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https://api.github.com/repos/pandas-dev/pandas/issues/3601 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3601/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3601/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3601/events | https://github.com/pandas-dev/pandas/issues/3601 | 14,315,331 | MDU6SXNzdWUxNDMxNTMzMQ== | 3,601 | Plotting incorrectly reversed when using dates as x-axis | {
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} | 5 | "2013-05-14T15:25:35Z" | "2013-05-19T17:30:26Z" | "2013-05-19T17:30:26Z" | CONTRIBUTOR | null | Sometimes I have to read in spreadsheets containing data with the most
recent observations first. If I try to plot these without re-sorting
first, the graph appears _backwards_. The dates on the x-axis are in
ascending order, but the data are plotted from left to right in the same
order they appeared in the spreadsheet. Since there's no warning that the
labels on the axis don't correspond to the data being plotted, this can
cause some embarrassment! This only happens when I use timestamps; dates
as integers work fine.
For example, this works fine (dates reversed, stored as int)
df2 = DataFrame({'Date' : [1998, 1997, 1996], 'Data' : [9, 8,
7]}).set_index('Date')
df2.plot()
This doesn't (same data set, but dates stored as timestamp instead):
from dateutil import parser
df = DataFrame({'Date' : ['1998-01-01', '1997-01-01', '1996-01-01'],
'Data' : [9, 8, 7]})
df.index = df.Date.map(parser.parse)
del df['Date']
df.plot()
I can get around the problem by sorting the data set before plotting, but
I don't always remember, and it definitely seems odd that the graph ends
up displaying something that is not actually in the original data set...
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} | 20 | "2013-05-14T15:28:02Z" | "2013-05-19T15:12:02Z" | "2013-05-19T15:12:02Z" | CONTRIBUTOR | null | Under certain circumstances, concat seems to produce erroneous results. I haven't worked out what causes the problems to arise, but here's an example:
df1 = DataFrame({'firmNo' : [0,0,0,0], 'stringvar' : ['rrr', 'rrr', 'rrr', 'rrr'], 'prc' : [6,6,6,6] })
df2 = DataFrame({'misc' : [1,2,3,4], 'prc' : [6,6,6,6], 'C' : [9,10,11,12]})
concat([df1,df2],axis=1)
produces as output:
firmNo prc stringvar C misc prc
0 rrr 0 6 9 1 6
1 rrr 0 6 10 2 6
2 rrr 0 6 11 3 6
3 rrr 0 6 12 4 6
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} | 8 | "2013-05-14T19:41:21Z" | "2014-06-27T14:18:49Z" | "2013-05-19T21:12:04Z" | CONTRIBUTOR | null | Make categorical a little more user-friendly and add some documentation.
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"description": "RC1 ANN went out on 2013-12-6: https://groups.google.com/forum/#!topic/pydata/OHutLByJvK0\r\nTwo weeks, clear all reported bugs then call it on 2013-12-20. \r\n(@y-p)\r\n\r\nsince we've had some bugs reported at a high enough rate, extending rc by one\r\nweek until 2013-12-27. \r\n(@y-p)\r\n\r\nBumping to clear debian sparc\r\n2013-12-30. \r\n(@y-p)",
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} | 0 | "2013-05-14T21:17:09Z" | "2013-09-10T13:07:33Z" | "2013-09-10T13:07:33Z" | CONTRIBUTOR | null | fair warning in 0.11.1, see #3575
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} | 6 | "2013-05-14T22:05:35Z" | "2013-05-20T11:45:59Z" | "2013-05-20T11:45:59Z" | CONTRIBUTOR | null | I just got a bunch of test errors because I don't have bs4. I do have lxml. Haven't looked at this at all to see why the test isn't conditional. E.g.,
```
======================================================================
ERROR: test_string (pandas.io.tests.test_html.TestBs4ReadHtml)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/skipper/src/pandas-skipper/pandas/io/tests/test_html.py", line 221, in test_string
df1 = self.run_read_html(data, '.*Water.*', infer_types=False)
File "/home/skipper/src/pandas-skipper/pandas/io/tests/test_html.py", line 324, in run_read_html
return _run_read_html(*args, **kwargs)
File "/home/skipper/src/pandas-skipper/pandas/io/tests/test_html.py", line 31, in _run_read_html
return read_html(*args, **kwargs)
File "/home/skipper/src/pandas-skipper/pandas/io/html.py", line 701, in read_html
for data in p.parse_raw_data()]
File "/home/skipper/src/pandas-skipper/pandas/io/html.py", line 152, in parse_raw_data
for rows in self.parse_rows()]
File "/home/skipper/src/pandas-skipper/pandas/io/html.py", line 137, in parse_rows
tables = self._parse_tables(self._build_doc(), self.match, self.attrs)
File "/home/skipper/src/pandas-skipper/pandas/io/html.py", line 333, in _build_doc
from bs4 import BeautifulSoup, SoupStrainer
ImportError: No module named bs4
```
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} | 10 | "2013-05-14T22:27:22Z" | "2013-08-06T23:30:09Z" | "2013-05-20T11:47:17Z" | CONTRIBUTOR | null | The second column of the table
http://code.google.com/p/pythonxy/wiki/StandardPlugins#Python_packages
is not parsed as shown with this code:
``` python
# -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>
# <codecell>
import pandas as pd
# <codecell>
url = 'http://code.google.com/p/pythonxy/wiki/StandardPlugins'
# <codecell>
dfs = pd.read_html(url, attrs={'class': 'wikitable'})
# <codecell>
dfs
# <codecell>
dfs = pd.read_html(url, flavor='lxml', attrs={'class': 'wikitable'})
# <codecell>
dfs
# <codecell>
python_core = dfs[0]
# <codecell>
python_core[:10]
```
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} | 2 | "2013-05-14T22:51:47Z" | "2014-06-25T21:04:16Z" | "2013-05-15T22:18:50Z" | MEMBER | null | closes #3605
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} | 4 | "2013-05-15T12:11:47Z" | "2013-05-23T14:03:36Z" | "2013-05-19T19:09:38Z" | NONE | null | version: 0.11.0
with small variation of python for data analysis book:
``` python
tips = pd.read_csv("https://github.com/pydata/pydata-book/raw/master/ch08/tips.csv")
grouped = tips.groupby(['sex','smoker'])
res = grouped.agg({'tip': ['min','max','mean','std'], 'size':'sum'}) #By now, copied from the book, ~page 263-264
#New stuff
res["foo"] = res["tip", "max"] * res["tip", "mean"]
```
The result attached (with missing line. it seems like 'sum' is part of 'foo')
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} | 13 | "2013-05-15T12:44:42Z" | "2016-10-12T23:03:48Z" | "2013-11-06T00:23:40Z" | CONTRIBUTOR | null | Should raise that `kind='period'` is not accepted for `DatetimeIndex` when resampling
Possible issue with period index resampling hanging (see @cpcloud example below)
version = 0.12.0.dev-f61d7e3
This bug also exists in 0.11.
## The bug
``` Python
In [20]: s.resample('T', kind='period')
-----------------
AssertionError
Traceback (most recent call last)
<ipython-input-79-c290c0578332> in <module>()
----> 1 s.resample('T', kind='period')
/home/dk3810/workspace/python/pda/scripts/src/pandas/pandas/core/generic.py in resample(self, rule, how, axis, fill_method, closed, label, convention, kind, loffset, limit, base)
255 fill_method=fill_method, convention=convention,
256 limit=limit, base=base)
--> 257 return sampler.resample(self)
258
259 def first(self, offset):
/home/dk3810/workspace/python/pda/scripts/src/pandas/pandas/tseries/resample.py in resample(self, obj)
81
82 if isinstance(axis, DatetimeIndex):
---> 83 rs = self._resample_timestamps(obj)
84 elif isinstance(axis, PeriodIndex):
85 offset = to_offset(self.freq)
/home/dk3810/workspace/python/pda/scripts/src/pandas/pandas/tseries/resample.py in _resample_timestamps(self, obj)
224 # Irregular data, have to use groupby
225 grouped = obj.groupby(grouper, axis=self.axis)
--> 226 result = grouped.aggregate(self._agg_method)
227
228 if self.fill_method is not None:
/home/dk3810/workspace/python/pda/scripts/src/pandas/pandas/core/groupby.py in aggregate(self, func_or_funcs, *args, **kwargs)
1410 if isinstance(func_or_funcs, basestring):
-> 1411 return getattr(self, func_or_funcs)(*args, **kwargs)
1412
1413 if hasattr(func_or_funcs, '__iter__'):
/home/dk3810/workspace/python/pda/scripts/src/pandas/pandas/core/groupby.py in mean(self)
356 except Exception: # pragma: no cover
357 f = lambda x: x.mean(axis=self.axis)
--> 358 return self._python_agg_general(f)
359
360 def median(self):
/home/dk3810/workspace/python/pda/scripts/src/pandas/pandas/core/groupby.py in _python_agg_general(self, func, *args, **kwargs)
498 output[name] = self._try_cast(values[mask],result)
499
--> 500 return self._wrap_aggregated_output(output)
501
502 def _wrap_applied_output(self, *args, **kwargs):
/home/dk3810/workspace/python/pda/scripts/src/pandas/pandas/core/groupby.py in _wrap_aggregated_output(self, output, names)
1473 return DataFrame(output, index=index, columns=names)
1474 else:
-> 1475 return Series(output, index=index, name=self.name)
1476
1477 def _wrap_applied_output(self, keys, values, not_indexed_same=False):
/home/dk3810/workspace/python/pda/scripts/src/pandas/pandas/core/series.py in __new__(cls, data, index, dtype, name, copy)
494 else:
495 subarr = subarr.view(Series)
--> 496 subarr.index = index
497 subarr.name = name
498
/home/dk3810/workspace/python/pda/scripts/src/pandas/pandas/lib.so in pandas.lib.SeriesIndex.__set__ (pandas/lib.c:29775)()
AssertionError: Index length did not match values
```
## A workaround / expected behaviour
``` Python
In [81]: s.resample('T').to_period()
Out[81]:
2013-04-12 19:15 325.000000
2013-04-12 19:16 326.899994
...
2013-04-12 22:58 305.600006
2013-04-12 22:59 320.444458
Freq: T, Length: 225, dtype: float32
```
## More information
``` Python
In [83]: s
Out[83]:
2013-04-12 19:15:25 323
2013-04-12 19:15:28 NaN
...
2013-04-12 22:59:55 319
2013-04-12 22:59:56 NaN
2013-04-12 22:59:57 NaN
2013-04-12 22:59:58 NaN
2013-04-12 22:59:59 NaN
Name: aggregate, Length: 13034, dtype: float32
In [76]: s.index
Out[76]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-04-12 19:15:25, ..., 2013-04-12 22:59:59]
Length: 13034, Freq: None, Timezone: None
In [77]: s.head()
Out[77]:
2013-04-12 19:15:25 323
2013-04-12 19:15:28 NaN
2013-04-12 19:15:29 NaN
2013-04-12 19:15:30 NaN
2013-04-12 19:15:31 327
Name: aggregate, dtype: float32
In [78]: s.resample('T')
Out[78]:
2013-04-12 19:15:00 325.000000
2013-04-12 19:16:00 326.899994
...
2013-04-12 22:58:00 305.600006
2013-04-12 22:59:00 320.444458
Freq: T, Length: 225, dtype: float32
In [80]: pd.__version__
Out[80]: '0.12.0.dev-f61d7e3'
In [84]: type(s)
Out[84]: pandas.core.series.TimeSeries
```
(Please let me know if you need more info! I'm using Ubuntu 13.04. It's entirely possible that this isn't a bug but instead I am doing something stupid. Oh, and let me take this opportunity to thank the Pandas dev team! Pandas is awesome!!! THANK YOU!)
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} | 3 | "2013-05-15T15:44:38Z" | "2013-05-15T19:28:17Z" | "2013-05-15T19:27:59Z" | NONE | null | ``` 'python
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({"c1" : [1,2,6,6,8]})
In [3]: df["c2"] = df.c1/2.0
In [4]: df.groupby("c2").mean().reset_index().c2
Out[4]:
0 0.5
1 1.0
2 3.0
3 4.0
Name: c2, dtype: float64
In [5]: df.groupby("c2", as_index=False).mean().c2
Out[5]:
0 0.5
1 1
2 3
3 4
Name: c2, dtype: object
```
Note the difference in dtype between the two cases.
I think the 2nd case shouldn't be different then the 1st case.
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https://api.github.com/repos/pandas-dev/pandas/issues/3611 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3611/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3611/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3611/events | https://github.com/pandas-dev/pandas/issues/3611 | 14,365,206 | MDU6SXNzdWUxNDM2NTIwNg== | 3,611 | Odd behavior from read_csv with na_values set to non-string values | {
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} | 34 | "2013-05-15T15:47:37Z" | "2013-06-11T17:43:23Z" | "2013-06-11T17:43:23Z" | CONTRIBUTOR | null | read_csv behaves oddly when na_values is set to non-string values. Sometimes
it correctly replaces the assigned number with NaN, and sometimes it doesn't. Here are some examples. Note in particular the different behavior of the last two statements:
# Create file
df = DataFrame({'A' : [-999, 2, 3], 'B' : [1.2, -999, 4.5]})
df.to_csv('test2.csv', sep=' ', index=False)
print read_csv('test2.csv', sep= ' ', header=0, na_values=[-999])
---
A B
0 NaN 1.2
1 2 -999.0
## 2 3 4.5
print read_csv('test2.csv', sep= ' ', header=0, na_values=[-999.0])
---
A B
0 -999 1.2
1 2 NaN
## 2 3 4.5
print read_csv('test2.csv', sep= ' ', header=0, na_values=[-999.0,-999])
---
A B
0 -999 1.2
1 2 NaN
## 2 3 4.5
print read_csv('test2.csv', sep= ' ', header=0, na_values=[-999,-999.0])
---
A B
0 NaN 1.2
1 2 -999.0
## 2 3 4.5
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} | 14 | "2013-05-15T18:13:32Z" | "2013-10-09T19:11:19Z" | "2013-10-09T19:10:20Z" | MEMBER | null | See #3609. Copied (more or less) from there
``` python
dind = period_range('1/1/2001', '1/1/2002').to_timestamp()
s = Series(randn(dind.size), dind)
s.resample('T', kind='period') # hangs here
```
Doesn't hang (throws an error) for the simple case of
``` python
dind = period_range('1/1/2001', '1/2/2001').to_timestamp()
s = Series(randn(dind.size), dind)
s.resample('T', kind='period')
```
and starts to hang for `dind.size > 2`.
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https://api.github.com/repos/pandas-dev/pandas/issues/3613 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3613/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3613/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3613/events | https://github.com/pandas-dev/pandas/pull/3613 | 14,373,589 | MDExOlB1bGxSZXF1ZXN0NTczNDc4Mw== | 3,613 | BUG: Fix incorrect dtype on groupby with ``as_index=False`` (GH3610_) | {
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} | 0 | "2013-05-15T18:57:28Z" | "2014-06-12T13:38:01Z" | "2013-05-15T19:27:59Z" | CONTRIBUTOR | null | closes #3610
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https://api.github.com/repos/pandas-dev/pandas/issues/3614 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3614/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3614/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3614/events | https://github.com/pandas-dev/pandas/pull/3614 | 14,378,969 | MDExOlB1bGxSZXF1ZXN0NTczNzkwNg== | 3,614 | ENH add date to DatetimeIndex | {
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} | 2 | "2013-05-15T21:00:01Z" | "2014-06-26T11:26:58Z" | "2013-05-17T16:00:23Z" | CONTRIBUTOR | null | Allows the date to be pulled out from a DatetimeIndex easily/efficiently.
Similar to #3180, which was for the time-part to be extracted.
_From SO question: http://stackoverflow.com/questions/16563552/pandas-fancy-indexing-a-dataframe_.
~~I haven't added this to release notes, as maybe too late for 11.1?~~
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https://api.github.com/repos/pandas-dev/pandas/issues/3615 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3615/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3615/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3615/events | https://github.com/pandas-dev/pandas/pull/3615 | 14,382,207 | MDExOlB1bGxSZXF1ZXN0NTczOTc0OA== | 3,615 | BUG: (GH3611) Fix read_csv to correctly encode identical na_values | {
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} | 0 | "2013-05-15T22:16:46Z" | "2014-06-27T14:18:58Z" | "2013-05-16T01:01:21Z" | CONTRIBUTOR | null | eg. na_values=[-999.0,-999] was failing
closes #3611
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https://api.github.com/repos/pandas-dev/pandas/issues/3616 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3616/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3616/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3616/events | https://github.com/pandas-dev/pandas/pull/3616 | 14,382,221 | MDExOlB1bGxSZXF1ZXN0NTczOTc1NQ== | 3,616 | ENH: read-html fixes | {
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} | 20 | "2013-05-15T22:17:07Z" | "2014-06-18T01:06:50Z" | "2013-05-20T11:43:11Z" | MEMBER | null | Some updates and bug fixes. See release notes for more details.
- ~~`vbench` stuff~~ sort of pointless right now since we don't really have control over the speed of the parsing library
- ~~Figure out why `lxml` chooses to ignore things~~ reported a bug w/ example to lxml people
- ~~Figure out why `bs4`'s `thead.find_all(['th', 'td'])` parses differently than `lxml`'s `thead.xpath('.//thead//th|.//thead//td')` even when `lxml` is the `bs4` backend.~~ same as above
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} | 2 | "2013-05-15T22:39:10Z" | "2013-05-16T11:35:05Z" | "2013-05-16T11:35:05Z" | NONE | null | Panel.iloc[] doesn't index correctly given a mix of scalars and ranges
```
from pandas import Panel, date_range
from numpy.random import randn
p = Panel(randn(4, 4, 4))
print(p.iloc[:3, :3, :3].shape)
print(p.iloc[1, :3, :3].shape)
print(p.iloc[:3, 1, :3].shape)
print(p.iloc[:3, :3, 1].shape)
print(p.iloc[1, 1, :3].shape)
print(p.iloc[1, :3, 1].shape)
print(p.iloc[:3, 1, 1].shape)
```
Returns
```
[3, 3, 3]
(4, 3)
(4, 3)
(3, 3)
(3L,)
(4L,)
(4L,)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3618 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3618/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3618/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3618/events | https://github.com/pandas-dev/pandas/pull/3618 | 14,386,679 | MDExOlB1bGxSZXF1ZXN0NTc0MjM4NQ== | 3,618 | BUG: (GH3617) Fix indexing issue with ndim >= 3 with iloc | {
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} | 0 | "2013-05-16T00:57:42Z" | "2014-06-30T17:14:22Z" | "2013-05-16T11:35:05Z" | CONTRIBUTOR | null | closes #3617
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https://api.github.com/repos/pandas-dev/pandas/issues/3619 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3619/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3619/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3619/events | https://github.com/pandas-dev/pandas/pull/3619 | 14,388,574 | MDExOlB1bGxSZXF1ZXN0NTc0MzQyNw== | 3,619 | BUG: Reindex data if plotting time/period index (GH3601) | {
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} | 4 | "2013-05-16T02:38:54Z" | "2014-07-16T08:09:21Z" | "2013-05-17T23:58:15Z" | CONTRIBUTOR | null | Just wanted to push this fix to see if it looked OK and let the Travis build start.
If it looks ok, i will add something in the release notes and try to write a proper test.
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https://api.github.com/repos/pandas-dev/pandas/issues/3620 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3620/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3620/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3620/events | https://github.com/pandas-dev/pandas/pull/3620 | 14,402,545 | MDExOlB1bGxSZXF1ZXN0NTc1MDYwNw== | 3,620 | BUG: fixed platform int issues on 32-bit | {
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|
https://api.github.com/repos/pandas-dev/pandas/issues/3621 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3621/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3621/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3621/events | https://github.com/pandas-dev/pandas/pull/3621 | 14,404,463 | MDExOlB1bGxSZXF1ZXN0NTc1MTY1Mg== | 3,621 | BUG: (GH3062) Correctly parse date columns with embedded (nan/NaT) into datetime64[ns] in read_csv | {
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} | 0 | "2013-05-16T13:00:16Z" | "2014-07-16T08:09:24Z" | "2013-05-16T13:38:33Z" | CONTRIBUTOR | null | closes #3062
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https://api.github.com/repos/pandas-dev/pandas/issues/3622 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3622/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3622/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3622/events | https://github.com/pandas-dev/pandas/issues/3622 | 14,405,554 | MDU6SXNzdWUxNDQwNTU1NA== | 3,622 | PerformanceWarning: what is actually the problem I can change? | {
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} | [] | closed | false | null | [] | null | 14 | "2013-05-16T13:27:47Z" | "2019-05-18T04:58:36Z" | "2013-05-21T11:39:12Z" | CONTRIBUTOR | null | I get several PerformanceWarnings when I store my dataframe in a hdfstore:
```
C:\portabel\Python27\lib\site-packages\pandas\io\pytables.py:1788: PerformanceWarning:
your performance may suffer as PyTables will pickle object types that it cannot map
directly to c-types [inferred_type->mixed,key->axis0]
warnings.warn(ws, PerformanceWarning)
C:\portabel\Python27\lib\site-packages\pandas\io\pytables.py:1788: PerformanceWarning:
your performance may suffer as PyTables will pickle object types that it cannot map
directly to c-types [inferred_type->mixed,key->block0_values]
warnings.warn(ws, PerformanceWarning)
C:\portabel\Python27\lib\site-packages\pandas\io\pytables.py:1788: PerformanceWarning:
your performance may suffer as PyTables will pickle object types that it cannot map
directly to c-types [inferred_type->unicode,key->block0_items]
```
What I can't get from this is what column gives me these problems, at least I don't have any "block0" columns :-) It would be nice if this warnings can give me an indicator what i can actually do about this warnings.
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https://api.github.com/repos/pandas-dev/pandas/issues/3623 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3623/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3623/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3623/events | https://github.com/pandas-dev/pandas/pull/3623 | 14,408,458 | MDExOlB1bGxSZXF1ZXN0NTc1MzkwNw== | 3,623 | DOC: more informative PerformanceWarning in HDFStore | {
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} | 7 | "2013-05-16T16:50:31Z" | "2013-05-16T18:44:33Z" | "2013-05-16T17:38:59Z" | CONTRIBUTOR | null | In [7]: pd.**version**
Out[7]: '0.12.0.dev-a026561'
In [8]: import pandas as pd
In [9]: df = pd.DataFrame({'c1':[1,2,3], 'c2':[4,5,6]})
In [10]: df.to_csv('f1.txt', index=False)
In [11]: df['c3'] = [7,8,9]
## In [12]: df.to_csv('f2.txt', index=False)
KeyError Traceback (most recent call last)
<ipython-input-12-b4861447b824> in <module>()
----> 1 df.to_csv('f2.txt', index=False)
/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/pandas-0.12.0.dev_a026561-py3.3-macosx-10.6-intel.egg/pandas/core/frame.py in to_csv(self, path_or_buf, sep, na_rep, float_format, cols, header, index, index_label, mode, nanRep, encoding, quoting, line_terminator, chunksize, **kwds)
1445 index_label=index_label,mode=mode,
1446 chunksize=chunksize,engine=kwds.get("engine") )
-> 1447 formatter.save()
1448
1449 def to_excel(self, excel_writer, sheet_name='sheet1', na_rep='',
/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/pandas-0.12.0.dev_a026561-py3.3-macosx-10.6-intel.egg/pandas/core/format.py in save(self)
945
946 else:
--> 947 self._save()
948
949
/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/pandas-0.12.0.dev_a026561-py3.3-macosx-10.6-intel.egg/pandas/core/format.py in _save(self)
1017 break
1018
-> 1019 self._save_chunk(start_i, end_i)
1020
1021 def _save_chunk(self, start_i, end_i):
/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/pandas-0.12.0.dev_a026561-py3.3-macosx-10.6-intel.egg/pandas/core/format.py in _save_chunk(self, start_i, end_i)
1031
1032 # self.data is a preallocated list
-> 1033 self.data[self.column_map[b][i]] = d[i]
1034
1035 ix = data_index.to_native_types(slicer=slicer, na_rep=self.na_rep, float_format=self.float_format)
KeyError: IntBlock: [c3], 1 x 3, dtype int64
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https://api.github.com/repos/pandas-dev/pandas/issues/3625 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3625/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3625/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3625/events | https://github.com/pandas-dev/pandas/pull/3625 | 14,416,538 | MDExOlB1bGxSZXF1ZXN0NTc1ODM3OQ== | 3,625 | BUG: Fix not consolidating before to_csv (GH3624_) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3626 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3626/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3626/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3626/events | https://github.com/pandas-dev/pandas/issues/3626 | 14,422,011 | MDU6SXNzdWUxNDQyMjAxMQ== | 3,626 | Assigning dataframe by specifying both row/col doesn't handle nan correctly | {
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} | [] | closed | false | null | [] | null | 10 | "2013-05-16T19:17:21Z" | "2013-05-17T16:42:39Z" | "2013-05-17T16:19:08Z" | NONE | null | If I run the following code under pandas 0.11, I will get different results from the 2 identical assign statment at the bottom:
```
import numpy as np
import pandas as pd
if __name__ == '__main__':
df = pd.DataFrame({'FC':['a','b','a','b','a','b'],
'PF':[0,0,0,0,1,1],
'col1':np.random.rand(6),
'col2':np.random.rand(6)});
df.ix[1,0]=np.nan
mask=~df.FC.isnull()
cols=['col1', 'col2']
dft = df * 2
dft.ix[3,3] = np.nan
# bug?
df.ix[mask, cols]= dft.ix[mask, cols]
print df
df.ix[mask, cols]= dft.ix[mask, cols]
print df
```
Notice in the second result, the NaN at [3,3] disappeared and all values below it got shifted up.
```
FC PF col1 col2
0 a 0 0.679388 0.508501
1 NaN 0 0.168159 0.346730
2 a 0 1.802611 0.870135
3 b 0 0.577989 NaN
4 a 1 1.027070 0.530890
5 b 1 0.383662 1.855584
FC PF col1 col2
0 a 0 0.679388 0.508501
1 NaN 0 0.168159 0.346730
2 a 0 1.802611 0.870135
3 b 0 0.577989 0.530890
4 a 1 1.027070 1.855584
5 b 1 0.383662 0.508501
```
The issue seems to be in pandas index.py line 143:
v = v.reindex(self.obj[item].reindex(v.index).dropna().index)
notice it's dropping NA from the target.
I then tried to use df.ix[mask, cols]= dft.ix[mask, cols].values to bypass this, and it failed also. The problem is in pandas index.py line 149:
if len(labels) != len(value):
notice it's comparing number of columns to be assigned against number of rows in ndarray.
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https://api.github.com/repos/pandas-dev/pandas/issues/3627 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3627/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3627/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3627/events | https://github.com/pandas-dev/pandas/pull/3627 | 14,426,721 | MDExOlB1bGxSZXF1ZXN0NTc2NDI1Mw== | 3,627 | BUG: (GH3588) fix pivoting with nan in the index | {
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} | 1 | "2013-05-16T21:04:18Z" | "2014-06-14T22:46:49Z" | "2013-05-19T16:55:29Z" | CONTRIBUTOR | null | closed #3588
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https://api.github.com/repos/pandas-dev/pandas/issues/3628 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3628/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3628/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3628/events | https://github.com/pandas-dev/pandas/issues/3628 | 14,428,577 | MDU6SXNzdWUxNDQyODU3Nw== | 3,628 | systematic bug in pandas/io/sql.py when trying to write a dataframe of one column of integers into sqlite | {
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} | 22 | "2013-05-16T21:50:30Z" | "2013-07-13T00:18:20Z" | "2013-05-19T16:49:55Z" | CONTRIBUTOR | null | Hello,
trying this code with pandasql, with ipython, I get a bogus result .
import pandas as pd
%load https://raw.github.com/yhat/pandasql/master/pandasql/sqldf.py
df=pd.DataFrame([ 1, 2], columns=['f'])
sqldf("select \* from df ", locals())
this gives the mysterious (binary ?) output:
f
0 (1, 0, 0, 0, 0, 0, 0, 0)
1 (2, 0, 0, 0, 0, 0, 0, 0)
Digging further, I found that the problem is in those lines generated by pandas/io/sql/py :
import pandas as pd
import sqlite3
df=pd.DataFrame([ 1, 2], columns=['f' ])
con = sqlite3.connect(":memory:")
con.execute("CREATE TABLE df ( [f] INTEGER );")
data = [tuple(x) for x in df.values]
con.executemany("INSERT INTO df ([f]) VALUES (?)", data)
# here is the way to see the bogus result
for row in con.execute("select \* from f"):
print (row)
# this print :
# (b'\x01\x00\x00\x00\x00\x00\x00\x00',)
# (b'\x02\x00\x00\x00\x00\x00\x00\x00',)
# instead of expected :
# (1,)
# (2,)
I can't guess further what is causing the issue, it seems there is a misunderstanding between pandas and sqlite when passing a "data" list of integers.
problem doesn't exist if we switch to non-integers like :
df=pd.DataFrame([ 1., 2], columns=['f' ])
Nota : I'm under windows vista 32 bits, python 3.3, in a country where the decimal separator is a comma (,) , maybe it's related to the problem, maybe not.
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https://api.github.com/repos/pandas-dev/pandas/issues/3629 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3629/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3629/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3629/events | https://github.com/pandas-dev/pandas/issues/3629 | 14,435,269 | MDU6SXNzdWUxNDQzNTI2OQ== | 3,629 | frame values access is slow with large frames with different dtypes | {
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} | 87 | "2013-05-17T02:08:01Z" | "2013-06-21T16:26:00Z" | "2013-06-21T15:45:41Z" | MEMBER | null | In addtion, there is a memory leak. Repeatedly `eval`ing `big.values` eats up the amount of RAM taken up by the array.
``` python
from pandas import DataFrame, concat
from numpy.random import randint, randn
from string import ascii_letters as letters
ncols = 16
nrows = 1e7
letters = list(letters)
df = DataFrame(randn(nrows, ncols), columns=letters[:ncols])
col = DataFrame(randint(1, size=nrows), columns=[letters[ncols]])
big = concat([df, col], axis=1)
big.values # very slow and leaky
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3630 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3630/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3630/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3630/events | https://github.com/pandas-dev/pandas/pull/3630 | 14,437,816 | MDExOlB1bGxSZXF1ZXN0NTc3MDQzOA== | 3,630 | ENH: Add Series.sort ascending keyword | {
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} | 1 | "2013-05-17T04:47:14Z" | "2014-07-16T08:09:34Z" | "2013-05-19T21:14:06Z" | CONTRIBUTOR | null | A convenience.
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} | 42 | "2013-05-17T10:20:57Z" | "2013-05-24T19:23:27Z" | "2013-05-19T14:18:45Z" | CONTRIBUTOR | null | Currently masking by boolean vectors it doesn't matter which syntax you use:
```
df[mask]
df.iloc[mask]
df.loc[mask]
```
are all equivalent. Should mask `df.iloc[mask]` mask by position? (this makes sense if mask is integer index).
This [SO question](http://stackoverflow.com/questions/16603765/what-is-the-most-idiomatic-way-to-index-an-object-with-a-boolean-array-in-pandas).
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https://api.github.com/repos/pandas-dev/pandas/issues/3633 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3633/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3633/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3633/events | https://github.com/pandas-dev/pandas/issues/3633 | 14,452,364 | MDU6SXNzdWUxNDQ1MjM2NA== | 3,633 | ipython segfaults when viewing DataFrame?? docs | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3634 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3634/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3634/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3634/events | https://github.com/pandas-dev/pandas/issues/3634 | 14,453,917 | MDU6SXNzdWUxNDQ1MzkxNw== | 3,634 | TextReader中文路径问题 (Bug with Chinese characters in file path) | {
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} | 7 | "2013-05-17T14:10:39Z" | "2013-06-07T17:05:41Z" | "2013-06-06T19:56:11Z" | NONE | null | Traceback (most recent call last):
File "D:\wiki\pandas_test\hello_pandas.py", line 55, in <module>
tp = pd.read_csv('tmp测试.csv', iterator=True, chunksize=5000)
File "C:\Python27\lib\site-packages\pandas\io\parsers.py", line 401, in parser_f
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-01 00:00:00, ..., 2013-01-06 00:00:00]
Length: 6, Freq: D, Timezone: None
return _read(filepath_or_buffer, kwds)
File "C:\Python27\lib\site-packages\pandas\io\parsers.py", line 209, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "C:\Python27\lib\site-packages\pandas\io\parsers.py", line 509, in __init__
self._make_engine(self.engine)
File "C:\Python27\lib\site-packages\pandas\io\parsers.py", line 611, in _make_engine
self._engine = CParserWrapper(self.f, **self.options)
File "C:\Python27\lib\site-packages\pandas\io\parsers.py", line 893, in __init__
self._reader = _parser.TextReader(src, **kwds)
File "parser.pyx", line 312, in pandas._parser.TextReader.__cinit__ (pandas\src\parser.c:2781)
File "parser.pyx", line 512, in pandas._parser.TextReader._setup_parser_source (pandas\src\parser.c:4813)
IOError: File tmp测试.csv does not exist
code:
tp = pd.read_csv('tmp测试.csv', iterator=True, chunksize=5000)
for i, chunk in enumerate(tp):
print chunk
t1 = datetime.now()
如下代码则正常: (the following code works)
tp = pd.read_csv('tmp.csv', iterator=True, chunksize=5000)
for i, chunk in enumerate(tp):
print chunk
t1 = datetime.now()
如下代码也正常： (the following also works)
tp = pd.read_csv('tmp测试.csv', iterator=True, skip_footer=1, chunksize=5000)
for i, chunk in enumerate(tp):
print chunk
t1 = datetime.now()
当不加skip_footer时用的是engine='c',这时如果文件名中有中文则会报出如下错误:IOError: File tmp测试.csv does not exist
这应该算一个bug,希望能帮忙解决
(When not using the `skip_footer` parameter, the default engine is 'c'. In that case if there are Chinese characters in the path an exception is raised "IOError: File tmp测试.csv does not exist". This should be considered a bug and I hope you can help solve the issue).
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https://api.github.com/repos/pandas-dev/pandas/issues/3635 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3635/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3635/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3635/events | https://github.com/pandas-dev/pandas/pull/3635 | 14,456,288 | MDExOlB1bGxSZXF1ZXN0NTc4MDE0Mw== | 3,635 | API: Raise on iloc indexing with a non-integer based boolean mask (GH3631) | {
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} | 0 | "2013-05-17T14:59:58Z" | "2014-06-30T11:47:32Z" | "2013-05-19T14:18:45Z" | CONTRIBUTOR | null | closed #3631
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https://api.github.com/repos/pandas-dev/pandas/issues/3636 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3636/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3636/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3636/events | https://github.com/pandas-dev/pandas/pull/3636 | 14,464,326 | MDExOlB1bGxSZXF1ZXN0NTc4NDU3NA== | 3,636 | systematic failure when writing a dataframe of 1 column of integer. this should solve issue Issue #3628 | {
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} | 10 | "2013-05-17T18:00:56Z" | "2014-06-25T05:42:34Z" | "2013-05-19T15:33:15Z" | CONTRIBUTOR | null | ...lite ( Issue #3628 )
A push of a dataframe of one column of integers fail ungraciously, because sqlite doesn't recognize well the data type.
This should solve the problem by converting back to normal types
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https://api.github.com/repos/pandas-dev/pandas/issues/3637 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3637/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3637/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3637/events | https://github.com/pandas-dev/pandas/issues/3637 | 14,467,485 | MDU6SXNzdWUxNDQ2NzQ4NQ== | 3,637 | drop from datetimeindex requires a timestamp | {
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} | 1 | "2013-05-17T19:17:17Z" | "2019-03-26T06:09:53Z" | "2019-03-26T06:09:37Z" | CONTRIBUTOR | null | See this [SO question](http://stackoverflow.com/a/16613835/1240268):
Apparently (for a DataFrame with DatetimeIndex) drop requires a Timestamp object (this is different from ix/loc which can cunningly work out you meant a timestamp, if fed a string).
```
In [11]: df1
Out[11]:
Price1 Price2 Price3
Date_Time
2012-01-01 00:00:00 63.05 41.40 68.14
2012-01-01 01:00:00 68.20 42.44 59.64
2012-01-01 02:00:00 61.68 43.18 49.81
In [12]: df1.drop(pd.Timestamp('2012-01-01 01:00:00'))
Out[12]:
Price1 Price2 Price3
Date_Time
2012-01-01 00:00:00 63.05 41.40 68.14
2012-01-01 02:00:00 61.68 43.18 49.81
In [13]: df1.drop('2012-01-01 01:00:00')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-13-334d00239c8b> in <module>()
----> 1 df1.drop('2012-01-01 01:00:00')
/Users/234BroadWalk/pandas/pandas/core/generic.pyc in drop(self, labels, axis, level)
373 new_axis = axis.drop(labels, level=level)
374 else:
--> 375 new_axis = axis.drop(labels)
376 dropped = self.reindex(**{axis_name: new_axis})
377 try:
/Users/234BroadWalk/pandas/pandas/core/index.pyc in drop(self, labels)
1296 mask = indexer == -1
1297 if mask.any():
-> 1298 raise ValueError('labels %s not contained in axis' % labels[mask])
1299 return self.delete(indexer)
1300
ValueError: labels ['2012-01-01 01:00:00'] not contained in axis
```
Compare to ix/iloc which don't care:
```
In [21]: df1.loc['2012-01-01 01:00:00']
Out[21]:
Price1 68.20
Price2 42.44
Price3 59.64
Name: 2012-01-01 01:00:00, dtype: float64
In [22]: df1.loc[pd.Timestamp('2012-01-01 01:00:00')]
Out[22]:
Price1 68.20
Price2 42.44
Price3 59.64
Name: 2012-01-01 01:00:00, dtype: float64
```
_Perhaps worth investigating where else this could work/not._
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} | [] | closed | false | null | [] | null | 9 | "2013-05-17T21:08:38Z" | "2013-05-19T16:15:48Z" | "2013-05-19T16:15:48Z" | CONTRIBUTOR | null | This was [noticed on SO](http://stackoverflow.com/questions/16617394/pandas-how-to-select-by-partial-label-in-index/16617491?noredirect=1#comment23892039_16617491) by @DSM. It seems that the Series `str` method is a never ending iterator. I have no idea whether this should be classified as a bug, or just misuse. But here goes:
```
In [224]: g = (i for i in ds.str)
In [225]: next(g)
Out[225]:
google NaN
wikimedia NaN
wikipedia NaN
wikitravel NaN
dtype: float64
In [226]: next(g)
Out[226]:
google NaN
wikimedia NaN
wikipedia NaN
wikitravel NaN
dtype: float64
In [227]: next(g)
Out[227]:
google NaN
wikimedia NaN
wikipedia NaN
wikitravel NaN
dtype: float64
In [228]: next(g)
Out[228]:
google NaN
wikimedia NaN
wikipedia NaN
wikitravel NaN
dtype: float64
In [229]: list(g) # lalalala
```
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} | 1 | "2013-05-17T23:34:55Z" | "2013-05-18T12:22:30Z" | "2013-05-18T12:22:30Z" | CONTRIBUTOR | null | I don't understand which rst file builds this page, but the one currently online here:
http://pandas.pydata.org/pandas-docs/stable/whatsnew.html
as the following typo in it:
- added option display.with_wmp_style
the key `with_wmp_style` is not known. It sounds a bit like this should be
`display.mpl_style` which works fine for me like this:
`pd.set_option('display.mpl_style','default')`
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} | 12 | "2013-05-18T00:51:48Z" | "2013-09-22T01:06:04Z" | "2013-09-22T01:06:04Z" | CONTRIBUTOR | null | This is the example in io.rst for Stata (in current master)
coming from PR #3270 and issue #1512
```
In [5]: from pandas.io.stata import StataWriter
In [6]: df = DataFrame(randn(10,2),columns=list('AB'))
In [7]: writer = StataWriter('stata.dta',df)
In [8]: writer.write_file()
```
I am not sure if you have enough information saved to know that this needs a
`df.set_index('index')`
?
```
In [10]: from pandas.io.stata import StataReader
In [11]: reader = StataReader('stata.dta')
In [12]: reader.data()
Out[12]:
index A B
0 0 0.818436 -0.616332
1 1 -0.673509 2.209445
2 2 -0.074915 0.444409
3 3 0.984456 -1.397691
4 4 -0.402488 0.884691
5 5 0.407234 0.499808
6 6 -0.041578 0.724288
7 7 -0.110134 0.707406
8 8 0.986992 1.281154
9 9 -1.491163 -0.686034
```
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} | 13 | "2013-05-18T15:00:24Z" | "2013-06-19T06:37:35Z" | "2013-06-19T06:37:35Z" | NONE | null | introduced by #3619, opening this to track the issue for 0.11.1.
While the user should be able to get sorted ticks when plotting, it should not
be forced on him. so either opt-in or opt-out but not "one size fits all".
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} | 1 | "2013-05-18T22:14:11Z" | "2014-06-12T07:39:51Z" | "2013-05-19T16:49:55Z" | CONTRIBUTOR | null | closes #3628
This should be the proper pull request to
- TST test the problem,
- BUG the problem of panda fail to write a single column of integer to sqlite
Test successfully on Travis CI
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DOC: added non-unique index issues to v0.11.1
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} | 12 | "2013-05-19T18:59:50Z" | "2014-06-12T14:57:21Z" | "2013-05-21T14:25:37Z" | CONTRIBUTOR | null | ENH, CLN: When melt()'ing, allow for specification of custom variable and value names of the resulting DataFrame. Also fix indentation in melt() example to reflect actual output.
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} | 1 | "2013-05-19T21:09:36Z" | "2013-09-26T21:22:21Z" | "2013-09-26T21:22:21Z" | CONTRIBUTOR | null | see #3603 deprecated in 0.11.1
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## Prepare the data:
```
import string
import random
import pandas as pd
matrix = np.random.random((100, 3000))
my_cols = [random.choice(string.ascii_uppercase) for x in range(matrix.shape[1])]
mydf = pd.DataFrame(matrix, columns=my_cols)
mydf['something'] = 'hello_world'
```
## Set highest compression possible for HDF5:
```
store = pd.HDFStore('myfile.h5',complevel=9, complib='bzip2')
store['mydf'] = mydf
store.close()
```
## Save also to CSV:
```
mydf.to_csv('myfile.csv', sep=':')
```
The result is:
- `myfile.h5` is 11 MB big
- `myfile.csv` is 5.6 MB big
I have tried with other compression methods and levels. Is this a bug? (I am using Pandas 0.11 and the latest stable version of HDF5 and Python).
## Summary
I am surprised that even though:
- The majority of the DataFrame (300K entries) is of type `float64`,
- There are only 100 strings, and **all** of them hold the exact same value (`hello_world`)
- I am using the highest possible compression scheme.
the HDF5 takes _more_ space than the CSV file. Is this normal/expected?
Note: I have posted this issue also in PyTables, since I don't know where exactly it fits:
https://github.com/PyTables/PyTables/issues/257
Note: This issue is based on the following thread at StackOverflow:
http://stackoverflow.com/questions/16639877/hdf5-taking-more-space-than-csv
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Once localized, the underlying timestamps are in UTC and remain in UTC, to revert to naive / no-timezone format with UTC timestamps corresponding to the equivalent local time is not a straightforward op.
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} | 0 | "2013-05-20T13:25:07Z" | "2013-05-21T13:43:38Z" | "2013-05-21T13:43:38Z" | CONTRIBUTOR | null | @cpcloud
https://www.travis-ci.org/pydata/pandas/jobs/7320947
I don't think travis was actually testing html5lib stuff....(I just added it in)
its taken out right now
add in ci/install.sh (right after bs4)....and test
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} | 2 | "2013-05-20T16:31:24Z" | "2014-06-12T18:10:10Z" | "2013-05-25T23:08:36Z" | NONE | null | #3573, and SO question mentioned in #3541
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} | 9 | "2013-05-20T18:31:31Z" | "2014-05-29T06:53:23Z" | "2014-05-29T06:53:23Z" | NONE | null | It appears that it is not possible to create a Series from a dict whose keys are unorderable. `Series.__init__` unconditionally attempts to sort a dict that is passed as the data. This means that, for instance, you can't simply create a Series from a dict whose keys are complex numbers:
`>>> pandas.Series({1j: 1, 2j: 2})
Traceback (most recent call last):
File "<pyshell#33>", line 1, in <module>
pandas.Series({1j: 1, 2j: 2})
File "c:\users\brenbarn\documents\python\extensions\pandas\pandas\core\series.py", line 444, in __new__
index = Index(sorted(data))
TypeError: no ordering relation is defined for complex numbers`
It's possible to work around the problem by passing the dict values as the data and the keys as the index, but this is cumbersome. It would be nice to just have an argument to Series() that tells it not to try to sort the index.
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} | 0 | "2013-05-20T19:16:07Z" | "2013-05-20T23:34:40Z" | "2013-05-20T23:34:40Z" | CONTRIBUTOR | null | This throws a weird error
```
df = DataFrame({'A' : [1,2,3,4], 'B' : [3, 4, 5, 6]}, index = [0,1,0,1])
df.loc[1:]
```
https://groups.google.com/forum/?fromgroups#!topic/pydata/zTm2No0crYs
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} | [] | closed | false | null | [] | null | 2 | "2013-05-20T19:34:17Z" | "2013-05-30T01:13:00Z" | "2013-05-30T01:13:00Z" | CONTRIBUTOR | null | ``` python
import pandas as pd
from numpy.random import rand
idx = pd.date_range(start='5/20/2013', end='5/21/2013')
df = pd.DataFrame({k:(100*rand(len(idx))).astype('int') for k in ['6', '36']}, index=idx)
```
everything looks fine:
``` python
>>> df
36 6
2013-05-20 58 23
2013-05-21 68 31
```
and even this works:
``` python
>>> df['6']
2013-05-20 23
2013-05-21 31
Freq: D, Name: 6, dtype: int32
```
but this is one fails:
``` python
>>> df['36']
Empty DataFrame
Columns: [36, 6]
Index: []
```
it seems to me the problem comes from a 'DatetimeIndex', because this works fine:
``` python
>>> df.index = [d.date() for d in df.index]
>>> df['36']
2013-05-20 58
2013-05-21 68
Name: 36, dtype: int32
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3661 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3661/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3661/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3661/events | https://github.com/pandas-dev/pandas/pull/3661 | 14,535,448 | MDExOlB1bGxSZXF1ZXN0NTgxNzY4MQ== | 3,661 | BUG: Non-unique indexing via loc and friends fixed when slicing (GH3659_) | {
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"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/22"
} | 2 | "2013-05-20T19:44:44Z" | "2014-06-20T10:08:11Z" | "2013-05-20T23:34:40Z" | CONTRIBUTOR | null | closes #3659
This is if you try a non_monotonic selection on a non_unique index (a mouthful)!
The reason is we cannot determinate a proper start/end point on what to include
```
In [11]: df = DataFrame({'A' : [1,2,3,4,5,6], 'B' : [3,4,5,6,7,8]},
index = [0,1,0,1,2,3])
In [18]: df
Out[18]:
A B
0 1 3
1 2 4
0 3 5
1 4 6
2 5 7
3 6 8
In [12]: df.loc[1:]
KeyError: 'cannot perform a slice operation on a non-unique non-monotonic index'
```
On a non_unique, but monotonic index, however, slicing works normally
(notice, since we are using loc, that both endpoints ARE included)
```
In [13]: df = DataFrame({'A' : [1,2,3,4,5,6], 'B' : [3,4,5,6,7,8]},
index = [0,1,0,1,2,3]).sort(axis=0)
In [14]: df
Out[14]:
A B
0 1 3
0 3 5
1 2 4
1 4 6
2 5 7
3 6 8
In [15]: df.loc[1:]
Out[15]:
A B
1 2 4
1 4 6
2 5 7
3 6 8
In [16]: df.loc[1:2]
Out[16]:
A B
1 2 4
1 4 6
2 5 7
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3662 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3662/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3662/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3662/events | https://github.com/pandas-dev/pandas/issues/3662 | 14,542,236 | MDU6SXNzdWUxNDU0MjIzNg== | 3,662 | Merge on single level of MultiIndex | {
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] | closed | false | null | [] | {
"closed_at": "2014-05-30T12:17:57Z",
"closed_issues": 755,
"created_at": "2014-01-03T20:19:04Z",
"creator": null,
"description": "2014-02-3: previous version released",
"due_on": "2014-05-31T07:00:00Z",
"html_url": "https://github.com/pandas-dev/pandas/milestone/24",
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} | 20 | "2013-05-20T22:16:27Z" | "2014-06-12T13:47:33Z" | "2014-02-15T22:08:44Z" | CONTRIBUTOR | null | I don't think we can do this now, but it potentially sounds useful...
http://stackoverflow.com/questions/16650945/merge-on-single-level-of-multiindex
https://groups.google.com/forum/#!topic/pydata/LBSFq6of8ao
Example:
```
In [10]: df = pd.DataFrame(randn(10,5))
In [11]: df1 = df[range(0, 3)].set_index([0, 2])
In [12]: df2 = df[range(2, 5)].set_index([2, 4])
```
join/merge on 2.
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https://api.github.com/repos/pandas-dev/pandas/issues/3663 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3663/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3663/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3663/events | https://github.com/pandas-dev/pandas/pull/3663 | 14,545,363 | MDExOlB1bGxSZXF1ZXN0NTgyMjk2Ng== | 3,663 | Rework display logic again. | {
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} | 8 | "2013-05-21T00:07:41Z" | "2014-06-22T03:00:30Z" | "2013-05-25T23:08:36Z" | NONE | null | v0.11.0 introduced last-minute changes to the way the dataframe display logic works,
and they haven't proven themselves. The churn is unforunate, but if it's broken,
fix it.
`height` is deprecated.
max_rows solely determines the amount of rows displayed.
rebased on top of #3657.
Merging the previous PR and subsequent last minute fixes so close to the release
contributed to the problems only being discovered after the release.
So please test and report if this works for you, in terminal, qtconsole or notebook,
since the 0.11.1 release is so close.
cc @hayd , @lodagro ,@jreback
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https://api.github.com/repos/pandas-dev/pandas/issues/3664 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3664/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3664/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3664/events | https://github.com/pandas-dev/pandas/pull/3664 | 14,546,945 | MDExOlB1bGxSZXF1ZXN0NTgyMzgxNw== | 3,664 | ENH: sort the keys of a passed dict using numpy's sort function | {
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} | [] | closed | false | null | [] | null | 0 | "2013-05-21T01:24:17Z" | "2014-07-04T15:32:41Z" | "2013-05-21T02:51:26Z" | MEMBER | null | closes #3658
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https://api.github.com/repos/pandas-dev/pandas/issues/3665 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3665/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3665/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3665/events | https://github.com/pandas-dev/pandas/pull/3665 | 14,548,327 | MDExOlB1bGxSZXF1ZXN0NTgyNDUyNw== | 3,665 | CLN: pandas stata reader should clean up after itself | {
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|
https://api.github.com/repos/pandas-dev/pandas/issues/3666 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3666/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3666/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3666/events | https://github.com/pandas-dev/pandas/pull/3666 | 14,562,751 | MDExOlB1bGxSZXF1ZXN0NTgzMTMxMQ== | 3,666 | ENH: Allow fetching stock dividend and split info from yahoo | {
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} | [] | closed | false | null | [] | null | 8 | "2013-05-21T11:28:43Z" | "2014-07-01T07:47:18Z" | "2014-01-18T03:42:51Z" | NONE | null | Usage:
``` python
from pandas.io.data import DataReader
```
Requesting dividends and splits but only dividends available
``` python
DataReader('CAT', 'yahoo', '12/31/04', '1/18/05', dividends=True, splits=True)
Dividends
Date
2005-01-18.0 0.205
```
Requesting dividends and splits but only splits available
``` python
DataReader('CAT', 'yahoo', '7/14/05', '7/15/05', dividends=True, splits=True)
Splits
Date
2005-07-14 2:1
```
Requesting dividends and splits and both are available
``` python
DataReader('CAT', 'yahoo', '7/1/05', '8/1/05', dividends=True, splits=True)
Dividends Splits
Date
2005-07-14 NaN 2:1
2005-07-20 0.250000 NaN
```
Requesting multiple dividends and splits
``` python
DataReader('CAT', 'yahoo', '7/14/97', '7/15/05', dividends=True, splits=True)
Dividends Splits
Date
1997-07-14 NaN 2:1
1997-07-17 0.125000 NaN
1997-10-16 0.125000 NaN
1998-01-15 0.125000 NaN
1998-04-16 0.125000 NaN
1998-07-16 0.150000 NaN
1998-10-22 0.150000 NaN
1999-01-15 0.150000 NaN
1999-04-22 0.150000 NaN
1999-07-16 0.162500 NaN
1999-10-21 0.162500 NaN
2000-01-18 0.162500 NaN
2000-04-19 0.162500 NaN
2000-07-18 0.170000 NaN
2000-10-18 0.170000 NaN
2001-01-18 0.170000 NaN
2001-04-19 0.170000 NaN
2001-07-18 0.175000 NaN
2001-10-18 0.175000 NaN
2002-01-17 0.175000 NaN
2002-04-18 0.175000 NaN
2002-07-18 0.175000 NaN
2002-10-17 0.175000 NaN
2003-01-16 0.175000 NaN
2003-04-16 0.175000 NaN
2003-07-17 0.175000 NaN
2003-10-16 0.185000 NaN
2004-01-15 0.185000 NaN
2004-04-22 0.185000 NaN
2004-07-16 0.205000 NaN
2004-10-21 0.205000 NaN
2005-01-18 0.205000 NaN
2005-04-21 0.205000 NaN
2005-07-14 NaN 2:1
```
Requesting dividends or splits and neither are available
``` python
DataReader('CAT', 'yahoo', '7/21/05', '7/22/05', dividends=True)
DataReader('CAT', 'yahoo', '7/21/05', '7/22/05', splits=True)
Empty DataFrame
```
Requesting dividends and only dividends are available
``` python
DataReader('CAT', 'yahoo', '12/31/04', '1/18/05', dividends=True)
Dividends
2005-01-18 0.205
```
Requesting dividends and both dividends and splits are available
``` python
DataReader('CAT', 'yahoo', '7/1/05', '8/01/05', dividends=True)
Dividends
2005-07-20 0.25
```
Requesting dividends and only splits are available
``` python
DataReader('CAT', 'yahoo', '7/14/05', '7/15/05', dividends=True)
Empty DataFrame
```
Requesting multiple dividends
``` python
DataReader('CAT', 'yahoo', '7/20/05', '9/21/06', dividends=True)
Dividends
2005-07-20 0.25
2005-10-20 0.25
2006-01-18 0.25
2006-04-20 0.25
2006-07-18 0.30
```
Requesting splits and only splits are available
``` python
DataReader('CAT', 'yahoo', '7/14/05', '7/15/05', splits=True)
Splits
Date
2005-07-14 2:1
```
Requesting splits and both dividends and splits are available
``` python
DataReader('CAT', 'yahoo', '7/1/05', '8/01/05', splits=True)
Splits
Date
2005-07-14 2:1
```
Requesting splits and only dividends are available
``` python
DataReader('CAT', 'yahoo', '12/31/04', '1/18/05', splits=True)
Empty DataFrame
```
Requesting multiple splits
``` python
DataReader('CAT', 'yahoo', '7/14/70', '7/15/05', splits=True)
Splits
Date
1994-09-06 2:1
1997-07-14 2:1
2005-07-14 2:1
```
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} | 0 | "2013-05-21T13:13:27Z" | "2013-05-30T18:12:03Z" | "2013-05-30T18:12:03Z" | CONTRIBUTOR | null | https://github.com/pydata/pandas/pull/3663
maybe allow these:
`pd.set_option(pat,val,pat,val`
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} | 0 | "2013-05-21T14:55:01Z" | "2013-06-12T17:55:10Z" | "2013-05-21T17:37:31Z" | CONTRIBUTOR | null | ```
In [35]: df = pandas.DataFrame({'x':range(10), 'y':range(10,20),'z' : 'bar'})
In [36]: df.ix[df.x % 2 == 0, 'y'] = df.ix[df.x % 2 == 0, 'y'].values * 100
In [37]: df
Out[37]:
x y z
0 0 1000 bar
1 1 11 bar
2 2 1000 bar
3 3 13 bar
4 4 1000 bar
5 5 15 bar
6 6 1000 bar
7 7 17 bar
8 8 1000 bar
9 9 19 bar
```
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} | 7 | "2013-05-21T14:56:48Z" | "2013-07-08T09:27:57Z" | "2013-07-06T21:16:06Z" | CONTRIBUTOR | null | _From the [So question](http://stackoverflow.com/questions/16672237/specifying-date-format-when-converting-with-pandas-to-datetime)._
I think apply be passing on the format keyword argument:
```
In [1]: s = pd.Series(['12/1/2012', '30/01/2012'])
In [2]: s.apply(pd.to_datetime, format='%d/%m/%Y')
Out[2]:
0 2012-12-01 00:00:00
1 2012-01-30 00:00:00
dtype: datetime64[ns]
In [3]: pd.to_datetime(s, format='%d/%m/%Y')
Out[3]:
0 2012-01-12 00:00:00
1 2012-01-30 00:00:00
dtype: datetime64[ns]
```
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} | 0 | "2013-05-21T16:14:14Z" | "2014-06-23T10:57:56Z" | "2013-05-21T17:37:31Z" | CONTRIBUTOR | null | closes #3668
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} | 0 | "2013-05-21T17:41:33Z" | "2014-07-09T17:58:31Z" | "2013-05-21T19:04:01Z" | CONTRIBUTOR | null | this is a 'bug' in dateutil:
`a,t,m,p` are the offenders
```
In [1]: import dateutil
In [2]: dateutil.parser.parse('a')
Out[2]: datetime.datetime(2013, 5, 21, 0, 0)
```
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} | 0 | "2013-05-21T18:25:32Z" | "2014-03-11T01:41:23Z" | "2014-03-11T01:41:23Z" | CONTRIBUTOR | null | see #3663
examples:
limit the max_columns, rows
(just set `max_rows or max_columns`)
but allow a certain number to be shown expaneded
(need `expand_frame_repr = False`)
setting line width to interact with the above
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} | 2 | "2013-05-21T18:26:36Z" | "2014-07-16T08:10:09Z" | "2013-06-03T01:18:28Z" | CONTRIBUTOR | null | Currently scatter_matrix passes all unmatched keyword arguments to both scatter and line subplots (off-diagonal plots are scatter, diagonal plots are discrete or continuous histograms). I needed to color the points on the scatter plots but could not pass a "c" argument or pandas would try to pass the "c" argument to the line plots and barf. This change scratches my itch and doesn't break backward compatibility.
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} | 4 | "2013-05-21T19:40:29Z" | "2013-06-13T18:43:02Z" | "2013-06-13T18:42:47Z" | CONTRIBUTOR | null | from the [pydata mailing list](https://groups.google.com/forum/?fromgroups#!topic/pydata/40-Yj9R0LJ4)
I've been using Series.interpolate(), and have noticed that, while it
leaves NaN entries alone if they precede a non-null value, it blows up if
there are no non-null entries. Wouldn't it make more sense just to leave
completely null series alone (so the user doesn't have to manually check
for this case)? Here's an example:
1) This one works:
``` python
S1 = Series([np.nan, 2.0])
print S1.interpolate()
-------
0 NaN
1 2
dtype: float64
```
2) This one blows up:
``` python
S2 = Series([np.nan, np.nan])
print S2.interpolate()
ValueError Traceback (most recent call last)
<ipython-input-49-620b22122e43> in <module>()
3
4 S2 = Series([np.nan, np.nan])
----> 5 print S2.interpolate()
/RHS/packages/anaconda/pandas/pandas/core/series.pyc in interpolate(self,
method)
3191 result = values.copy()
3192 result[firstIndex:][invalid] = np.interp(inds[invalid],
inds[valid],
-> 3193
values[firstIndex:][valid])
3194
3195 return Series(result, index=self.index, name=self.name)
/Users/stanton/anaconda/lib/python2.7/site-packages/numpy/lib/function_base
.pyc in interp(x, xp, fp, left, right)
1067 return compiled_interp([x], xp, fp, left, right).item()
1068 else:
-> 1069 return compiled_interp(x, xp, fp, left, right)
1070
1071
ValueError: array of sample points is empty
```
3) empty series (not mentioned in the original post)
``` python
In [38]: pd.Series([]).interpolate()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-38-012633a78523> in <module>()
----> 1 pd.Series([]).interpolate()
.../pandas/core/series.pyc in interpolate(self, method)
3184 valid = -invalid
3185
-> 3186 firstIndex = valid.argmax()
3187 valid = valid[firstIndex:]
3188 invalid = invalid[firstIndex:]
ValueError: attempt to get argmax/argmin of an empty sequence
```
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} | 8 | "2013-05-21T20:33:17Z" | "2014-06-14T02:16:46Z" | "2013-05-22T13:45:59Z" | MEMBER | null | closes #3582.
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} | 8 | "2013-05-21T22:59:34Z" | "2014-08-19T13:58:05Z" | "2014-08-19T13:58:05Z" | CONTRIBUTOR | null | Not sure how to handle this yet. It looks like NaNs do not become a level. Should they? Maybe so. Also describe fails if NaNs are present.
```
pandas.Categorical([np.nan, np.nan, 1, 1, 1, 2, 3, 4, 5, 5, 4, 3, 3]).describe()
```
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} | 2 | "2013-05-22T00:54:35Z" | "2013-05-30T00:48:23Z" | "2013-05-30T00:48:23Z" | CONTRIBUTOR | null | This currently breaks
```
In [1]: df = DataFrame([[1,1,1,5],[1,1,2,5],[2,1,3,5]],columns=['foo','bar','foo','hello'])
In [2]: df['string'] = 'bar'
In [3]: df
IndexError: ('index out of bounds', u'occurred at index hello')
```
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} | 41 | "2013-05-22T02:43:16Z" | "2014-06-15T15:23:56Z" | "2013-06-06T21:06:26Z" | CONTRIBUTOR | null | closes #919
I have been using Wes' workaround (see #919) for filtering groups. Finally, for brevity's sake, I wrote a real filter method. In the one simple case I checked, it performs faster than the workaround.
On a small (~10) Series:
```
In [7]: %timeit grouped.filter(lambda x: x.mean() > 10) # my method
1000 loops, best of 3: 346 us per loop
In [8]: %timeit grouped.obj[grouped.transform(lambda x: x.mean() > 10)] # workaround
1000 loops, best of 3: 462 us per loop
```
On a large (1000000) Series:
```
In [18]: %timeit grouped.filter(lambda x: x.mean() > 0) # my method
1 loops, best of 3: 213 ms per loop
In [19]: %timeit grouped.obj[grouped.transform(lambda x: x.mean() > 0)]
1 loops, best of 3: 696 ms per loop
```
This PR only handles Series, and I included one simple test. If I am on the right track, I'll write one for DataFrames also and write additional tests. If this is a job for Cython, I'm out of my depth, but I think numpy is sufficient. Does this look OK?
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} | 10 | "2013-05-22T03:00:05Z" | "2014-05-29T06:58:24Z" | "2014-05-29T06:58:24Z" | NONE | null | I'm having an issue with undefined symbols to_boolean and to_double. I have tried a few different things, but with no success. This build is in a cluster environment and the following packages have been built from source previous before attempting the pandas build:
Python 2.7.3
NumPy 1.7.0
SciPy 0.12.0 beta1
mpi4py 1.3
matplotlib 1.2.1
ipython 0.13.1
I suppose the SciPy package could be the issue. I will upgrade and try again. I think the issue may be more with using the Intel compilers and not the GNU compilers perhaps. The rest of the packages work well. If anybody has had issues similar, I'd be curious on your input / suggestions. Thanks!
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} | 0 | "2013-05-22T14:02:09Z" | "2013-07-25T16:46:59Z" | "2013-05-30T00:46:35Z" | NONE | null | ``` python
a = np.array([(1, 2)], dtype=[('id', np.int64), ('value', np.int64)])
df = pd.DataFrame.from_records(a, index='id') # ok
b = np.array([], dtype=[('id', np.int64), ('value', np.int64)])
df = pd.DataFrame.from_records(b, index='id') # KeyError: 'id'
```
The cause is _to_arrays checks for empty data before understanding that ndarray was passed.
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} | 6 | "2013-05-22T14:44:07Z" | "2014-06-12T20:29:27Z" | "2013-05-30T00:48:23Z" | CONTRIBUTOR | null | closes #3679, #3687
Here's example of various operations
```
In [3]: df = DataFrame([[1,1,1,5],[1,1,2,5],[2,1,3,5]],
columns=['foo','bar','foo','hello'])
In [27]: df.columns.is_unique
Out[27]: False
In [4]: # insert
In [5]: df['string'] = 'bah'
In [6]: df
Out[6]:
foo bar foo hello string
0 1 1 1 5 bah
1 1 1 2 5 bah
2 2 1 3 5 bah
In [7]: # insert same dtype
In [8]: df['foo2'] = 3
In [9]: df
Out[9]:
foo bar foo hello string foo2
0 1 1 1 5 bah 3
1 1 1 2 5 bah 3
2 2 1 3 5 bah 3
In [10]: # delete (non dup)
In [11]: del df['bar']
In [12]: df
Out[12]:
foo foo hello string foo2
0 1 1 5 bah 3
1 1 2 5 bah 3
2 2 3 5 bah 3
In [13]: # try to delete again (its not consolidated)
In [14]: del df['hello']
In [15]: df
Out[15]:
foo foo string foo2
0 1 1 bah 3
1 1 2 bah 3
2 2 3 bah 3
In [16]: # insert
In [17]: df.insert(2,'new_col',5.)
In [18]: df
Out[18]:
foo foo new_col string foo2
0 1 1 5 bah 3
1 1 2 5 bah 3
2 2 3 5 bah 3
```
This is the current default behavior now
```
In [19]: # insert a dup
In [20]: df.insert(2,'new_col',4.)
Exception: cannot insert new_col, already exists
In [21]: # insert a dup
In [22]: df.insert(2,'new_col',4.,allow_duplicates=True)
In [23]: df
Out[23]:
foo foo new_col new_col string foo2
0 1 1 4 5 bah 3
1 1 2 4 5 bah 3
2 2 3 4 5 bah 3
```
```
In [24]: # delete (dup)
In [25]: del df['foo']
In [26]: df
Out[26]:
new_col new_col string foo2
0 4 5 bah 3
1 4 5 bah 3
2 4 5 bah 3
```
Don't try this at home
1) duplicates across dtypes
2) assigning those duplicates
```
In [5]: df = DataFrame([[1,1,1.,5],[1,1,2.,5],[2,1,3.,5]],columns=['foo','bar','foo','hello'])
In [6]: df.dtypes
Out[6]:
foo int64
bar int64
foo float64
hello int64
dtype: object
In [7]: df['foo'] = 'string'
In [8]: df
Out[8]:
foo bar foo hello
0 string 1 string 5
1 string 1 string 5
2 string 1 string 5
```
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} | 3 | "2013-05-22T16:26:29Z" | "2013-09-22T01:09:34Z" | "2013-09-22T01:09:34Z" | CONTRIBUTOR | null | pandas already uses statsmodels for various stat functions, this seems natural (and can always fall back to the currernt method of statsmodels is not available)
https://groups.google.com/forum/?fromgroups#!topic/pydata/sQVPKD1RTrs
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} | 6 | "2013-05-22T17:52:46Z" | "2015-01-26T01:00:49Z" | "2015-01-26T01:00:49Z" | CONTRIBUTOR | null | Carrying over from [SO comments](http://stackoverflow.com/questions/16697869/getting-specific-median-from-data/16698053?noredirect=1#comment24034122_16698053)
Is it worth adding kwargs `start` and `stop` to enable this succinct usage?
```
df.resample('D', how='range', start=0, stop=3)
```
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} | 8 | "2013-05-22T19:17:24Z" | "2019-10-18T17:03:48Z" | "2014-02-26T22:34:07Z" | NONE | null | I have a multiindex dataframe from which I am dropping columns using df.drop(col,axis=1). Then, I am looking through column.levels[0] and doing some operations on all the columns. However, when I try to do this, pandas looks for the removed column since it is not removed from column.levels. Is this a bug? Is there a WAR?
Here is the df I am working with:
```
^GSPC PF
Adj Close Adj Close
Date
2013-04-22 1562.50 4023.45
2013-04-23 1578.78 4099.20
2013-04-24 1578.79 4094.70
2013-04-25 1585.16 4124.25
2013-04-26 1582.24 4211.65
2013-04-29 1593.61 4340.75
2013-04-30 1597.57 4467.55
2013-05-01 1582.70 4432.25
2013-05-02 1597.59 4494.95
2013-05-03 1614.42 4539.55
2013-05-06 1617.50 4645.95
2013-05-07 1625.96 4624.65
2013-05-08 1632.69 4677.40
2013-05-09 1626.67 4637.25
2013-05-10 1633.70 4602.40
2013-05-13 1633.77 4618.60
2013-05-14 1650.34 4510.85
2013-05-15 1658.78 4362.00
2013-05-16 1650.47 4418.95
2013-05-17 1667.47 4406.95
2013-05-20 1666.29 4503.50
2013-05-21 1669.16 4471.20
```
Here is how I am dropping the columns:
``` python
data = data.drop(stock.ticker,axis=1,level=0)
```
Here is where the issue is:
``` python
print data.columns
MultiIndex
[(^GSPC, Adj Close), (PF, Adj Close)]
print data.columns.labels
[array([2, 3]), array([0, 0])]
print data.columns.levels
[Index([nvda, aapl, ^GSPC, PF], dtype=object), Index([Adj Close], dtype=object)]
```
Method used to generate DF as requested in the comment:
tickers is a list of stock ticker strings.
stock is an object that has a ticker property among others.
portfolio is an object that is a collection of stocks.
```
data = getdata.get_history(tickers,dt.today()-relativedelta(months=months))
data = data.drop(['Open','High','Low','Close','Volume'],axis=1)
data = data.unstack(0).swaplevel(0,1,axis=1).sortlevel(0,axis=1)
data['PF','Adj Close'] = np.zeros(len(data))
for stock in portfolio.getStocksInPortfolio():
data['PF','Adj Close'] += data[stock.ticker,'Adj Close'] * stock.getSharesOwned()
data = data.drop(stock.ticker,axis=1,level=0)
```
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} | 3 | "2013-05-23T11:10:38Z" | "2013-05-30T00:48:55Z" | "2013-05-30T00:48:55Z" | CONTRIBUTOR | null | If you try and change the column names of a DataFrame with non-unique column names, it seems to throw and error (11.0 and dev).
```
In [1]: df = pd.DataFrame(np.random.randn(3, 2), columns=['A', 'A'])
In [2]: df.columns = range(2)
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
<ipython-input-2-454b6d12f4bf> in <module>()
----> 1 df.columns = range(2)
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/frame.pyc in __setattr__(self, name, value)
2016 existing = getattr(self, name)
2017 if isinstance(existing, Index):
-> 2018 super(DataFrame, self).__setattr__(name, value)
2019 elif name in self.columns:
2020 self[name] = value
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/lib.so in pandas.lib.AxisProperty.__set__ (pandas/lib.c:28448)()
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/generic.pyc in _set_axis(self, axis, labels)
557
558 def _set_axis(self, axis, labels):
--> 559 self._data.set_axis(axis, labels)
560 self._clear_item_cache()
561
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/internals.pyc in set_axis(self, axis, value)
920 if axis == 0:
921 for block in self.blocks:
--> 922 block.set_ref_items(self.items, maybe_rename=True)
923
924 # make items read only for now
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/internals.pyc in set_ref_items(self, ref_items, maybe_rename)
72 raise AssertionError('block ref_items must be an Index')
73 if maybe_rename:
---> 74 self.items = ref_items.take(self.ref_locs)
75 self.ref_items = ref_items
76
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/internals.pyc in ref_locs(self)
57 def ref_locs(self):
58 if self._ref_locs is None:
---> 59 indexer = self.ref_items.get_indexer(self.items)
60 indexer = com._ensure_platform_int(indexer)
61 if (indexer == -1).any():
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/index.pyc in get_indexer(self, target, method, limit)
847
848 if not self.is_unique:
--> 849 raise Exception('Reindexing only valid with uniquely valued Index '
850 'objects')
851
Exception: Reindexing only valid with uniquely valued Index objects
```
The similar behaviour (with index) doesn't.
_From this [SO question](http://stackoverflow.com/questions/16711716/rename-pandas-columns-with-datetime-objects)._
What makes this problem a little more annoying is that once you've seen this error you can no longer use `df` (!):
```
In [5]: df
Out[5]: ---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-5-7ed0097d7e9e> in <module>()
----> 1 df
/Library/Python/2.7/site-packages/IPython/core/displayhook.pyc in __call__(self, result)
236 self.start_displayhook()
237 self.write_output_prompt()
--> 238 format_dict = self.compute_format_data(result)
239 self.write_format_data(format_dict)
240 self.update_user_ns(result)
/Library/Python/2.7/site-packages/IPython/core/displayhook.pyc in compute_format_data(self, result)
148 MIME type representation of the object.
149 """
--> 150 return self.shell.display_formatter.format(result)
151
152 def write_format_data(self, format_dict):
/Library/Python/2.7/site-packages/IPython/core/formatters.pyc in format(self, obj, include, exclude)
124 continue
125 try:
--> 126 data = formatter(obj)
127 except:
128 # FIXME: log the exception
/Library/Python/2.7/site-packages/IPython/core/formatters.pyc in __call__(self, obj)
445 type_pprinters=self.type_printers,
446 deferred_pprinters=self.deferred_printers)
--> 447 printer.pretty(obj)
448 printer.flush()
449 return stream.getvalue()
/Library/Python/2.7/site-packages/IPython/lib/pretty.pyc in pretty(self, obj)
358 if callable(meth):
359 return meth(obj, self, cycle)
--> 360 return _default_pprint(obj, self, cycle)
361 finally:
362 self.end_group()
/Library/Python/2.7/site-packages/IPython/lib/pretty.pyc in _default_pprint(obj, p, cycle)
478 if getattr(klass, '__repr__', None) not in _baseclass_reprs:
479 # A user-provided repr.
--> 480 p.text(repr(obj))
481 return
482 p.begin_group(1, '<')
/Users/234BroadWalk/pandas/pandas/core/frame.pyc in __repr__(self)
725 Yields Bytestring in Py2, Unicode String in py3.
726 """
--> 727 return str(self)
728
729 def _repr_html_(self):
/Users/234BroadWalk/pandas/pandas/core/frame.pyc in __str__(self)
668 if py3compat.PY3:
669 return self.__unicode__()
--> 670 return self.__bytes__()
671
672 def __bytes__(self):
/Users/234BroadWalk/pandas/pandas/core/frame.pyc in __bytes__(self)
678 """
679 encoding = com.get_option("display.encoding")
--> 680 return self.__unicode__().encode(encoding, 'replace')
681
682 def __unicode__(self):
/Users/234BroadWalk/pandas/pandas/core/frame.pyc in __unicode__(self)
693 # This needs to compute the entire repr
694 # so don't do it unless rownum is bounded
--> 695 fits_horizontal = self._repr_fits_horizontal_()
696
697 if fits_vertical and fits_horizontal:
/Users/234BroadWalk/pandas/pandas/core/frame.pyc in _repr_fits_horizontal_(self)
653 d=d.iloc[:min(max_rows, height,len(d))]
654
--> 655 d.to_string(buf=buf)
656 value = buf.getvalue()
657 repr_width = max([len(l) for l in value.split('\n')])
/Users/234BroadWalk/pandas/pandas/core/frame.pyc in to_string(self, buf, columns, col_space, colSpace, header, index, na_rep, formatters, float_format, sparsify, nanRep, index_names, justify, force_unicode, line_width)
1542 header=header, index=index,
1543 line_width=line_width)
-> 1544 formatter.to_string()
1545
1546 if buf is None:
/Users/234BroadWalk/pandas/pandas/core/format.pyc in to_string(self, force_unicode)
292 text = info_line
293 else:
--> 294 strcols = self._to_str_columns()
295 if self.line_width is None:
296 text = adjoin(1, *strcols)
/Users/234BroadWalk/pandas/pandas/core/format.pyc in _to_str_columns(self)
245 for i, c in enumerate(self.columns):
246 if self.header:
--> 247 fmt_values = self._format_col(i)
248 cheader = str_columns[i]
249
/Users/234BroadWalk/pandas/pandas/core/format.pyc in _format_col(self, i)
383 def _format_col(self, i):
384 formatter = self._get_formatter(i)
--> 385 return format_array(self.frame.icol(i).values, formatter,
386 float_format=self.float_format,
387 na_rep=self.na_rep,
/Users/234BroadWalk/pandas/pandas/core/frame.pyc in icol(self, i)
1911
1912 def icol(self, i):
-> 1913 return self._ixs(i,axis=1)
1914
1915 def _ixs(self, i, axis=0, copy=False):
/Users/234BroadWalk/pandas/pandas/core/frame.pyc in _ixs(self, i, axis, copy)
1961 return self.take(i, axis=1, convert=True)
1962
-> 1963 values = self._data.iget(i)
1964 return self._col_klass.from_array(values, index=self.index,
1965 name=label)
/Users/234BroadWalk/pandas/pandas/core/internals.pyc in iget(self, i)
1649 item = self.items[i]
1650 if self.items.is_unique:
-> 1651 return self.get(item)
1652
1653 # compute the duplicative indexer if needed
/Users/234BroadWalk/pandas/pandas/core/internals.pyc in get(self, item)
1643
1644 def get(self, item):
-> 1645 _, block = self._find_block(item)
1646 return block.get(item)
1647
TypeError: 'NoneType' object is not iterable
```
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} | 5 | "2013-05-23T12:29:21Z" | "2021-10-29T01:57:34Z" | null | CONTRIBUTOR | null | Perhaps the following [groupby aggregation](http://pandas.pydata.org/pandas-docs/dev/groupby.html#cython-optimized-aggregation-functions) should work only the numeric columns, as they would when using the dataframe:
```
In [1]: df = pd.DataFrame({'bar': {0: 1, 1: 1, 2: 1}, 'foo': {0: 0, 1: 1, 2: 2}, 'foo1': {0: 1, 1: 2, 2: 3}, 'hello': {0: 'a', 1: 'a', 2: 'a'}}, columns=['bar', 'foo', 'foo', 'hello'])
In [2]: df
Out[2]:
bar foo foo hello
0 1 0 1 a
1 1 1 2 a
2 1 2 3 a
In [3]: df.mean() # hello is ignored
Out[13]:
bar 1
foo 1
foo 2
dtype: float64
In [4]: df.groupby(level=0, axis=1).mean()
---------------------------------------------------------------------------
DataError Traceback (most recent call last)
<ipython-input-4-7c2612a8fbda> in <module>()
----> 1 df.groupby(level=0, axis=1).mean()
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/groupby.pyc in mean(self)
351 """
352 try:
--> 353 return self._cython_agg_general('mean')
354 except GroupByError:
355 raise
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/groupby.pyc in _cython_agg_general(self, how, numeric_only)
1569
1570 def _cython_agg_general(self, how, numeric_only=True):
-> 1571 new_blocks = self._cython_agg_blocks(how, numeric_only=numeric_only)
1572 return self._wrap_agged_blocks(new_blocks)
1573
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/groupby.pyc in _cython_agg_blocks(self, how, numeric_only)
1616
1617 if len(new_blocks) == 0:
-> 1618 raise DataError('No numeric types to aggregate')
1619
1620 return new_blocks
DataError: No numeric types to aggregate
```
_From this [SO question](http://stackoverflow.com/questions/16678551/pandas-create-new-dataframe-that-averages-duplicates-from-another-dataframe), where I gave very hacky workaround._
cc #3683 @jreback was this the question you were talking about? This ones related but in the sense of coming up against non unique problems... Thought I should mention it here anyway.
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https://api.github.com/repos/pandas-dev/pandas/issues/3689 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3689/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3689/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3689/events | https://github.com/pandas-dev/pandas/issues/3689 | 14,684,117 | MDU6SXNzdWUxNDY4NDExNw== | 3,689 | mean over axis=1 in not-completely-numeric frame returns all nans | {
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} | 12 | "2013-05-23T16:47:40Z" | "2021-04-11T02:04:08Z" | null | MEMBER | null | DataFrame from #3688. This might be related to that:
``` python
df = pd.DataFrame({'bar': {0: 1, 1: 1, 2: 1}, 'foo': {0: 0, 1: 1, 2: 2}, 'foo1': {0: 1, 1: 2, 2: 3}, 'hello': {0: 'a', 1: 'a', 2: 'a'}}, columns=['bar', 'foo', 'foo', 'hello'])
print df.T.sum(1) == df.sum() # fine. + is str cat
print df.T.mean(1).isnull().all() # prints True
```
I think non numeric should be dropped...
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https://api.github.com/repos/pandas-dev/pandas/issues/3690 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3690/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3690/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3690/events | https://github.com/pandas-dev/pandas/issues/3690 | 14,716,764 | MDU6SXNzdWUxNDcxNjc2NA== | 3,690 | Problem using panel.to_frame() conversion | {
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} | [] | closed | false | null | [] | null | 4 | "2013-05-24T10:59:03Z" | "2014-07-30T13:54:09Z" | "2013-05-25T09:22:30Z" | NONE | null | Hi,
Basically I wanted to kind of replicate the example given in the doc about to_frame() with data that I have.
In the doc:
panel = Panel(np.random.randn(3, 5, 4), items=['one', 'two', 'three'], major_axis=date_range('1/1/2000', periods=5), minor_axis=['a', 'b', 'c', 'd'])
gives:
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
Items axis: one to three
Major_axis axis: 2000-01-01 00:00:00 to 2000-01-05 00:00:00
Minor_axis axis: a to d
then if you use panel.to_frame().index() you will of course get:
MultiIndex
[(2000-01-01 00:00:00, a), (2000-01-01 00:00:00, b), (2000-01-01 00:00:00, c), (2000-01-01 00:00:00, d), (2000-01-02 00:00:00, a), (2000-01-02 00:00:00, b), (2000-01-02 00:00:00, c), (2000-01-02 00:00:00, d), (2000-01-03 00:00:00, a), (2000-01-03 00:00:00, b), (2000-01-03 00:00:00, c), (2000-01-03 00:00:00, d), (2000-01-04 00:00:00, a), (2000-01-04 00:00:00, b), (2000-01-04 00:00:00, c), (2000-01-04 00:00:00, d), (2000-01-05 00:00:00, a), (2000-01-05 00:00:00, b), (2000-01-05 00:00:00, c), (2000-01-05 00:00:00, d)]
I do the same with my panel data (basically I have 254 daily prices/brand (there are brand changes) at 9995 gas stations:
pd_panel_data_master
<class 'pandas.core.panel.Panel'>
Dimensions: 9995 (items) x 254 (major_axis) x 2 (minor_axis)
Items axis: 10000001 to 9700001
Major_axis axis: 20110904 to 20120514
Minor_axis axis: brand to price
then I use: pd_panel_data_master.to_frame().index
MultiIndex
[(20110904, brand), (20110905, brand), (20110906, brand), (20110907, brand), (20110908, brand), (20110909, brand), (20110910, brand), (20110911, brand), (20110912, brand), (20110913, brand), (20110914, brand), (20110915, brand), (20110916, brand), (20110917, brand), (20110918, brand), (20110919, brand), (20110920, brand), (20110921, brand), (20110922, brand), (20110923, brand), (20110924, brand), (20110925, brand), (20110926, brand), (20110927, brand), (20110928, brand), (20110929, brand), (20110930, brand), (20111001, brand), (20111002, brand), (20111003, brand), (20111004, brand), (20111005, brand), (20111006, brand), (20111007, brand), (20111008, brand), (20111009, brand), (20111010, brand), (20111011, brand), (20111012, brand), (20111013, brand), (20111014, brand), (20111015, brand), (20111016, brand), (20111017, brand), (20111018, brand), (20111019, brand), (20111020, brand), (20111021, brand), (20111022, brand), (20111023, brand), (20120326, brand), (20120327, brand), (20120328, brand), (20120329, brand), (20120330, brand), (20120331, brand), (20120401, brand), (20120402, brand), (20120403, brand), (20120404, brand), (20120405, brand), (20120406, brand), (20120407, brand), (20120408, brand), (20120409, brand), (20120410, brand), (20120411, brand), (20120412, brand), (20120413, brand), (20120414, brand), (20120415, brand), (20120416, brand), (20120417, brand), (20120418, brand), (20120419, brand), (20120420, brand), (20120421, brand), (20120422, brand), (20120423, brand), (20120424, brand), (20120425, brand), (20120426, brand), (20120427, brand), (20120428, brand), (20120429, brand), (20120430, brand), (20120501, brand), (20120502, brand), (20120503, brand), (20120504, brand), (20120505, brand), (20120506, brand), (20120507, brand), (20120508, brand), (20120509, brand), (20120510, brand), (20120511, brand), (20120512, brand), (20120513, brand), (20120514, brand)]
I don't get the (date, price) indexes... for the record, multi index object obtained is:
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 254 entries, (20110904, brand) to (20120514, brand)
Columns: 9995 entries, 10000001 to 9700001
dtypes: object(9995)
Am I missing something here, or is there a real issue?
Cheers,
Etienne
PS: I upgraded to 0.11.0 version right after posting (:x) but the issue remains essentially the same (though the form of the output slightly changes)
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} | 4 | "2013-05-24T16:59:20Z" | "2013-06-25T18:38:49Z" | "2013-05-30T16:45:00Z" | NONE | null | It would be nice if dataframe instances implemented `__nonzero__` so you could test if a dataframe is empty or not as if it were a boolean.
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Downloading/unpacking pandas
Running setup.py egg_info for package pandas
```
warning: no files found matching 'TODO.rst'
warning: no files found matching 'setupegg.py'
no previously-included directories found matching 'doc/build'
warning: no previously-included files matching '*.so' found anywhere in distribution
warning: no previously-included files matching '*.pyd' found anywhere in distribution
warning: no previously-included files matching '*.pyc' found anywhere in distribution
warning: no previously-included files matching '.git*' found anywhere in distribution
warning: no previously-included files matching '.DS_Store' found anywhere in distribution
warning: no previously-included files matching '*.png' found anywhere in distribution
```
Requirement already satisfied (use --upgrade to upgrade): python-dateutil in /Library/Python/2.7/site-packages (from pandas)
Requirement already satisfied (use --upgrade to upgrade): pytz in /Library/Python/2.7/site-packages (from pandas)
Requirement already satisfied (use --upgrade to upgrade): numpy>=1.6.1 in /Library/Python/2.7/site-packages (from pandas)
Requirement already satisfied (use --upgrade to upgrade): six in /Library/Python/2.7/site-packages (from python-dateutil->pandas)
Installing collected packages: pandas
Running setup.py install for pandas
skipping 'pandas/index.c' Cython extension (up-to-date)
building 'pandas.index' extension
llvm-gcc-4.2 -fno-strict-aliasing -fno-common -dynamic -g -Os -pipe -fno-common -fno-strict-aliasing -fwrapv -mno-fused-madd -DENABLE_DTRACE -DMACOSX -DNDEBUG -Wall -Wstrict-prototypes -Wshorten-64-to-32 -DNDEBUG -g -fwrapv -Os -Wall -Wstrict-prototypes -DENABLE_DTRACE -arch i386 -arch x86_64 -pipe -Ipandas/src/klib -Ipandas/src -I/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy/core/include -I/System/Library/Frameworks/Python.framework/Versions/2.7/include/python2.7 -c pandas/index.c -o build/temp.macosx-10.7-intel-2.7/pandas/index.o
In file included from pandas/index.c:260:
pandas/src/numpy_helper.h: In function ‘infer_type’:
pandas/src/numpy_helper.h:32: error: ‘PyDatetimeArrType_Type’ undeclared (first use in this function)
pandas/src/numpy_helper.h:32: error: (Each undeclared identifier is reported only once
pandas/src/numpy_helper.h:32: error: for each function it appears in.)
pandas/src/numpy_helper.h: In function ‘get_datetime64_value’:
pandas/src/numpy_helper.h:53: error: ‘PyDatetimeScalarObject’ undeclared (first use in this function)
pandas/src/numpy_helper.h:53: error: expected expression before ‘)’ token
pandas/src/numpy_helper.h: In function ‘is_datetime64_object’:
pandas/src/numpy_helper.h:84: error: ‘PyDatetimeArrType_Type’ undeclared (first use in this function)
pandas/src/numpy_helper.h: In function ‘is_timedelta64_object’:
pandas/src/numpy_helper.h:89: error: ‘PyTimedeltaArrType_Type’ undeclared (first use in this function)
In file included from pandas/src/klib/khash_python.h:3,
from pandas/index.c:261:
pandas/src/klib/khash.h: In function ‘kh_del_str’:
pandas/src/klib/khash.h:573: warning: statement with no effect
pandas/src/klib/khash.h: In function ‘kh_del_int32’:
pandas/src/klib/khash.h:574: warning: statement with no effect
pandas/src/klib/khash.h: In function ‘kh_del_int64’:
pandas/src/klib/khash.h:575: warning: statement with no effect
In file included from pandas/index.c:261:
pandas/src/klib/khash_python.h: In function ‘kh_del_float64’:
pandas/src/klib/khash_python.h:13: warning: statement with no effect
pandas/src/klib/khash_python.h: In function ‘kh_del_pymap’:
pandas/src/klib/khash_python.h:38: warning: statement with no effect
pandas/src/klib/khash_python.h: In function ‘kh_del_pyset’:
pandas/src/klib/khash_python.h:44: warning: statement with no effect
pandas/src/klib/khash_python.h: In function ‘kh_del_strbox’:
pandas/src/klib/khash_python.h:49: warning: statement with no effect
In file included from pandas/index.c:267:
pandas/src/datetime/np_datetime.h: At top level:
pandas/src/datetime/np_datetime.h:107: error: expected declaration specifiers or ‘...’ before ‘NPY_CASTING’
In file included from pandas/index.c:268:
pandas/src/datetime/np_datetime_strings.h:46: error: expected declaration specifiers or ‘...’ before ‘NPY_CASTING’
pandas/src/datetime/np_datetime_strings.h:84: error: expected declaration specifiers or ‘...’ before ‘NPY_CASTING’
pandas/index.c:490: error: expected ‘=’, ‘,’, ‘;’, ‘asm’ or ‘**attribute**’ before ‘**pyx_t_5numpy_float16_t’
pandas/index.c: In function ‘__pyx_pf_6pandas_5index_get_value_at’:
pandas/index.c:1928: error: ‘NPY_DATETIME’ undeclared (first use in this function)
pandas/index.c: In function ‘__pyx_f_6pandas_5index_11IndexEngine_get_value’:
pandas/index.c:2424: error: ‘NPY_DATETIME’ undeclared (first use in this function)
pandas/index.c: In function ‘__pyx_f_6pandas_5index_convert_scalar’:
pandas/index.c:8594: error: ‘NPY_DATETIME’ undeclared (first use in this function)
pandas/index.c: In function ‘__pyx_f_8datetime__cstring_to_dts’:
pandas/index.c:9395: error: ‘NPY_UNSAFE_CASTING’ undeclared (first use in this function)
pandas/index.c:9395: warning: passing argument 5 of ‘parse_iso_8601_datetime’ from incompatible pointer type
pandas/index.c:9395: warning: passing argument 6 of ‘parse_iso_8601_datetime’ from incompatible pointer type
pandas/index.c:9395: warning: passing argument 7 of ‘parse_iso_8601_datetime’ from incompatible pointer type
pandas/index.c:9395: error: too many arguments to function ‘parse_iso_8601_datetime’
pandas/index.c: In function ‘__pyx_f_8datetime__dts_to_pydatetime’:
pandas/index.c:9499: warning: implicit conversion shortens 64-bit value into a 32-bit value
In file included from pandas/index.c:260:
pandas/src/numpy_helper.h: In function ‘infer_type’:
pandas/src/numpy_helper.h:32: error: ‘PyDatetimeArrType_Type’ undeclared (first use in this function)
pandas/src/numpy_helper.h:32: error: (Each undeclared identifier is reported only once
pandas/src/numpy_helper.h:32: error: for each function it appears in.)
pandas/src/numpy_helper.h: In function ‘get_datetime64_value’:
pandas/src/numpy_helper.h:53: error: ‘PyDatetimeScalarObject’ undeclared (first use in this function)
pandas/src/numpy_helper.h:53: error: expected expression before ‘)’ token
pandas/src/numpy_helper.h: In function ‘is_datetime64_object’:
pandas/src/numpy_helper.h:84: error: ‘PyDatetimeArrType_Type’ undeclared (first use in this function)
pandas/src/numpy_helper.h: In function ‘is_timedelta64_object’:
pandas/src/numpy_helper.h:89: error: ‘PyTimedeltaArrType_Type’ undeclared (first use in this function)
In file included from pandas/src/klib/khash_python.h:3,
from pandas/index.c:261:
pandas/src/klib/khash.h: In function ‘kh_resize_str’:
pandas/src/klib/khash.h:573: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h:573: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h:573: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h: In function ‘kh_put_str’:
pandas/src/klib/khash.h:573: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h:573: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h: In function ‘kh_del_str’:
pandas/src/klib/khash.h:573: warning: statement with no effect
pandas/src/klib/khash.h: In function ‘kh_resize_int32’:
pandas/src/klib/khash.h:574: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h:574: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h:574: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h: In function ‘kh_put_int32’:
pandas/src/klib/khash.h:574: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h:574: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h: In function ‘kh_del_int32’:
pandas/src/klib/khash.h:574: warning: statement with no effect
pandas/src/klib/khash.h: In function ‘kh_resize_int64’:
pandas/src/klib/khash.h:575: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h:575: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h:575: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h: In function ‘kh_put_int64’:
pandas/src/klib/khash.h:575: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h:575: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h: In function ‘kh_del_int64’:
pandas/src/klib/khash.h:575: warning: statement with no effect
In file included from pandas/index.c:261:
pandas/src/klib/khash_python.h: In function ‘kh_get_float64’:
pandas/src/klib/khash_python.h:13: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h: In function ‘kh_resize_float64’:
pandas/src/klib/khash_python.h:13: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:13: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:13: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:13: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h: In function ‘kh_put_float64’:
pandas/src/klib/khash_python.h:13: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:13: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:13: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h: In function ‘kh_del_float64’:
pandas/src/klib/khash_python.h:13: warning: statement with no effect
pandas/src/klib/khash_python.h: In function ‘kh_get_pymap’:
pandas/src/klib/khash_python.h:38: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h: In function ‘kh_resize_pymap’:
pandas/src/klib/khash_python.h:38: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:38: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:38: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:38: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h: In function ‘kh_put_pymap’:
pandas/src/klib/khash_python.h:38: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:38: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:38: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h: In function ‘kh_del_pymap’:
pandas/src/klib/khash_python.h:38: warning: statement with no effect
pandas/src/klib/khash_python.h: In function ‘kh_get_pyset’:
pandas/src/klib/khash_python.h:44: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h: In function ‘kh_resize_pyset’:
pandas/src/klib/khash_python.h:44: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:44: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:44: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:44: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h: In function ‘kh_put_pyset’:
pandas/src/klib/khash_python.h:44: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:44: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:44: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h: In function ‘kh_del_pyset’:
pandas/src/klib/khash_python.h:44: warning: statement with no effect
pandas/src/klib/khash_python.h: In function ‘kh_resize_strbox’:
pandas/src/klib/khash_python.h:49: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:49: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:49: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h: In function ‘kh_put_strbox’:
pandas/src/klib/khash_python.h:49: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:49: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h: In function ‘kh_del_strbox’:
pandas/src/klib/khash_python.h:49: warning: statement with no effect
In file included from pandas/index.c:267:
pandas/src/datetime/np_datetime.h: At top level:
pandas/src/datetime/np_datetime.h:107: error: expected declaration specifiers or ‘...’ before ‘NPY_CASTING’
In file included from pandas/index.c:268:
pandas/src/datetime/np_datetime_strings.h:46: error: expected declaration specifiers or ‘...’ before ‘NPY_CASTING’
pandas/src/datetime/np_datetime_strings.h:84: error: expected declaration specifiers or ‘...’ before ‘NPY_CASTING’
pandas/index.c:490: error: expected ‘=’, ‘,’, ‘;’, ‘asm’ or ‘__attribute**’ before ‘**pyx_t_5numpy_float16_t’
pandas/index.c: In function ‘__pyx_pf_6pandas_5index_get_value_at’:
pandas/index.c:1928: error: ‘NPY_DATETIME’ undeclared (first use in this function)
pandas/index.c: In function ‘__pyx_f_6pandas_5index_11IndexEngine_get_value’:
pandas/index.c:2424: error: ‘NPY_DATETIME’ undeclared (first use in this function)
pandas/index.c: In function ‘__pyx_f_6pandas_5index_11IndexEngine__maybe_get_bool_indexer’:
pandas/index.c:3733: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/index.c: In function ‘__pyx_f_6pandas_5index_11Int64Engine__maybe_get_bool_indexer’:
pandas/index.c:5724: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/index.c: In function ‘__pyx_f_6pandas_5index_convert_scalar’:
pandas/index.c:8594: error: ‘NPY_DATETIME’ undeclared (first use in this function)
pandas/index.c: In function ‘__pyx_f_8datetime__string_to_dts’:
pandas/index.c:9312: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/index.c: In function ‘__pyx_f_8datetime__cstring_to_dts’:
pandas/index.c:9395: error: ‘NPY_UNSAFE_CASTING’ undeclared (first use in this function)
pandas/index.c:9395: warning: passing argument 5 of ‘parse_iso_8601_datetime’ from incompatible pointer type
pandas/index.c:9395: warning: passing argument 6 of ‘parse_iso_8601_datetime’ from incompatible pointer type
pandas/index.c:9395: warning: passing argument 7 of ‘parse_iso_8601_datetime’ from incompatible pointer type
pandas/index.c:9395: error: too many arguments to function ‘parse_iso_8601_datetime’
pandas/index.c: In function ‘__pyx_f_8datetime__dts_to_pydatetime’:
pandas/index.c:9499: warning: implicit conversion shortens 64-bit value into a 32-bit value
lipo: can't open input file: /var/tmp//ccIxVO4R.out (No such file or directory)
error: command 'llvm-gcc-4.2' failed with exit status 1
Complete output from command /usr/bin/python -c "import setuptools;__file**='/private/tmp/pip-build-root/pandas/setup.py';exec(compile(open(**file**).read().replace('\r\n', '\n'), **file**, 'exec'))" install --record /tmp/pip-NIyRvg-record/install-record.txt --single-version-externally-managed:
running install
running build
running build_py
copying pandas/version.py -> build/lib.macosx-10.7-intel-2.7/pandas
running build_ext
skipping 'pandas/index.c' Cython extension (up-to-date)
building 'pandas.index' extension
llvm-gcc-4.2 -fno-strict-aliasing -fno-common -dynamic -g -Os -pipe -fno-common -fno-strict-aliasing -fwrapv -mno-fused-madd -DENABLE_DTRACE -DMACOSX -DNDEBUG -Wall -Wstrict-prototypes -Wshorten-64-to-32 -DNDEBUG -g -fwrapv -Os -Wall -Wstrict-prototypes -DENABLE_DTRACE -arch i386 -arch x86_64 -pipe -Ipandas/src/klib -Ipandas/src -I/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy/core/include -I/System/Library/Frameworks/Python.framework/Versions/2.7/include/python2.7 -c pandas/index.c -o build/temp.macosx-10.7-intel-2.7/pandas/index.o
In file included from pandas/index.c:260:
pandas/src/numpy_helper.h: In function ‘infer_type’:
pandas/src/numpy_helper.h:32: error: ‘PyDatetimeArrType_Type’ undeclared (first use in this function)
pandas/src/numpy_helper.h:32: error: (Each undeclared identifier is reported only once
pandas/src/numpy_helper.h:32: error: for each function it appears in.)
pandas/src/numpy_helper.h: In function ‘get_datetime64_value’:
pandas/src/numpy_helper.h:53: error: ‘PyDatetimeScalarObject’ undeclared (first use in this function)
pandas/src/numpy_helper.h:53: error: expected expression before ‘)’ token
pandas/src/numpy_helper.h: In function ‘is_datetime64_object’:
pandas/src/numpy_helper.h:84: error: ‘PyDatetimeArrType_Type’ undeclared (first use in this function)
pandas/src/numpy_helper.h: In function ‘is_timedelta64_object’:
pandas/src/numpy_helper.h:89: error: ‘PyTimedeltaArrType_Type’ undeclared (first use in this function)
In file included from pandas/src/klib/khash_python.h:3,
```
from pandas/index.c:261:
```
pandas/src/klib/khash.h: In function ‘kh_del_str’:
pandas/src/klib/khash.h:573: warning: statement with no effect
pandas/src/klib/khash.h: In function ‘kh_del_int32’:
pandas/src/klib/khash.h:574: warning: statement with no effect
pandas/src/klib/khash.h: In function ‘kh_del_int64’:
pandas/src/klib/khash.h:575: warning: statement with no effect
In file included from pandas/index.c:261:
pandas/src/klib/khash_python.h: In function ‘kh_del_float64’:
pandas/src/klib/khash_python.h:13: warning: statement with no effect
pandas/src/klib/khash_python.h: In function ‘kh_del_pymap’:
pandas/src/klib/khash_python.h:38: warning: statement with no effect
pandas/src/klib/khash_python.h: In function ‘kh_del_pyset’:
pandas/src/klib/khash_python.h:44: warning: statement with no effect
pandas/src/klib/khash_python.h: In function ‘kh_del_strbox’:
pandas/src/klib/khash_python.h:49: warning: statement with no effect
In file included from pandas/index.c:267:
pandas/src/datetime/np_datetime.h: At top level:
pandas/src/datetime/np_datetime.h:107: error: expected declaration specifiers or ‘...’ before ‘NPY_CASTING’
In file included from pandas/index.c:268:
pandas/src/datetime/np_datetime_strings.h:46: error: expected declaration specifiers or ‘...’ before ‘NPY_CASTING’
pandas/src/datetime/np_datetime_strings.h:84: error: expected declaration specifiers or ‘...’ before ‘NPY_CASTING’
pandas/index.c:490: error: expected ‘=’, ‘,’, ‘;’, ‘asm’ or ‘**attribute**’ before ‘__pyx_t_5numpy_float16_t’
pandas/index.c: In function ‘__pyx_pf_6pandas_5index_get_value_at’:
pandas/index.c:1928: error: ‘NPY_DATETIME’ undeclared (first use in this function)
pandas/index.c: In function ‘__pyx_f_6pandas_5index_11IndexEngine_get_value’:
pandas/index.c:2424: error: ‘NPY_DATETIME’ undeclared (first use in this function)
pandas/index.c: In function ‘__pyx_f_6pandas_5index_convert_scalar’:
pandas/index.c:8594: error: ‘NPY_DATETIME’ undeclared (first use in this function)
pandas/index.c: In function ‘__pyx_f_8datetime__cstring_to_dts’:
pandas/index.c:9395: error: ‘NPY_UNSAFE_CASTING’ undeclared (first use in this function)
pandas/index.c:9395: warning: passing argument 5 of ‘parse_iso_8601_datetime’ from incompatible pointer type
pandas/index.c:9395: warning: passing argument 6 of ‘parse_iso_8601_datetime’ from incompatible pointer type
pandas/index.c:9395: warning: passing argument 7 of ‘parse_iso_8601_datetime’ from incompatible pointer type
pandas/index.c:9395: error: too many arguments to function ‘parse_iso_8601_datetime’
pandas/index.c: In function ‘__pyx_f_8datetime__dts_to_pydatetime’:
pandas/index.c:9499: warning: implicit conversion shortens 64-bit value into a 32-bit value
In file included from pandas/index.c:260:
pandas/src/numpy_helper.h: In function ‘infer_type’:
pandas/src/numpy_helper.h:32: error: ‘PyDatetimeArrType_Type’ undeclared (first use in this function)
pandas/src/numpy_helper.h:32: error: (Each undeclared identifier is reported only once
pandas/src/numpy_helper.h:32: error: for each function it appears in.)
pandas/src/numpy_helper.h: In function ‘get_datetime64_value’:
pandas/src/numpy_helper.h:53: error: ‘PyDatetimeScalarObject’ undeclared (first use in this function)
pandas/src/numpy_helper.h:53: error: expected expression before ‘)’ token
pandas/src/numpy_helper.h: In function ‘is_datetime64_object’:
pandas/src/numpy_helper.h:84: error: ‘PyDatetimeArrType_Type’ undeclared (first use in this function)
pandas/src/numpy_helper.h: In function ‘is_timedelta64_object’:
pandas/src/numpy_helper.h:89: error: ‘PyTimedeltaArrType_Type’ undeclared (first use in this function)
In file included from pandas/src/klib/khash_python.h:3,
```
from pandas/index.c:261:
```
pandas/src/klib/khash.h: In function ‘kh_resize_str’:
pandas/src/klib/khash.h:573: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h:573: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h:573: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h: In function ‘kh_put_str’:
pandas/src/klib/khash.h:573: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h:573: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h: In function ‘kh_del_str’:
pandas/src/klib/khash.h:573: warning: statement with no effect
pandas/src/klib/khash.h: In function ‘kh_resize_int32’:
pandas/src/klib/khash.h:574: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h:574: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h:574: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h: In function ‘kh_put_int32’:
pandas/src/klib/khash.h:574: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h:574: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h: In function ‘kh_del_int32’:
pandas/src/klib/khash.h:574: warning: statement with no effect
pandas/src/klib/khash.h: In function ‘kh_resize_int64’:
pandas/src/klib/khash.h:575: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h:575: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h:575: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h: In function ‘kh_put_int64’:
pandas/src/klib/khash.h:575: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h:575: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash.h: In function ‘kh_del_int64’:
pandas/src/klib/khash.h:575: warning: statement with no effect
In file included from pandas/index.c:261:
pandas/src/klib/khash_python.h: In function ‘kh_get_float64’:
pandas/src/klib/khash_python.h:13: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h: In function ‘kh_resize_float64’:
pandas/src/klib/khash_python.h:13: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:13: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:13: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:13: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h: In function ‘kh_put_float64’:
pandas/src/klib/khash_python.h:13: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:13: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:13: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h: In function ‘kh_del_float64’:
pandas/src/klib/khash_python.h:13: warning: statement with no effect
pandas/src/klib/khash_python.h: In function ‘kh_get_pymap’:
pandas/src/klib/khash_python.h:38: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h: In function ‘kh_resize_pymap’:
pandas/src/klib/khash_python.h:38: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:38: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:38: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:38: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h: In function ‘kh_put_pymap’:
pandas/src/klib/khash_python.h:38: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:38: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:38: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h: In function ‘kh_del_pymap’:
pandas/src/klib/khash_python.h:38: warning: statement with no effect
pandas/src/klib/khash_python.h: In function ‘kh_get_pyset’:
pandas/src/klib/khash_python.h:44: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h: In function ‘kh_resize_pyset’:
pandas/src/klib/khash_python.h:44: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:44: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:44: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:44: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h: In function ‘kh_put_pyset’:
pandas/src/klib/khash_python.h:44: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:44: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:44: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h: In function ‘kh_del_pyset’:
pandas/src/klib/khash_python.h:44: warning: statement with no effect
pandas/src/klib/khash_python.h: In function ‘kh_resize_strbox’:
pandas/src/klib/khash_python.h:49: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:49: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:49: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h: In function ‘kh_put_strbox’:
pandas/src/klib/khash_python.h:49: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h:49: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/src/klib/khash_python.h: In function ‘kh_del_strbox’:
pandas/src/klib/khash_python.h:49: warning: statement with no effect
In file included from pandas/index.c:267:
pandas/src/datetime/np_datetime.h: At top level:
pandas/src/datetime/np_datetime.h:107: error: expected declaration specifiers or ‘...’ before ‘NPY_CASTING’
In file included from pandas/index.c:268:
pandas/src/datetime/np_datetime_strings.h:46: error: expected declaration specifiers or ‘...’ before ‘NPY_CASTING’
pandas/src/datetime/np_datetime_strings.h:84: error: expected declaration specifiers or ‘...’ before ‘NPY_CASTING’
pandas/index.c:490: error: expected ‘=’, ‘,’, ‘;’, ‘asm’ or ‘**attribute**’ before ‘__pyx_t_5numpy_float16_t’
pandas/index.c: In function ‘__pyx_pf_6pandas_5index_get_value_at’:
pandas/index.c:1928: error: ‘NPY_DATETIME’ undeclared (first use in this function)
pandas/index.c: In function ‘__pyx_f_6pandas_5index_11IndexEngine_get_value’:
pandas/index.c:2424: error: ‘NPY_DATETIME’ undeclared (first use in this function)
pandas/index.c: In function ‘__pyx_f_6pandas_5index_11IndexEngine__maybe_get_bool_indexer’:
pandas/index.c:3733: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/index.c: In function ‘__pyx_f_6pandas_5index_11Int64Engine__maybe_get_bool_indexer’:
pandas/index.c:5724: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/index.c: In function ‘__pyx_f_6pandas_5index_convert_scalar’:
pandas/index.c:8594: error: ‘NPY_DATETIME’ undeclared (first use in this function)
pandas/index.c: In function ‘__pyx_f_8datetime__string_to_dts’:
pandas/index.c:9312: warning: implicit conversion shortens 64-bit value into a 32-bit value
pandas/index.c: In function ‘__pyx_f_8datetime__cstring_to_dts’:
pandas/index.c:9395: error: ‘NPY_UNSAFE_CASTING’ undeclared (first use in this function)
pandas/index.c:9395: warning: passing argument 5 of ‘parse_iso_8601_datetime’ from incompatible pointer type
pandas/index.c:9395: warning: passing argument 6 of ‘parse_iso_8601_datetime’ from incompatible pointer type
pandas/index.c:9395: warning: passing argument 7 of ‘parse_iso_8601_datetime’ from incompatible pointer type
pandas/index.c:9395: error: too many arguments to function ‘parse_iso_8601_datetime’
pandas/index.c: In function ‘__pyx_f_8datetime__dts_to_pydatetime’:
pandas/index.c:9499: warning: implicit conversion shortens 64-bit value into a 32-bit value
lipo: can't open input file: /var/tmp//ccIxVO4R.out (No such file or directory)
error: command 'llvm-gcc-4.2' failed with exit status 1
---
I tried to update everything under the sun: numpy (1.7.1), pip (1.3.1), XCode command line tools (4.6.2)..
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https://api.github.com/repos/pandas-dev/pandas/issues/3693 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3693/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3693/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3693/events | https://github.com/pandas-dev/pandas/pull/3693 | 14,750,297 | MDExOlB1bGxSZXF1ZXN0NTkyODQ0Mw== | 3,693 | CLN: added io.api for i/o importing functions | {
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} | 12 | "2013-05-25T00:40:50Z" | "2014-07-16T08:10:24Z" | "2013-05-30T16:32:27Z" | CONTRIBUTOR | null | closes most of #3411 (all except the deprecation of `from_csv`)
- moved excel functionaility out of io.parsers to io.excel
added read_excel top-level function
aliases from pandas.io.excel
- added read_stata top-level function, to_stata DataFrame method
aliases from pandas.io.stata
removed read_dta (replace by read_stata)
- added read_sql top-level function, to_sql DataFrame method
aliases from pandas.io.sql
DOC: doc updates for all the above and intro section to io.rst
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} | 0 | "2013-05-25T23:36:33Z" | "2014-07-16T08:10:25Z" | "2013-06-02T20:38:25Z" | MEMBER | null | Was not able to reproduce the issue. #3503
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} | 7 | "2013-05-25T23:53:22Z" | "2016-10-12T23:03:50Z" | "2014-01-28T00:52:46Z" | MEMBER | null | from @jreback's clever data alignment trick: http://stackoverflow.com/questions/16637171/pandas-reshaping-data
not sure where this should go, it does come up quite frequently, e.g. the Movielens data set https://raw.github.com/pydata/pydata-book/master/ch02/movielens/movies.dat:
```
df = read_table('https://raw.github.com/pydata/pydata-book/master/ch02/movielens/movies.dat', header=None, sep='::')
In [10]: genres = df[2].str.split('|')
In [11]: genres
Out[11]:
0 [Animation, Children's, Comedy]
1 [Adventure, Children's, Fantasy]
2 [Comedy, Romance]
3 [Comedy, Drama]
4 [Comedy]
5 [Action, Crime, Thriller]
6 [Comedy, Romance]
7 [Adventure, Children's]
8 [Action]
9 [Action, Adventure, Thriller]
10 [Comedy, Drama, Romance]
11 [Comedy, Horror]
12 [Animation, Children's]
13 [Drama]
14 [Action, Adventure, Romance]
...
3868 [Horror]
3869 [Horror]
3870 [Horror]
3871 [Horror]
3872 [Horror]
3873 [Comedy]
3874 [Comedy, Drama]
3875 [Adventure, Animation, Children's]
3876 [Action, Drama, Thriller]
3877 [Thriller]
3878 [Comedy]
3879 [Drama]
3880 [Drama]
3881 [Drama]
3882 [Drama, Thriller]
Name: 2, Length: 3883, dtype: object
In [12]: dummies = genres.apply(lambda x: Series(1, index=x)).fillna(0)
In [13]: dummies[:4].T
Out[13]:
0 1 2 3
Action 0 0 0 0
Adventure 0 1 0 0
Animation 1 0 0 0
Children's 1 1 0 0
Comedy 1 0 1 1
Crime 0 0 0 0
Documentary 0 0 0 0
Drama 0 0 0 1
Fantasy 0 1 0 0
Film-Noir 0 0 0 0
Horror 0 0 0 0
Musical 0 0 0 0
Mystery 0 0 0 0
Romance 0 0 1 0
Sci-Fi 0 0 0 0
Thriller 0 0 0 0
War 0 0 0 0
Western 0 0 0 0
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3697 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3697/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3697/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3697/events | https://github.com/pandas-dev/pandas/issues/3697 | 14,809,979 | MDU6SXNzdWUxNDgwOTk3OQ== | 3,697 | BUG? when incrementing .loc[]-view | {
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} | [] | closed | false | null | [] | null | 6 | "2013-05-27T20:34:28Z" | "2013-05-28T17:59:41Z" | "2013-05-28T00:15:44Z" | CONTRIBUTOR | null | Am I doing something wrong with indexing here?
PS. on the second thought it looks related to #3492, but I don't have a dev environment at hand to test and it fails for some reason when trying to build pandas via pip. If that's the case and it's already fixed, my apologies and feel free to close this one.
Steps to reproduce:
``` python
>>> df = DataFrame(index=np.arange(10))
>>> df['foobar'] = False
>>> df['a'] = np.arange(10)
>>> df
foobar a
0 False 0
1 False 1
2 False 2
3 False 3
4 False 4
5 False 5
6 False 6
7 False 7
8 False 8
9 False 9
# This works as expected
>>> df.a[df.a % 2 == 0] += 10
>>> df
foobar a
0 False 10
1 False 1
2 False 12
3 False 3
4 False 14
5 False 5
6 False 16
7 False 7
8 False 18
9 False 9
# This doesn't work, but expected due to copying on bool-indexing
>>> df[df.a % 2 == 0].a += 100
>>> df
foobar a
0 False 10
1 False 1
2 False 12
3 False 3
4 False 14
5 False 5
6 False 16
7 False 7
8 False 18
9 False 9
# But what does this?
>>> df.loc[df.a % 2 == 0, 'a'] += 1000
>>> df
foobar a
0 False 1010
1 False 1
2 False 1010
3 False 3
4 False 1010
5 False 5
6 False 1010
7 False 7
8 False 1010
9 False 9
>>> pandas.__version__
'0.11.0'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3698 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3698/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3698/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3698/events | https://github.com/pandas-dev/pandas/issues/3698 | 14,810,182 | MDU6SXNzdWUxNDgxMDE4Mg== | 3,698 | DataFrame apply change return type from Series to DataFrame if result is empty | {
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} | 9 | "2013-05-27T20:44:10Z" | "2016-02-17T13:38:49Z" | "2016-02-17T13:38:49Z" | NONE | null | pd.version.version ## return '0.11.0'
x = pd.DataFrame({'a': range(10), 'b': range(10)})
type(x.apply(lambda x: x['a'] + x['b'], 1)) # <class 'pandas.core.series.Series'>
x['c'] = x.apply(lambda x: x['a'] + x['b'], 1) ## works
x = x[x['a'] < 0]
type(x.apply(lambda x: x['a'] + x['b'], 1)) # <class 'pandas.core.frame.DataFrame'>
x['c'] = x.apply(lambda x: x['a'] + x['b'], 1) ## FAILS
Code above is the problem for quite common usage of apply to create new dataframe column using func applyed on row. It fails for empty dataframe and works for non-empty one.
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https://api.github.com/repos/pandas-dev/pandas/issues/3699 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3699/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3699/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3699/events | https://github.com/pandas-dev/pandas/issues/3699 | 14,812,788 | MDU6SXNzdWUxNDgxMjc4OA== | 3,699 | Failure to write large dataframe to file: "SystemError: error return without exception set" | {
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} | [] | closed | false | null | [] | null | 2 | "2013-05-27T23:03:12Z" | "2013-06-06T12:04:27Z" | "2013-06-06T12:04:27Z" | NONE | null | I have a simple but large dataframe. I can write it to disk using .to_csv(). The file size is then 11GB. It looks like this:
```
TLID2,NEAR_DIST,TLID1,TLID2,yr1,yr2,yr2_older
104519610,487,104519631,104519610,1952.0,1912.0,True
104519610,418,104519632,104519610,,1912.0,False
104519610,747,104519451,104519610,,1912.0,False
104519610,559,104519645,104519610,,1912.0,False
104519610,979,104582619,104519610,,1912.0,False
...
```
But when I use DataFrame.save(), it fails after 1.9GB with a strange error:
```
548 prox_table.to_csv(output_path+'proxim_table_all.csv')
--> 549 prox_table.save(output_path+'proxim_table_all.pandas')
550
lib64/python/pandas-0.11.0-py2.6-linux-x86_64.egg/pandas/core/generic.pyc in save(self, path)
26
27 def save(self, path):
---> 28 com.save(self, path)
29
30 @classmethod
lib64/python/pandas-0.11.0-py2.6-linux-x86_64.egg/pandas/core/common.pyc in save(obj, path)
1562 f = open(path, 'wb')
1563 try:
-> 1564 pickle.dump(obj, f, protocol=pickle.HIGHEST_PROTOCOL)
1565 finally:
1566 f.close()
SystemError: error return without exception set
> /lib64/python/pandas-0.11.0-py2.6-linux-x86_64.egg/pandas/core/common.py(1566)save()
1565 finally:
-> 1566 f.close()
1567
```
The df looks like:
```
ipdb> prox_table.dtypes
NEAR_DIST int16
TLID1 int64
TLID2 int64
yr1 float16
yr2 float16
yr2_older bool
dtype: object
ipdb> prox_table
<class 'pandas.core.frame.DataFrame'>
Int64Index: 246425908 entries, 104519610 to 21973121
Columns: 6 entries, NEAR_DIST to yr2_older
dtypes: bool(1), float16(2), int16(1), int64(2)
```
I'm using pandas v 0.11.0. The machine is running RHEL 6.1 and has 384GB of RAM and plenty of disk space.
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https://api.github.com/repos/pandas-dev/pandas/issues/3700 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3700/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3700/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3700/events | https://github.com/pandas-dev/pandas/pull/3700 | 14,819,959 | MDExOlB1bGxSZXF1ZXN0NTk1NTAxMg== | 3,700 | DOC: use na_rep not nanRep in .to_csv() | {
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} | [] | closed | false | null | [] | null | 1 | "2013-05-28T05:03:16Z" | "2015-08-15T22:13:32Z" | "2013-05-30T00:53:56Z" | CONTRIBUTOR | null | nanRep works but is deprecated and emits a FutureWarning.
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