8 0.349220 0.904122 -0.033104 -1.469096 1 4 0.349220 0.904122 -0.033104 -1.469096 1 4
9 0.798829 -0.882321 -1.969144 -0.237184 4 7 0.798829 -0.882321 -1.969144 -0.237184 4 7
In [8]: concat([df,df],axis=0)
AttributeError: 'BlockManager' object has no attribute 'dtype'
```
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} | 3 | 2013-09-07T19:16:11Z | 2014-06-13T14:32:04Z | 2013-09-07T20:57:14Z | CONTRIBUTOR | null | closes #4771
TST/BUG: Bug in iloc with a slice index failing (GH4771)
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} | 18 | 2013-09-08T16:16:49Z | 2014-07-16T08:27:12Z | 2013-09-20T22:21:44Z | MEMBER | null | This is a PR with some stylistic improvements to the docstring rendering in the documentation. The incentive was that I found the reference (docstring) documentation not always clearly organised.
Things I changed (mainly copied from the new numpy/scipy docs):
- reduced space around parameter description
- italic font for parameter types
- assure that colon after "Parameters" is on same line and some grey background in Parameter field to highlight the
- "See also" box: put link and description on same line
- light background color in code examples
- line under Notes and Examples section headers
You can see the result here: http://jorisvandenbossche.github.io/example-pandas-docs/html-docstring-rendering/generated/pandas.DataFrame.apply.html#pandas.DataFrame.apply
and compare with the original: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.apply.html#pandas.DataFrame.apply
Each of the points is of course debatable and I can change some of them back. Maybe the grey background of the code cells (this also applies to the tutorial docs) and the grey highlighting of the "parameter" and "returns" field (this can alse be the full block as in eg http://docs.scipy.org/doc/numpy/reference/generated/numpy.reshape.html#numpy.reshape) is maybe the most debatable.
Style is of course always subjective :-) So what do you think?
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} | 4 | 2013-09-08T16:56:25Z | 2014-07-16T08:27:13Z | 2013-09-20T22:18:14Z | MEMBER | null | There is an error in the example of the `unstack` docstring (http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.unstack.html ):
```
>>> df = s.unstack(level=0)
>>> df
one two
a 1. 2.
b 3. 4
```
should be
```
one two
a 1. 3.
b 2. 4
```
So this PR fixes that, but in the same time I did some other adjustments. Mainly:
- added a "see also"
- added in the example the code to create the series, so one can run the example him/herself
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} | 10 | 2013-09-08T23:21:55Z | 2014-06-14T23:47:58Z | 2013-09-10T13:50:23Z | NONE | null | Included vbench shows ~12% improvement. I've seen 15-20% on movielens, 8% on FEC.
I've encountered no regressions on any dataset I tried.
Tested with gcc 4.6,4.7,4.8 on linux, results from OSX+clang would be useful:
`./test_perf.sh -H -r read_table_multiple_long` before and after.
Second VB commit is unrelated.
Will merge in a couple of days or so, if all is quiet.
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} | 0 | 2013-09-09T01:51:08Z | 2014-06-15T21:16:42Z | 2013-09-09T02:03:15Z | CONTRIBUTOR | null | closes #4550,#4548
```
In [1]: s = pd.concat([Series(list(range(3))),Series(list(range(3)))])
In [2]: s
Out[2]:
0 0
1 1
2 2
0 0
1 1
2 2
dtype: int64
In [3]: s.where(s<2)
Out[3]:
0 0
1 1
2 NaN
0 0
1 1
2 NaN
dtype: float64
In [4]: s[s<1] = 5
In [5]: s
Out[5]:
0 5
1 1
2 2
0 5
1 1
2 2
dtype: int64
In [6]: s[s<2] += 10
In [7]: s
Out[7]:
0 5
1 11
2 2
0 5
1 11
2 2
dtype: int64
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4780 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4780/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4780/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4780/events | https://github.com/pandas-dev/pandas/issues/4780 | 19,173,435 | MDU6SXNzdWUxOTE3MzQzNQ== | 4,780 | Documentation Improvement -- Typo in 10 Minutes to Pandas Tutorial? (date_range) | {
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} | [] | closed | false | null | [] | null | 2 | 2013-09-09T02:42:07Z | 2013-09-09T03:59:54Z | 2013-09-09T03:59:54Z | NONE | null | Hi all, I'm new to pandas and going through the tutorial: http://pandas.pydata.org/pandas-docs/stable/10min.html . At the top, under the Object Creation tutorial there's a date_range function, which is part of pandas so it has the pd in front of it:
In [5]: dates = pd.date_range('20130101',periods=6)
Later on, under Setting, there's another date_range function but this time without pd in front:
In [42]: s1 = pd.Series([1,2,3,4,5,6],index=date_range('20130102',periods=6))
I can't get it to run properly without adding a 'pd.' in front of the date_range, so I imagine this is a typo in the tutorial. The top cell of the image is the code as it's presented in the tutorial; the bottom cell is after adding .pd to it.
It took me a minute or two to figure this out, so figured it might confuse people if in fact this is a typo, especially those new to pandas such as myself. I'm also seeing this same problem with some of the Series (specifically, histogram). And I'm running my IPython notebook from the top of the file, each time, so it's not like I haven't already evaluated the cells above:
Histogramming
In [65]: s = Series(np.random.randint(0,7,size=10))
String Methods
In [68]: s = Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
Time Series
In [115]: ts = Series(randn(len(prng)), prng)
Other cells in Time Series above and below both use pd.Series. The error in all cases is: NameError: name 'Series' is not defined.
In Merge Concat section, concat instead of pd.concat. Here is what the tutorial gives:
In [73]: concat(pieces)
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} | 2 | 2013-09-09T03:05:55Z | 2014-06-12T07:40:42Z | 2013-09-10T11:34:51Z | CONTRIBUTOR | null | So traditionally a fillna that does the means of the columns is an apply operation
```
In [1]: df = DataFrame(np.random.randn(10,3))
In [2]: df.iloc[3:5,0] = np.nan
In [3]: df.iloc[4:6,1] = np.nan
In [4]: df.iloc[5:8,2] = np.nan
In [5]: df
Out[5]:
0 1 2
0 0.096030 0.197451 1.645981
1 -0.443437 0.359204 -0.382563
2 0.613981 1.418754 -0.589935
3 0.000000 0.449953 -0.308414
4 0.000000 0.000000 -0.471054
5 -2.350309 0.000000 0.000000
6 -0.218522 0.498207 0.000000
7 0.478238 0.399154 0.000000
8 0.895854 0.230992 0.025799
9 0.085675 2.189373 -0.946990
```
The following currently fails in 0.12 as where is finicky about how it broadcasts
```
In [4]: df.where(df>0,df[0],axis='index')
ValueError: other must be the same shape as self when an ndarray
```
Adding `axis` and `level` arguments to `where` (which uses align under the hood), now enables the `other` object to be a Series/DataFrame (as well as a scalar) without a whole bunch of alignment/broadcasting. This also should be quite a bit faster.
```
IIn [6]: df.where(df>0,df[0],axis='index')
Out[6]:
0 1 2
0 0.096030 0.197451 1.645981
1 -0.443437 0.359204 -0.443437
2 0.613981 1.418754 0.613981
3 0.000000 0.449953 0.000000
4 0.000000 0.000000 0.000000
5 -2.350309 -2.350309 -2.350309
6 -0.218522 0.498207 -0.218522
7 0.478238 0.399154 0.478238
8 0.895854 0.230992 0.025799
9 0.085675 2.189373 0.085675
```
This works in 0.12.
```
In [7]: df.apply(lambda x, y: x.where(x>0,y), y=df[0])
Out[7]:
0 1 2
0 0.096030 0.197451 1.645981
1 -0.443437 0.359204 -0.443437
2 0.613981 1.418754 0.613981
3 0.000000 0.449953 0.000000
4 0.000000 0.000000 0.000000
5 -2.350309 -2.350309 -2.350309
6 -0.218522 0.498207 -0.218522
7 0.478238 0.399154 0.478238
8 0.895854 0.230992 0.025799
9 0.085675 2.189373 0.085675
```
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} | 2 | 2013-09-09T03:29:21Z | 2016-08-01T17:35:47Z | 2016-08-01T10:17:17Z | CONTRIBUTOR | null | Any ideas on what this was supposed to be? (from 50ae5268)
``` python
def test_multiple_skts_example(self):
data = "year, month, a, b\n 2001, 01, 0.0, 10.\n 2001, 02, 1.1, 11."
pass
```
Came in a commit with release notes that said:
``` diff
diff --git a/RELEASE.rst b/RELEASE.rst
index a82e511..607be2e 100644
--- a/RELEASE.rst
+++ b/RELEASE.rst
@@ -41,6 +41,9 @@ pandas 0.8.0
- Add ``match`` function to API (#502)
- Add Cython-optimized first, last, min, max, prod functions to GroupBy (#994,
#1043)
+ - Dates can be split across multiple columns (#1227, #1186)
+ - Add experimental support for converting pandas DataFrame to R data.frame
+ via rpy2 (#350, #1212)
**Improvements to existing features**
@@ -53,6 +56,7 @@ pandas 0.8.0
- Can pass arrays in addition to column names to DataFrame.set_index (#402)
- Improve the speed of "square" reindexing of homogeneous DataFrame objects
by significant margin (#836)
+ - Handle more dtypes when passed MaskedArrays in DataFrame constructor (#406)
```
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} | 5 | 2013-09-09T03:45:05Z | 2014-06-19T08:30:04Z | 2013-09-14T01:59:59Z | CONTRIBUTOR | null | Fixes #3963, #4785
Fixed bug with reading compressed files in as `bytes` (`gzip` and `bz2` both now return `bytes` rather
than `str` in Python 3) rather than `str` in Python 3, as well as the lack of conversion of `BytesIO`. Now, `_get_handle` and `_wrap_compressed` both wrap in an `io.TextIOWrapper`, so that the parsers work internally only with `str` in Python 3. In Python 3.2, has to read the entire file in first (because `gzip` and `bz2` files both lack a `read1()` method in 3.2)
Also adds support for passing fileobjects with compression == 'bz2'.
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} | 3 | 2013-09-09T04:46:13Z | 2014-06-19T08:29:24Z | 2013-09-09T12:27:12Z | CONTRIBUTOR | null | Fixes #3963.
`gzip` and `bz2` both now return `bytes` rather than `str` in Python 3, so
need to check for bytes and decode as necessary.
replacing #4783
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} | 7 | 2013-09-09T07:10:50Z | 2016-10-12T23:04:11Z | 2013-09-14T02:00:38Z | NONE | null | ```
>>> import pandas as pd
>>> from io import BytesIO
>>> pd.read_fwf(BytesIO("שלום".encode('utf8')),widths=[2])
>>>pandas/io/parsers.py", line 1944, in <listcomp>
>>> for (fromm, to) in self.colspecs]
>>>TypeError: Type str doesn't support the buffer API
```
By another path:
```
>>> from io import BytesIO
>>> pd.read_table(BytesIO("שלום::1234\n".encode('cp1255')),sep="::", engine='python', encoding='cp1255')
File "/usr/local/lib/python3.3/dist-packages/pandas-0.12.0_357_g218f334-py3.3-linux-x86_64.egg/pandas/io/parsers.py", line 1324, in _read
yield pat.split(line.decode('utf-8').strip())
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xf9 in position 0: invalid start byte
```
is broken. Note that len(sep)>1 activates the python engine anyway right now.
related #4784
Edit: fixed incorrect encoding and updated error
Edit: Updated examples
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} | 21 | 2013-09-09T14:36:17Z | 2016-10-12T23:04:11Z | 2014-01-24T16:13:33Z | CONTRIBUTOR | null | Is this a local problem? I'm on g41d10b5. The following url appears never to return, or at least it's taking an inordinately long time.
http://www.nku.edu/~longa/geomed/ppa/doc/globals/Globals.htm
Same result with either the url or the raw html.
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} | 3 | 2013-09-09T16:08:18Z | 2020-01-01T16:02:29Z | 2020-01-01T15:52:33Z | CONTRIBUTOR | null | for:
`min,max,sum,var,std,kurt,median,mad,prod,skew`
The big reason to do this is a performance hit when you happen to have an object column. and you don't realize it.
See: http://stackoverflow.com/questions/18701569/pandas-dataframe-mean-very-slow-how-can-i-calculate-means-of-columns-faster
`_reduce` in Frame already supports this (and Series will ignore it), so should be trvial to implement
alternatively could just pass `**kwds` thru (I think we had an issue about this earlier)
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} | 24 | 2013-09-09T20:36:09Z | 2013-09-10T14:04:41Z | 2013-09-10T13:05:46Z | CONTRIBUTOR | null | I'm in a bit of a pickle here. If I try to save and read back a multi-indexed dataframe, I get this error (and in some situations, can't reproduce when, I get a `TypeError: Required argument 'shape' (pos 1) not found` error).
The gist with the full traceback is [here](https://gist.github.com/d10genes/6500977).
```
In [3]: import numpy as np
In [4]: import pandas as pd
In [5]: np.random.seed(10)
In [6]: a = np.random.randint(0, 20, (6, 5))
In [7]: df = pd.DataFrame(a).set_index([2,3,4])
In [8]: df
Out[8]:
0 1
2 3 4
15 0 17 9 4
8 9 0 16 17
4 19 16 10 8
11 11 1 4 15
14 17 19 8 4
13 19 13 13 5
In [9]: df.to_pickle('~/Desktop/dummy.df')
In [10]: df2 = pd.read_pickle('~/Desktop/dummy.df')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
...
TypeError: 'FrozenList' does not support mutable operations.
```
When I `reset_index` it saves fine. I'm on Mac OSX, numpy 1.7, pandas 0.12.0-361-g53eec08.
In the meantime, if there's no quick fix, anyone know another way to save multi-indexes to file? csv doesn't look like it can preserve them. I could do something like appending `_index` to the columns and un-rename them after reading but would prefer something less hacky.
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} | 13 | 2013-09-09T20:45:14Z | 2016-10-12T23:04:11Z | 2013-09-14T20:31:47Z | CONTRIBUTOR | null | ```
======================================================================
FAIL: test_bar_log (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/tests/test_graphics.py", line 568, in test_bar_log
self.assertEqual(ax.yaxis.get_ticklocs()[0], 1.0)
AssertionError: 0.10000000000000001 != 1.0
----------------------------------------------------------------------
Ran 1 test in 0.341s
```
needs to be fixed up for debian where mpl now 1.3.0
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} | 10 | 2013-09-09T21:03:57Z | 2013-10-14T18:18:38Z | 2013-10-14T18:18:38Z | MEMBER | null | The docstring of `DataFrame()` is not present in the documentation (so no 'generated/pandas.DataFrame.html'), because it is not included in the API reference (http://pandas.pydata.org/pandas-docs/dev/api.html).
But there is one for `DataFrame.__init__()` (http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.__init__.html#pandas.DataFrame.__init__), which seems not that usefull.
Do I replace the init version with DataFrame itself, or should I just add a line for DataFrame in api.rst?
The same applies for Series and Panel.
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} | 34 | 2013-09-09T23:04:09Z | 2014-06-26T18:22:27Z | 2013-09-10T13:05:46Z | CONTRIBUTOR | null | closes #4788
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} | 2 | 2013-09-09T23:28:48Z | 2017-02-10T00:19:24Z | 2017-02-10T00:19:23Z | CONTRIBUTOR | null | I think expected result of this is with multiindex column...
```
df = pd.DataFrame({'PRICE': {pd.Timestamp('2011-01-06 10:59:05', tz=None): 24990,
pd.Timestamp('2011-01-06 12:43:33', tz=None): 25499,
pd.Timestamp('2011-01-06 12:54:09', tz=None): 25499},
'VOLUME': {pd.Timestamp('2011-01-06 10:59:05', tz=None): 1500000000,
pd.Timestamp('2011-01-06 12:43:33', tz=None): 5000000000,
pd.Timestamp('2011-01-06 12:54:09', tz=None): 100000000}})
In [2]: g = df.groupby('PRICE')
In [3]: df.groupby('PRICE').describe() # expected .unstack(1)
Out[3]:
PRICE VOLUME
PRICE
24990 count 1 1.000000e+00
mean 24990 1.500000e+09
std NaN NaN
min 24990 1.500000e+09
25% 24990 1.500000e+09
50% 24990 1.500000e+09
75% 24990 1.500000e+09
max 24990 1.500000e+09
25499 count 2 2.000000e+00
mean 25499 2.550000e+09
std 0 3.464823e+09
min 25499 1.000000e+08
25% 25499 1.325000e+09
50% 25499 2.550000e+09
75% 25499 3.775000e+09
max 25499 5.000000e+09
```
cc #4740 @jreback
I guess it just applies method/attribute from df:
```
In [4]: pd.DataFrame.foo = 1
In [5]: g.foo
Out[5]:
PRICE
24990 1
25499 1
dtype: int64
In [6]: pd.DataFrame.bar = lambda x: 1
In [7]: g.bar()
Out[7]:
PRICE
24990 1
25499 1
dtype: int64
```
should we just special case describe? :s
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} | 27 | 2013-09-09T23:37:06Z | 2020-05-22T12:35:10Z | 2016-08-05T10:35:50Z | NONE | null | I am using Pandas version 0.12.0 on a Mac.
I noticed that when there is a BOM utf-8 file, and if the header row is in the first line, the read_csv() method will leave a leading quotation mark in the first column's name. However, if the header row is further down the file and I use the "header=" option, then the whole header row gets parsed correctly.
Here is an example code:
bing_kw = pd.read_csv('../../data/sem/Bing-Keyword_daily.csv', header=9, thousands=',', encoding='utf-8')
Parses the header correctly.
bing_kw = pd.read_csv('../../data/sem/Bing-Keyword_daily.csv', thousands=',', encoding='utf-8')
Parses the first header column name incorrectly by leaving the leading quotation mark.
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} | 6 | 2013-09-10T00:31:05Z | 2013-10-14T01:43:01Z | 2013-10-14T01:43:01Z | CONTRIBUTOR | null | related #4687
Bad errors and/or not checking:
```
>>> ind = tm.makeCustomIndex(5, 5)
>>> ind.set_levels([[1, 2]]) # Wrong Error
Traceback
...
ValueError: Length of names (5) must be same as level (1)
>>> ind = tm.makeCustomIndex(5, 5)
>>> ind.set_levels([[1, 2]], inplace=True) # Wrong Error
Traceback
...
ValueError: Length of names (5) must be same as level (1)
>>> ind = tm.makeCustomIndex(5, 5)
>>> ind.levels = [[1, 2]] # Wrong Error
Traceback
...
ValueError: Length of names (5) must be same as level (1)
```
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} | 2 | 2013-09-10T02:59:40Z | 2013-09-10T11:42:07Z | 2013-09-10T11:42:07Z | CONTRIBUTOR | null | @jreback - `Timestamp` isn't actually defined/imported in core/common. Did you mean `lib.Timestamp`?
``` python
# snippet from core/common.py
def _coerce_to_dtypes(result, dtypes):
""" given a dtypes and a result set, coerce the result elements to the dtypes """
if len(result) != len(dtypes):
raise AssertionError("_coerce_to_dtypes requires equal len arrays")
def conv(r,dtype):
try:
if isnull(r):
pass
elif dtype == _NS_DTYPE:
r = Timestamp(r)
elif dtype == _TD_DTYPE:
r = _coerce_scalar_to_timedelta_type(r)
elif dtype == np.bool_:
r = bool(r)
elif dtype.kind == 'f':
r = float(r)
elif dtype.kind == 'i':
r = int(r)
except:
pass
return r
return np.array([ conv(r,dtype) for r, dtype in zip(result,dtypes) ])
```
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} | 2 | 2013-09-10T13:48:59Z | 2013-09-15T13:30:45Z | 2013-09-15T13:30:45Z | NONE | null | I am trying to write some Python code to download historical data, similar to the yahoo data reader.
The sites I am interested in are:
http://www.henderson.com/sites/henderson/uk_pi/fundcentre/historicprices.aspx
http://www.mandg.co.uk/investor/funds/prices/
I cannot work out how to submit the Javascript forms on either site successfully, though I have managed to write download scripts for other similar sites.
Is this something someone could knock up quickly, or give me some pointers on how to do it myself?
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