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https://api.github.com/repos/pandas-dev/pandas/issues/5001 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5001/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5001/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5001/events | https://github.com/pandas-dev/pandas/pull/5001 | 20,138,013 | MDExOlB1bGxSZXF1ZXN0ODY0NDI5Mg== | 5,001 | API: Remove set_printoptions/reset_printoptions (:issue:3046) | {
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} | 0 | 2013-09-26T21:24:22Z | 2014-06-14T13:53:48Z | 2013-09-27T00:36:48Z | CONTRIBUTOR | null | closes #3046
closes #3215
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https://api.github.com/repos/pandas-dev/pandas/issues/5002 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5002/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5002/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5002/events | https://github.com/pandas-dev/pandas/issues/5002 | 20,142,879 | MDU6SXNzdWUyMDE0Mjg3OQ== | 5,002 | Error resampling after selecting data | {
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} | [] | closed | false | null | [] | null | 1 | 2013-09-26T23:10:47Z | 2013-09-27T00:20:56Z | 2013-09-27T00:20:56Z | NONE | null | Hi,
I am using pandas version 0.10.1 and numpy version 1.7.1.
I get a missing row if I select a subset of the data before resampling. The following script reproduces the problem with simple data.
``` python
import pandas as pd
from StringIO import StringIO
f = StringIO("""\
time,row_id,a,b
2011-06-03 00:00:05,34083,1.8,1.8
2011-06-03 00:01:05,34083,1.8,1.7
2011-06-03 00:02:05,34083,1.6,1.6
2011-06-03 00:03:05,34083,1.5,1.4
2011-06-03 00:04:05,34083,1.7,1.7
2011-06-03 00:05:05,34083,1.8,1.7
2011-06-03 00:06:05,34083,1.7,1.7
2011-06-03 00:07:05,34083,1.6,1.6
2011-06-03 00:25:39,33909,0.9,0.2
2011-06-03 00:27:39,33909,1.2,1.2
2011-06-03 00:28:39,33909,1.2,1
2011-06-03 00:29:39,33909,1.1,1.1
2011-06-03 00:30:39,33909,1.4,1.4
2011-06-03 00:31:39,33909,1.4,1.3
2011-06-03 00:32:39,33909,1.4,1.3
2011-06-03 00:33:39,33909,1.3,1.3
2011-06-03 00:34:39,33909,1.3,1.3
2011-06-03 00:35:39,33909,1.3,1.2
""")
data = pd.read_csv(f, parse_dates=True, index_col=0)
print "This is the correct resampling result."
print "Notice the starting time is the same as the first line in the sample data"
print data.resample('s').head()
print
print "Now, split the data on each unique row number"
ids = data.row_id.unique()
data_split_by_id = [(row_id, data[data.row_id == row_id]) for row_id in ids]
data = data_split_by_id[0][1]
print "The data still looks as expected"
print data
print
print "But resampling gives the wrong result"
print "Notice that the first line is missing"
print data.resample('s').head()
print
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5003 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5003/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5003/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5003/events | https://github.com/pandas-dev/pandas/pull/5003 | 20,149,941 | MDExOlB1bGxSZXF1ZXN0ODY1MTA1MQ== | 5,003 | BUG: wrong index name during read_csv if using usecols | {
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} | [] | closed | false | null | [] | null | 5 | 2013-09-27T03:26:01Z | 2014-07-29T17:20:31Z | 2013-09-27T12:39:31Z | CONTRIBUTOR | null | Closes #4201
If user passes usecols and not names, then ensure that the
inferred names refer to the used columns, not the document's
columns.
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https://api.github.com/repos/pandas-dev/pandas/issues/5004 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5004/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5004/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5004/events | https://github.com/pandas-dev/pandas/pull/5004 | 20,150,571 | MDExOlB1bGxSZXF1ZXN0ODY1MTM3NQ== | 5,004 | ENH: Support for "52–53-week fiscal year" / "4–4–5 calendar" and LastWeekOfMonth DateOffset | {
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} | 98 | 2013-09-27T03:55:08Z | 2014-06-12T15:33:59Z | 2013-10-22T20:37:52Z | CONTRIBUTOR | null | Closes #4511 and #4637
- Added `LastWeekOfMonth` DateOffset #4637
- Added `FY5253`, and `FY5253Quarter` DateOffsets #4511
- Improved error handling in `get_freq_code`, `_period_str_to_code` and `_base_and_stride`
- Fix issue with datetime - Timestamp
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} | [] | closed | false | null | [] | null | 1 | 2013-09-27T06:25:12Z | 2013-09-27T12:09:17Z | 2013-09-27T12:09:10Z | NONE | null | I have a problem with pandas dropping 1st row when I am resampling certain data. Please see the example below. Note that if you advance the last timestamp forward by 1 second it works as expected.
I am using pandas 0.10.1
``` python
import pandas as pd
from datetime import datetime
from StringIO import StringIO
f = StringIO('''\
time,value
2011-06-03 00:00:05,0
2011-06-03 00:01:05,1
2011-06-03 00:02:05,2
''')
series = pd.read_csv(f, parse_dates=True, index_col=0)['value']
print series
# time
#2011-06-03 00:00:05 0
#2011-06-03 00:01:05 1
#2011-06-03 00:02:05 2
# Name: value
# Problem resampling: 1st sample is missing
print series.resample('s')
# time
#2011-06-03 00:00:06 NaN
#2011-06-03 00:00:07 NaN
#2011-06-03 00:00:08 NaN
#2011-06-03 00:00:09 NaN
# ...
#2011-06-03 00:01:52 NaN
#2011-06-03 00:02:03 NaN
#2011-06-03 00:02:04 NaN
#2011-06-03 00:02:05 2
#2011-06-03 00:02:06 NaN
# Freq: S, Name: value, Length: 121
```
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} | 8 | 2013-09-27T15:08:36Z | 2013-10-07T00:10:08Z | 2013-10-07T00:10:08Z | NONE | null | There is an inconsistency in the returned (python) types when indexing .iloc with an integer array. The result should always be a sequence (Series), but if the array contains exactly one index, it returns a scalar value:
``` python
>>> import pandas, numpy
>>> column = pandas.Series(numpy.arange(10))
>>> indices, = (column > 4).nonzero()
>>> column.iloc[indices]
5 5
6 6
7 7
8 8
9 9
dtype: int64
>>> indices, = (column == 5).nonzero()
>>> column.iloc[indices]
5
>>> indices, = (column == 99).nonzero()
>>> column.iloc[indices]
Series([], dtype: int64)
```
I am working around that using `numpy.atleast_1d`, but it's ugly.
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} | 6 | 2013-09-27T17:00:53Z | 2018-07-26T09:00:25Z | 2014-01-29T04:02:41Z | MEMBER | null | there aren't any tests for these (yet)
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} | 2 | 2013-09-27T17:18:48Z | 2019-10-09T05:47:29Z | 2019-10-09T05:47:29Z | NONE | null | Hi everybody,
I worked today with a lot of unicode data and deeply missed the option to define a default_charset when writing a dataframe to a new sql table. I did not know that pandas uses "latin-1" as default and ran into various strange encoding problems such as "Data too long for column" when inserting my unicode data. The exception was raised despite the explicit definition of utf-8 in the connection object.
As a workaround I created the table myself and let pandas only append the DataFrame. However, it would be much better if pandas could create the tables itself.
Best regards
Andy
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} | 0 | 2013-09-27T19:21:02Z | 2014-07-16T08:31:45Z | 2013-09-27T19:55:18Z | CONTRIBUTOR | null | closes #3717
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} | 1 | 2013-09-27T20:32:30Z | 2014-06-24T15:20:06Z | 2013-09-27T21:29:12Z | MEMBER | null | closes #4608
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} | 21 | 2013-09-27T21:15:14Z | 2014-06-18T01:33:02Z | 2014-02-04T09:30:05Z | CONTRIBUTOR | null | Closes #4987.
The problem is that all formatters always call `_make_fixed_width`. So, the only solution I found is just to strip the unnecessary whitespace.
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} | 14 | 2013-09-27T23:39:07Z | 2014-06-25T08:10:52Z | 2013-10-02T21:12:03Z | CONTRIBUTOR | null | Added PySide support for the qtpandas DataFrameWidget.
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```
FAIL: test_resample_median_bug_1688 (pandas.tseries.tests.test_resample.TestResample)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/alex/git/pandas/pandas/tseries/tests/test_resample.py", line 620, in test_resample_median_bug_1688
tm.assert_frame_equal(result, exp)
File "/home/alex/git/pandas/pandas/util/testing.py", line 303, in assert_frame_equal
assert_isinstance(left, DataFrame)
File "/home/alex/git/pandas/pandas/util/testing.py", line 170, in assert_isinstance
type(obj), class_type_or_tuple))
AssertionError: Expected object to be of type <class 'pandas.core.series.Series'>, found <class 'pandas.core.frame.DataFrame'> instead
```
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} | 2 | 2013-09-27T23:50:41Z | 2021-04-11T03:27:47Z | 2021-04-11T03:27:47Z | CONTRIBUTOR | null | There seems to be some requirements of the form the `rule_code` can take. These requirements are not clearly documented.
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} | 4 | 2013-09-28T01:24:50Z | 2013-09-28T19:22:59Z | 2013-09-28T19:22:45Z | CONTRIBUTOR | null | ``` python
>>> pd.DataFrame([[1,2,3]], columns=[1.1,2.2,np.nan])
Traceback (most recent call last):
...
TypeError: ("'NoneType' object is not iterable", u'occurred at index 2.2')
```
Is this a bug or a feature? I don't see anything about that in the `v0.13.0.txt`, and it worked in 0.12:
``` python
>>> pd.DataFrame([[1,2,3]], columns=[1.1,2.2,np.nan])
1.1 2.2 NaN
0 1 2 3
```
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} | 28 | 2013-09-28T08:07:30Z | 2018-07-13T07:12:07Z | 2014-02-16T22:56:48Z | NONE | null | Google Spreadsheet is an online spreadsheet.
Google Document can be use to generate survey and results of survey will be stored as a Google Spreadsheet document.
Maybe Pandas should provide a `pd.io.gspread.read_frame` function that will read a given Google Spreadsheet document (using email, password, url or name of document and range) and return a DataFrame.
`pd.io.gspread.read_frame(email, password, filename, sheet, cell_range)`
`gspread` package could help
http://burnash.github.io/gspread/
I wrote a little bit of code for that... but it could probably be improve and add into Pandas.
```
email = '...@...'
password = '...'
cell_range = 'A1:R20'
gc = gspread.login(email, password)
wks = gc.open(filename).sheet1
cell_list = wks.range('')
# Build a NumPy array
(row, col) = (cell_list[-1].row, cell_list[-1].col)
data = np.empty((row-1,col), dtype=object)
data[:] = np.nan
k = 0
cols = []
for i in range(row):
for j in range(col):
val = cell_list[k].value
if i==0:
if val != None:
if val not in cols:
cols.append(val)
else: # add a number if colname ever exists
ii = 1
while True:
new_val = val + '_' + str(ii)
if new_val not in cols:
break
ii += 1
cols.append(new_val)
else:
cols.append('col_'+str(j))
#cols.append(j)
else:
if val != None:
data[i-1, j] = val
k += 1
```
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} | 0 | 2013-09-28T16:19:45Z | 2014-07-03T18:50:32Z | 2013-09-28T19:22:45Z | CONTRIBUTOR | null | closes #5016
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} | 2 | 2013-09-28T20:16:26Z | 2019-09-19T17:17:03Z | 2019-09-19T17:17:02Z | CONTRIBUTOR | null | The lengthy conversation that started this issue can be found in the pull request #4955.
The basic idea is to create a safe stream wrapper in the `PythonParser.__init__` function that will always emit unicode strings instead of the current implementation that in some cases return `str` and in other return `unicode` depending on the thing that is passed as the input. The same thing should done for the `CParserWrapper`.
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} | 5 | 2013-09-28T20:37:55Z | 2014-07-09T23:49:20Z | 2013-09-29T17:02:25Z | CONTRIBUTOR | null | There's a lot of overlap right now, this is the first step to trying to
make this cleaner.
- Abstract all arithmetic methods into core/ops
- Add full range of flex arithmetic methods to all NDFrame/ndarray
PandasObjects (except for SparsePanel pow and mod, which only work for
scalars)
- Normalize arithmetic methods signature (see
`ops.add_special_arithmetic_methods` and
`ops.add_flex_arithmetic_methods` for signature).
- Opt-in more arithmetic operations with numexpr (except for
SparsePanel, which has to opt-out because it doesn't respond to
`shape`).
- BUG: Fix `_fill_zeros` call to work even if TypeError (previously
was inconsistent).
- Add bind method to core/common
Closes #3765.
Closes #4334.
Closes #4051.
Closes #5033.
Closes #4331.
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} | 9 | 2013-09-28T21:06:43Z | 2020-03-31T03:58:51Z | 2020-03-31T03:58:38Z | CONTRIBUTOR | null | Reference docs (see: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.resample.html):
- [ ] Add to docs the list of options that can be passed to `fill_method`. Some of which are `pad`, `bfill`, `ffill`.
-- The list seems to be documented for the `method` on `fillna`: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.fillna.html#pandas.DataFrame.fillna
- [x] The `convention` parameter is not well documented. It is unclear what the values of `end` or `start` means (and the associated aliases). (#15432 is dupe), PR #16965
- [x] The `kind` parameters is also unclear. The best I could get from it was a infinite stack recursion: #5026. (https://github.com/pandas-dev/pandas/pull/19084)
- [ ] `how` can also be a function or list of functions, not only a string
- [ ] for the `closed` and `label` keywords, I think it could be explained which are default in which cases (which is not always the same). In every case, that `label='right'` is default as stated in the docs (http://pandas.pydata.org/pandas-docs/dev/timeseries.html#up-and-downsampling) is not true.
- [ ] A list of all available methods for `how`
Tutorial docs:
- [ ] example using custom `how` function
- [ ] example showing how to use `base`
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https://api.github.com/repos/pandas-dev/pandas/issues/5024 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5024/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5024/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5024/events | https://github.com/pandas-dev/pandas/issues/5024 | 20,219,234 | MDU6SXNzdWUyMDIxOTIzNA== | 5,024 | Series.resample convention parameter is not well documented | {
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http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.resample.html
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} | 39 | 2013-09-28T21:13:11Z | 2014-06-27T23:28:48Z | 2014-03-12T13:00:58Z | NONE | null | ENH/TST: Support for non-ascii encodings in DataFrame.to_excel
Closes #3710.
Notice: Despite (for my modest knowledge of python) it should be easier to just put encoding='ascii' in the declaration of to_excel in line 1352 of frame.py, I've declared it with encoding=None as jreback suggested, and then making it default to ascii later with the if clause.
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} | 1 | 2013-09-28T22:06:39Z | 2016-10-12T23:04:14Z | 2013-09-28T22:24:07Z | CONTRIBUTOR | null | ```
rng = pd.date_range('1/1/2012', periods=4, freq=pd.offsets.YearEnd())
s = pd.Series(np.random.randn(4), index=rng)
s.resample(rule=pd.offsets.Week(weekday=0), fill_method="pad", closed="left", kind="period")
```
leads to:
```
...
/usr/local/lib/python2.7/dist-packages/pandas/tseries/period.pyc in _from_arraylike(cls, data, freq, tz)
652 'inferred from first element'))
653
--> 654 if np.issubdtype(data.dtype, np.datetime64):
655 data = dt64arr_to_periodarr(data, freq, tz)
656 elif data.dtype == np.int64:
/usr/local/lib/python2.7/dist-packages/numpy/core/numerictypes.pyc in issubdtype(arg1, arg2)
757
758 """
--> 759 if issubclass_(arg2, generic):
760 return issubclass(dtype(arg1).type, arg2)
761 mro = dtype(arg2).type.mro()
/usr/local/lib/python2.7/dist-packages/numpy/core/numerictypes.pyc in issubclass_(arg1, arg2)
697 """
698 try:
--> 699 return issubclass(arg1, arg2)
700 except TypeError:
701 return False
```
Setting `kind="period"` seems to trigger the issue.
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} | 26 | 2013-09-28T23:36:12Z | 2019-12-23T04:21:29Z | 2019-12-23T04:21:29Z | NONE | null | I've had recently made the experience of implementing and maintaining different down-stream applications supporting or building upon pandas. One thing I've learned is that it's quite painful to write code that e.g. runs for pandas 0.7.3 up to current master. Although 0.7.3 seems rather old given the rapid dev-cycle and the vibrant community, one may not forget that 0.7.3 is not older than 1.5 - 2 years and hence still counts as stable version in quite a few distros (while user-studies show that many people, let alone institutions, are often years behind recent versions ...).
Which guidelines to follow to make life easier when supporting such use cases does not seem too well documented. What would people think about adding such a -- maybe growing -- collection of hints + tips to the docs?
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} | 11 | 2013-09-28T23:52:53Z | 2021-04-11T03:31:57Z | 2021-04-11T03:31:57Z | CONTRIBUTOR | null | Many / most of the entries in `_offset_map` look like:
```
_offset_map['X'] = OffsetX()
```
where `X` is a string literal for what would have been returned by `OffsetX().rule_code`
See for example: https://github.com/pydata/pandas/blob/master/pandas/tseries/frequencies.py#L132
This makes adding to and maintaining the list of Offsets more cumbersome.
Manually populating `_offset_map` could be reserved for cases in which the entry in `_offset_map` is not the same as the `rule_code`. The remaining cases would be auto-populated using a `classmethod` on the Offset to return all possible Offsets.
A related map is `_offset_names`
One suggestion would be to change `get_offset_name` to return `offset.rule_code` in the else case rather than raising an exception. This would remove the need for `_offset_names`
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} | 15 | 2013-09-29T00:51:11Z | 2016-10-12T23:04:14Z | 2013-10-03T02:26:05Z | CONTRIBUTOR | null | `read_html`, find the correct table, parses the structure of the table (inclusing row and header labels), but does not parse the data:
`tables = pd.read_html("http://www.camacau.com/changeLang?lang=en_US&url=/statistic_list")`
```
In [119]: tables[7]
Out[119]:
0 1 2 3 4 5 6
0 NaT 2013 2012 2011 2010 2009 2008
1 2013-01-28 00:00:00 NaN NaN NaN NaN NaN NaN
2 2013-02-28 00:00:00 NaN NaN NaN NaN NaN NaN
3 2013-03-28 00:00:00 NaN NaN NaN NaN NaN NaN
4 2013-04-28 00:00:00 NaN NaN NaN NaN NaN NaN
5 2013-05-28 00:00:00 NaN NaN NaN NaN NaN NaN
6 2013-06-28 00:00:00 NaN NaN NaN NaN NaN NaN
7 2013-07-28 00:00:00 NaN NaN NaN NaN NaN NaN
8 2013-08-28 00:00:00 NaN NaN NaN NaN NaN NaN
9 2013-09-28 00:00:00 NaN NaN NaN NaN NaN NaN
10 2013-10-28 00:00:00 NaN NaN NaN NaN NaN NaN
11 2013-11-28 00:00:00 NaN NaN NaN NaN NaN NaN
12 2013-12-28 00:00:00 NaN NaN NaN NaN NaN NaN
13 NaT NaN NaN NaN NaN NaN NaN
```
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} | 3 | 2013-09-29T01:17:32Z | 2017-12-18T22:56:58Z | 2017-12-18T22:56:58Z | CONTRIBUTOR | null | It's because of Series._bin_op says @jreback.
This fails.
``` python
>>> from datetime import timedelta
>>> from pandas import Series, DataFrame, date_range
>>> s = Series(date_range('2012-1-1', periods=3, freq='D'))
>>> s.sub(s.max())
TypeError: ufunc sub cannot use operands with types dtype('<M8[ns]') and dtype('O')
```
Probably the same thing for all the valid time operations.
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} | 2 | 2013-09-29T01:54:35Z | 2015-09-07T04:42:01Z | 2015-09-07T04:40:44Z | CONTRIBUTOR | null | First two examples pass, third example doesn't.
``` python
# GH 4629
# arithmetic datetime64 ops with an index
s = Series(date_range('20130101',periods=5),index=date_range('20130101',periods=5))
expected = s-s.index.to_series()
result = s-s.index
assert_series_equal(result,expected)
result = s-s.index.to_period()
assert_series_equal(result,expected)
result2 = s.index.to_period() - s
expected2 = s.index.to_series() - s
assert_series_equal(result2, expected2)
```
Traceback:
``` python
/usr/local/bin/vim +2583 pandas/tests/test_series.py # test_datetime64_with_index
result2 = s.index.to_period() - s
/usr/local/bin/vim +873 pandas/tseries/period.py # __sub__
return PeriodIndex(ordinal=self.values - other, freq=self.freq)
TypeError: ufunc subtract cannot use operands with types dtype('int64') and dtype('<M8[ns]')
```
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} | 3 | 2013-09-29T02:21:53Z | 2013-09-29T17:02:25Z | 2013-09-29T17:02:25Z | CONTRIBUTOR | null | this blocks delegation to the `__r*__` methods.
```
In [7]: %paste
s = Series([Timestamp('20130101 9:01'), Timestamp('20130101 9:02')])
## -- End pasted text --
In [8]: s
Out[8]:
0 2013-01-01 09:01:00
1 2013-01-01 09:02:00
dtype: datetime64[ns]
In [9]: offsets.Second(5)
Out[9]: <5 * Seconds>
In [10]: offsets.Second(5) + s
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-10-48c2ebc28d0d> in <module>()
----> 1 offsets.Second(5) + s
../pandas/tseries/offsets.pyc in __add__(self, other)
1178 else:
1179 return _delta_to_tick(self.delta + other.delta)
-> 1180 return self.apply(other)
1181
1182 def __eq__(self, other):
../pandas/tseries/offsets.pyc in apply(self, other)
1222 return type(self)(self.n + other.n)
1223 else: # pragma: no cover
-> 1224 raise TypeError('Unhandled type: %s' % type(other))
1225
1226 _rule_base = 'undefined'
TypeError: Unhandled type: <class 'pandas.core.series.Series'>
In [11]: s + offsets.Second(5)
Out[11]:
0 2013-01-01 09:01:05
1 2013-01-01 09:02:05
dtype: datetime64[ns]
```
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} | 3 | 2013-09-29T02:25:49Z | 2014-06-24T03:57:23Z | 2013-09-29T19:20:49Z | CONTRIBUTOR | null | related #4787 (keywords now pass thru to _reduce), default for `numeric` is still `None`
closes #4435
BUG: provide numpy compatibility with 1.7 when implicity using `__array__`, IOW:
`np.prod(s)`, or `np.mean(df,axis=1)` will now work (because numpy _decided_ to try to see if the
object has this function and then pass all kinds of keywords to it, so pandas gets the right function called
with `dtype`, and `out` passed in (currently ignored)
CLN: refactored all numeric and stat like function (sum/mean/mad/min/max) and cums (cummin/sum..)
to `core/generic.py` from Series/DataFrame/Panel
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} | 8 | 2013-09-29T02:59:34Z | 2019-10-05T23:03:30Z | 2019-10-05T23:03:29Z | CONTRIBUTOR | null | ```
from pandas import Series, DataFrame
s = Series([2, 3, 4, 5, 6, 7, 8, 9, 10])
d = DataFrame({'a': s})
s + d
d + s
print(d & s)
```
produces Traceback:
```
raceback (most recent call last):
File "testcase2.py", line 6, in <module>
print(d & s)
File "../pandas/core/frame.py", line 217, in f
return self._combine_series(other, na_op, fill_value, axis, level)
File "../pandas/core/frame.py", line 3040, in _combine_series
return self._combine_match_columns(other, func, fill_value)
File "../pandas/core/frame.py", line 3079, in _combine_match_columns
func, right, axes=[left.columns, self.index])
File "../pandas/core/internals.py", line 2048, in eval
return self.apply('eval', *args, **kwargs)
File "../pandas/core/internals.py", line 2033, in apply
applied = getattr(blk, f)(*args, **kwargs)
File "../pandas/core/internals.py", line 850, in eval
result = get_result(other)
File "../pandas/core/internals.py", line 834, in get_result
return self._try_coerce_result(func(values, other))
File "../pandas/core/frame.py", line 201, in na_op
mask = notnull(xrav) & notnull(yrav)
ValueError: operands could not be broadcast together with shapes (81) (9)
```
`s & d` currently raises `NotImplemented` instead. (because there's no `__rand__`)
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} | [] | closed | false | null | [] | null | 12 | 2013-09-29T03:47:22Z | 2013-10-02T22:26:05Z | 2013-09-29T17:01:40Z | CONTRIBUTOR | null | Motivation for this pull request came from this problem: http://stackoverflow.com/q/19039356/190597.
The problems:
- `pandas.rpy.common.load_data` currently fails to load high-dimensional arrays such as the "Titanic" dataset. `load_data` prints an error to stdout and returns None.
- Applying `load_data` to "iris3", "state.division" or "state.region" R datasets currently raise a TypeError.
- Timeseries R data is currently converted to lists that lack a time series index.
- Factor data is converted to a list of index numbers without any levels information.
This pull request:
- Provides code which allows `load_data` to convert the "Titanic" dataset (and similar high-dimensional arrays) to "melted" DataFrames. The code _does not_ use R's melt function, since that would introduce a new dependency.
- load_data now returns a DataFrame for arrays like "iris3".
- load_data converts R factors like "state.division" and "state.region" to lists which use the levels as values.
- load_data converts ts (timeseries) R data, like "airmiles" to Series with an index showing the times. Note however I wasn't able to convert the index to a DatetimeIndex since the same code is used to convert numeric R data (like "euro") which does not have a time-related index.
- moves already existant tests from rpy/common.py to tests/test_rpy.py
- adds new unit tests which highlight the problems mentioned above, and tests for what I think is more desireable behavior as described above.
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} | 0 | 2013-09-29T04:47:48Z | 2014-07-16T08:32:01Z | 2013-09-29T04:48:11Z | MEMBER | null | closes #5031
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} | [] | closed | false | null | [] | null | 29 | 2013-09-29T13:33:56Z | 2014-07-16T08:32:03Z | 2013-11-17T01:09:10Z | CONTRIBUTOR | null | I stated a PEP8 cleanup and did it for a few modules. Changed only the things where this doesn't produce ugly code.
If this is acceptable, I can continue doing this and go through the whole pandas codebase. There are just a few things that I would like to check if this is acceptable:
1. PEP8: "For flowing long blocks of text with fewer structural restrictions (docstrings or comments), the line length should be limited to 72 characters." - should I do this?
2. Should I clean the unused imports? There are a lot of unused imports, not just from pandas but also from the standard library. Also duplicate imports (importing `timedelta` 2 times for example in the cleanup that I did)...
3. Should I discard the unused variables? (There are a lot of them especially in the tests).
4. `a[i+2:3]` this is against PEP8, it should look like `a[i + 2:3]`, but I don't think these things should be changed, the second one is uglier even if it's against PEP8.
Also one of the problems with this cleanup is that it changes a lot of things and should be merged as soon as possible :( Because it will soon become unmergable...
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} | 7 | 2013-09-29T13:56:02Z | 2016-10-12T23:04:14Z | 2014-06-03T13:36:59Z | CONTRIBUTOR | null | Maybe eval is producing a strange object or something? Or it could be unrelated.
```
../pandas/core/index.py:898: RuntimeWarning: tp_compare didn't return -1 or -2 for exception
result.sort()
../python2.7/site-packages/numpy/core/fromnumeric.py:595: RuntimeWarning: tp_compare didn't return -1 or -2 for exception
a.sort(axis, kind, order)
```
You can see this manifest in a weird sorting difference between numexpr and the flex add method (and this generates the RuntimeWarning shown above).
``` python
import numpy as np
import pandas as pd
engine = 'numexpr'
parser = 'pandas'
r_idx_type, c_idx_type, index_name = ('i', 'dt', 'index')
from pandas.util.testing import makeCustomDataframe as mkdf
f = lambda *args, **kwargs: np.random.randn()
df = mkdf(10, 7, data_gen_f=f, r_idx_type=r_idx_type,
c_idx_type=c_idx_type)
index = getattr(df, index_name)
s = pd.Series(np.random.randn(5), index[:5])
res = pd.eval('s + df', engine=engine, parser=parser)
expected = df.add(s)
print(res.columns)
print(expected.columns)
print(res)
print(df.add(s))
```
Somehow they end up with columns in different orders.
```
Index([1, 2000-01-03 00:00:00, 2000-01-04 00:00:00, 2000-01-05 00:00:00, 2, 4, 3, 0, 2000-01-06 00:00:00, 2000-01-07 00:00:00, 2000-01-10 00:00:00, 2000-01-11 00:00:00], dtype=object)
Index([2000-01-03 00:00:00, 2000-01-04 00:00:00, 2000-01-05 00:00:00, 2000-01-06 00:00:00, 2000-01-07 00:00:00, 2000-01-10 00:00:00, 2000-01-11 00:00:00, 0, 1, 2, 3, 4], dtype=object)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5040 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5040/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5040/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5040/events | https://github.com/pandas-dev/pandas/pull/5040 | 20,231,030 | MDExOlB1bGxSZXF1ZXN0ODY5MDIwNw== | 5,040 | TST: added null conversions for TimeDeltaBlock in core/internals related (GH4396) | {
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} | 1 | 2013-09-29T15:50:44Z | 2014-07-09T23:52:42Z | 2013-09-29T17:07:57Z | CONTRIBUTOR | null | closes #4396
NaT wraparound bug again, fixed
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} | 12 | 2013-09-29T16:49:27Z | 2016-10-12T23:04:14Z | 2014-02-23T21:30:03Z | CONTRIBUTOR | null | There are two places in test_eval where there are name errors (i.e., referring to items that aren't defined). Given that they don't raise errors, this means they aren't being used. I'm not sure whether this is because they're covered elsewhere or what, just need to make a decision.
From `check_complex_cmp_ops`
``` python
elif _bool_and_frame(lhs, rhs):
self.assertRaises(TypeError, _eval_single_bin, lhs_new, '&',
rhs_new, self.engine)
self.assertRaises(TypeError, pd.eval, ex,
local_dict={'lhs': lhs, 'rhs': rhs},
engine=self.engine, parser=self.parser)
```
and also the second branch of `check_operands` in `check_chained_cmp_ops`
``` python
if (np.isscalar(left) and np.isscalar(right) and
cmp_op in _bool_ops_syms):
ex1 = 'lhs {0} mid {1} rhs'.format(cmp1, cmp2)
ex2 = 'lhs {0} mid and mid {1} rhs'.format(cmp1, cmp2)
ex3 = '(lhs {0} mid) & (mid {1} rhs)'.format(cmp1, cmp2)
for ex in (ex1, ex2, ex3):
with assertRaises(NotImplementedError):
pd.eval(ex, engine=self.engine, parser=self.parser)
return
```
- [ ] test this function or remove it: `_align_core_single_unary_op`
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} | 16 | 2013-09-29T17:08:06Z | 2014-06-23T00:59:28Z | 2013-10-02T21:26:47Z | CONTRIBUTOR | null | TST: Move tests from rpy/common.py to tests/test_rpy.py
TST: Add tests to demonstrate the enhancements made to rpy/common.py.
DOC: Add explanation to doc/source/release.rst
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} | 0 | 2013-09-29T19:53:09Z | 2014-06-22T11:58:23Z | 2013-09-29T20:16:10Z | CONTRIBUTOR | null | closes #4396
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} | 1 | 2013-09-29T20:02:40Z | 2013-10-28T01:28:52Z | 2013-10-28T01:14:38Z | CONTRIBUTOR | null | post #4324
- [ ] Fillna column wise, #4514
- [x] interpolation, consolidate Series/DataFrame interpolation to internals.Block.interpolatte, #4434, cc @TomAugspurger
- [ ] update, #3025
- [ ] shift, #4865, #4994
- [ ] sort, #5190
- [ ] dropna API for Series, see #5234, #5250
- [ ] in place dropna , #1960
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} | 15 | 2013-09-29T20:30:22Z | 2014-01-03T22:24:57Z | 2014-01-03T22:24:57Z | CONTRIBUTOR | null | Pyflakes revealed that there are a number of places using undefined
names. I've tried to fix them where I can; however, those parts of the
code are also clearly untested (or tested with
`assertRaises(Exception,...)`) so they need some love.
The PyTables code had a bunch of places with undefined `TermValue`, as
did stats/misc.
Finally, I've also noted (or removed) various places where variables
were assigned but then not used.
cc @jreback
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} | [] | closed | false | null | [] | null | 0 | 2013-09-29T20:37:00Z | 2014-07-16T08:32:07Z | 2013-09-29T22:48:09Z | CONTRIBUTOR | null | Only would occur if both enumerations failed. better way to handle than
try/except.
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} | 0 | 2013-09-29T21:09:35Z | 2014-07-16T08:32:08Z | 2013-09-29T21:23:13Z | CONTRIBUTOR | null | ...utation/pytables.py)
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} | 2 | 2013-09-29T22:35:59Z | 2016-10-12T23:04:14Z | 2013-10-03T02:26:05Z | CONTRIBUTOR | null | I presume that the problem is that the data is first parsed and then the header is selected out. But when the dtype of the column is a number type the item that should become the column name, since it's not a valid number, becomes `NaN`.
Sample data:
``` python
data1 = io.StringIO(u'''<table>
<thead>
<tr>
<th>Country</th>
<th>Municipality</th>
<th>Year</th>
</tr>
</thead>
<tbody>
<tr>
<td>Ukraine</td>
<th>Odessa</th>
<td>1944</td>
</tr>
</tbody>
</table>''')
data2 = io.StringIO(u'''
<table>
<tbody>
<tr>
<th>Country</th>
<th>Municipality</th>
<th>Year</th>
</tr>
<tr>
<td>Ukraine</td>
<th>Odessa</th>
<td>1944</td>
</tr>
</tbody>
</table>''')
```
Output:
``` python
>>> pd.read_html(data1)[0]
Country Municipality Year
0 Ukraine Odessa 1944
>>> pd.read_html(data2, header=0)[0]
0 Country Municipality NaN
1 Ukraine Odessa 1944
```
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} | 6 | 2013-09-29T23:01:32Z | 2019-10-27T02:14:39Z | 2019-10-27T02:14:38Z | CONTRIBUTOR | null | leave as experimental or change docs
- [ ] eval/query
- [ ] CustomBusinessDay
- [x] msgpack #27084 (deprecating)
- [ ] BigQuery
- [x] Panel4D -> deprecated
- [x] PanelND -> deprecated
- [ ] SQL changes
- [ ] r*flex methods
- [ ] get_options_data (#5602)
- [ ] DataFrame.style
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} | 6 | 2013-09-30T02:01:15Z | 2014-06-25T01:17:18Z | 2013-10-01T00:04:14Z | CONTRIBUTOR | null | closes #4501
this removes the ipython completers code and instead defines `__dir__`,
with a `_local_dir` that a class can override
NDFrame objects will have a pre-defined local of the info_axis (e.g. columns in a DataFrame)
for example:
```
In [1]: df = DataFrame(columns=list('ABC'))
In [2]: df.
Display all 199 possibilities? (y or n)
df.A df.at_time df.corrwith df.eq df.groupby df.itertuples df.min df.reindex df.shape df.to_dict df.tshift
df.B df.axes df.count df.eval df.gt df.ix df.mod df.reindex_axis df.shift df.to_excel df.tz_convert
df.C df.between_time df.cov df.ffill df.head df.join df.mul df.reindex_like df.skew df.to_hdf df.tz_localize
df.T df.bfill df.cummax df.fillna df.hist df.keys df.multiply df.rename df.sort df.to_html df.unstack
df.abs df.blocks df.cummin df.filter df.iat df.kurt df.ndim df.rename_axis df.sort_index df.to_json df.update
df.add df.boxplot df.cumprod df.first df.icol df.kurtosis df.ne df.reorder_levels df.sortlevel df.to_latex df.values
df.add_prefix df.clip df.cumsum df.first_valid_index df.idxmax df.last df.pct_change df.replace df.squeeze df.to_panel df.var
df.add_suffix df.clip_lower df.delevel df.floordiv df.idxmin df.last_valid_index df.pivot df.resample df.stack df.to_period df.where
df.align df.clip_upper df.describe df.from_csv df.iget_value df.le df.pivot_table df.reset_index df.std df.to_pickle df.xs
df.all df.columns df.diff df.from_dict df.iloc df.load df.plot df.rfloordiv df.sub df.to_records
df.any df.combine df.div df.from_items df.index df.loc df.pop df.rmod df.subtract df.to_sparse
df.append df.combineAdd df.divide df.from_records df.info df.lookup df.pow df.rmul df.sum df.to_sql
df.apply df.combineMult df.dot df.ftypes df.insert df.lt df.prod df.rpow df.swapaxes df.to_stata
df.applymap df.combine_first df.drop df.ge df.interpolate df.mad df.product df.rsub df.swaplevel df.to_string
df.as_blocks df.compound df.drop_duplicates df.get df.irow df.mask df.quantile df.rtruediv df.tail df.to_timestamp
df.as_matrix df.consolidate df.dropna df.get_dtype_counts df.isin df.max df.query df.save df.take df.to_wide
df.asfreq df.convert_objects df.dtypes df.get_ftype_counts df.iteritems df.mean df.radd df.select df.to_clipboard df.transpose
df.astype df.copy df.duplicated df.get_value df.iterkv df.median df.rank df.set_index df.to_csv df.truediv
df.at df.corr df.empty df.get_values df.iterrows df.merge df.rdiv df.set_value df.to_dense df.truncate
```
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} | 6 | 2013-09-30T02:18:40Z | 2013-10-24T18:18:37Z | 2013-10-24T18:18:37Z | CONTRIBUTOR | null | Improved message and better exception types.
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} | 4 | 2013-09-30T02:58:12Z | 2014-07-16T08:32:14Z | 2013-10-01T04:00:14Z | CONTRIBUTOR | null | Resolves much of #4717. Basically cleans up docstrings in a few places
plus, for the few functions that need them, uses a shared dict of
function definitions to share them. This has the side-benefit of letting
the function docstrings reside right next to the functions themselves,
maintaining readability.
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} | 10 | 2013-09-30T03:13:38Z | 2014-07-07T01:51:22Z | 2013-10-02T21:17:01Z | CONTRIBUTOR | null | closes #4996
Aliases **iadd** = **add**.
Not doing anything else for now until time to consider.
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https://api.github.com/repos/pandas-dev/pandas/issues/5054 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5054/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5054/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5054/events | https://github.com/pandas-dev/pandas/issues/5054 | 20,252,043 | MDU6SXNzdWUyMDI1MjA0Mw== | 5,054 | pip install pandas fails (mac osx) | {
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} | [] | closed | false | null | [] | null | 3 | 2013-09-30T09:36:51Z | 2013-10-05T04:25:34Z | 2013-09-30T13:24:06Z | NONE | null | Tried 'mkvirtualenv pandas' and 'pip install pandas' and fails. Default python is 2.7 and my OS is OSX Mountain Lion. Pip log file:
---
/Users/hernan/Envs/pandas/bin/pip run on Mon Sep 30 11:28:28 2013
Downloading/unpacking pandas
Running setup.py egg_info for package pandas
```
Running from numpy source directory.
Traceback (most recent call last):
File "<string>", line 16, in <module>
File "/Users/hernan/Envs/pandas/build/pandas/setup.py", line 548, in <module>
**setuptools_kwargs)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/core.py", line 112, in setup
_setup_distribution = dist = klass(attrs)
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/dist.py", line 260, in __init__
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/dist.py", line 284, in fetch_build_eggs
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/pkg_resources.py", line 563, in resolve
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/pkg_resources.py", line 799, in best_match
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/pkg_resources.py", line 811, in obtain
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/dist.py", line 327, in fetch_build_egg
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/command/easy_install.py", line 446, in easy_install
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/command/easy_install.py", line 476, in install_item
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/command/easy_install.py", line 655, in install_eggs
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/command/easy_install.py", line 930, in build_and_install
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/command/easy_install.py", line 919, in run_setup
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/sandbox.py", line 62, in run_setup
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/sandbox.py", line 105, in run
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/sandbox.py", line 64, in <lambda>
File "setup.py", line 214, in <module>
FULLVERSION += "-%s" % rev
File "setup.py", line 191, in setup_package
MICRO = 0
File "/var/folders/hh/3g2p62410756r13dp0yqb9lw0000gn/T/easy_install-s2ZuBD/numpy-1.7.1/numpy/distutils/core.py", line 25, in <module>
File "/var/folders/hh/3g2p62410756r13dp0yqb9lw0000gn/T/easy_install-s2ZuBD/numpy-1.7.1/numpy/distutils/command/build_ext.py", line 16, in <module>
File "/var/folders/hh/3g2p62410756r13dp0yqb9lw0000gn/T/easy_install-s2ZuBD/numpy-1.7.1/numpy/distutils/system_info.py", line 220, in <module>
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/sandbox.py", line 203, in _open
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/sandbox.py", line 199, in _violation
setuptools.sandbox.SandboxViolation: SandboxViolation: open('/dev/null', 'w') {}
The package setup script has attempted to modify files on your system
that are not within the EasyInstall build area, and has been aborted.
This package cannot be safely installed by EasyInstall, and may not
support alternate installation locations even if you run its setup
script by hand. Please inform the package's author and the EasyInstall
maintainers to find out if a fix or workaround is available.
Complete output from command python setup.py egg_info:
Running from numpy source directory.
```
Traceback (most recent call last):
File "<string>", line 16, in <module>
File "/Users/hernan/Envs/pandas/build/pandas/setup.py", line 548, in <module>
```
**setuptools_kwargs)
```
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/core.py", line 112, in setup
```
_setup_distribution = dist = klass(attrs)
```
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/dist.py", line 260, in **init**
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/dist.py", line 284, in fetch_build_eggs
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/pkg_resources.py", line 563, in resolve
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/pkg_resources.py", line 799, in best_match
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/pkg_resources.py", line 811, in obtain
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/dist.py", line 327, in fetch_build_egg
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/command/easy_install.py", line 446, in easy_install
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/command/easy_install.py", line 476, in install_item
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/command/easy_install.py", line 655, in install_eggs
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/command/easy_install.py", line 930, in build_and_install
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/command/easy_install.py", line 919, in run_setup
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/sandbox.py", line 62, in run_setup
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/sandbox.py", line 105, in run
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/sandbox.py", line 64, in <lambda>
File "setup.py", line 214, in <module>
```
FULLVERSION += "-%s" % rev
```
File "setup.py", line 191, in setup_package
```
MICRO = 0
```
File "/var/folders/hh/3g2p62410756r13dp0yqb9lw0000gn/T/easy_install-s2ZuBD/numpy-1.7.1/numpy/distutils/core.py", line 25, in <module>
File "/var/folders/hh/3g2p62410756r13dp0yqb9lw0000gn/T/easy_install-s2ZuBD/numpy-1.7.1/numpy/distutils/command/build_ext.py", line 16, in <module>
File "/var/folders/hh/3g2p62410756r13dp0yqb9lw0000gn/T/easy_install-s2ZuBD/numpy-1.7.1/numpy/distutils/system_info.py", line 220, in <module>
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/sandbox.py", line 203, in _open
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/sandbox.py", line 199, in _violation
setuptools.sandbox.SandboxViolation: SandboxViolation: open('/dev/null', 'w') {}
The package setup script has attempted to modify files on your system
that are not within the EasyInstall build area, and has been aborted.
This package cannot be safely installed by EasyInstall, and may not
support alternate installation locations even if you run its setup
script by hand. Please inform the package's author and the EasyInstall
maintainers to find out if a fix or workaround is available.
---
Command python setup.py egg_info failed with error code 1 in /Users/hernan/Envs/pandas/build/pandas
Exception information:
Traceback (most recent call last):
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/pip-1.2.1-py2.7.egg/pip/basecommand.py", line 107, in main
status = self.run(options, args)
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/pip-1.2.1-py2.7.egg/pip/commands/install.py", line 256, in run
requirement_set.prepare_files(finder, force_root_egg_info=self.bundle, bundle=self.bundle)
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/pip-1.2.1-py2.7.egg/pip/req.py", line 1042, in prepare_files
req_to_install.run_egg_info()
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/pip-1.2.1-py2.7.egg/pip/req.py", line 236, in run_egg_info
command_desc='python setup.py egg_info')
File "/Users/hernan/Envs/pandas/lib/python2.7/site-packages/pip-1.2.1-py2.7.egg/pip/util.py", line 612, in call_subprocess
% (command_desc, proc.returncode, cwd))
InstallationError: Command python setup.py egg_info failed with error code 1 in /Users/hernan/Envs/pandas/build/pandas
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https://api.github.com/repos/pandas-dev/pandas/issues/5055 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5055/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5055/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5055/events | https://github.com/pandas-dev/pandas/issues/5055 | 20,253,229 | MDU6SXNzdWUyMDI1MzIyOQ== | 5,055 | read_csv with usecols and implicit index columns | {
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} | [] | closed | false | null | [] | null | 4 | 2013-09-30T10:04:30Z | 2013-10-01T13:11:09Z | 2013-09-30T11:36:55Z | NONE | null | Pandas 0.12
When specifying usecols with read_csv it seems impossible to use an implicit (no header) index column
Example:
``` python
>>> data = 'a,b,c\n4,apple,bat,5.7\n8,orange,cow,10'
>>> pd.read_csv(StringIO(data))
a b c
4 apple bat 5.7
8 orange cow 10.0
>>> pd.read_csv(StringIO(data), usecols=['a', 'b'])
a b
0 apple bat
1 orange cow
>>>
```
Where you would prefer/expect this:
``` python
>>> pd.read_csv(StringIO(data), usecols=['a', 'b'])
a b
4 apple bat
8 orange cow
```
Specifying index_col = 0 would give:
``` python
>>> pd.read_csv(StringIO(data), usecols=['a', 'b'], index_col = 0)
b
apple bat
orange cow
```
So this would only work if we explicity pass the column to be used as an index to usecols. However, we cannot explicitly pass an implicit index column to usecols as the index column is not named (it has no header).
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} | 24 | 2013-09-30T11:22:25Z | 2020-04-05T01:23:01Z | null | NONE | null | See https://github.com/pydata/pandas/pull/4955#issuecomment-25352643
```
id foo
1 a
1 a
1 a
123a
```
This works with the snippet in the orig #4488 but fails with the merged #4955 due to the last line.
It's a common example of a fwf file.
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} | 1 | 2013-09-30T14:42:25Z | 2014-06-25T16:32:09Z | 2013-10-04T12:50:04Z | CONTRIBUTOR | null | closes #4699
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https://api.github.com/repos/pandas-dev/pandas/issues/5058 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5058/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5058/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5058/events | https://github.com/pandas-dev/pandas/pull/5058 | 20,273,659 | MDExOlB1bGxSZXF1ZXN0ODcxMDMwMA== | 5,058 | TST: allow to check for specific xlrd version and skip reader test if xlrd < 0.9 | {
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} | 3 | 2013-09-30T16:17:57Z | 2014-06-28T20:27:27Z | 2013-10-02T23:08:16Z | CONTRIBUTOR | null | See http://nipy.bic.berkeley.edu/builders/pandas-py2.x-wheezy-sparc/builds/148/steps/shell_4/logs/stdio for failure when outdated xlrd found (e.g. on debian stable wheezy)
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https://api.github.com/repos/pandas-dev/pandas/issues/5059 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5059/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5059/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5059/events | https://github.com/pandas-dev/pandas/pull/5059 | 20,273,921 | MDExOlB1bGxSZXF1ZXN0ODcxMDQ0Ng== | 5,059 | BF: import isnull | {
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} | [] | closed | false | null | [] | null | 1 | 2013-09-30T16:21:52Z | 2014-07-16T08:32:21Z | 2013-09-30T16:42:04Z | CONTRIBUTOR | null | Used in the code
see http://nipy.bic.berkeley.edu/builders/pandas-py2.x-wheezy-sparc/builds/148/steps/shell_4/logs/stdio for the failure
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https://api.github.com/repos/pandas-dev/pandas/issues/5060 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5060/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5060/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5060/events | https://github.com/pandas-dev/pandas/pull/5060 | 20,281,974 | MDExOlB1bGxSZXF1ZXN0ODcxNDc3OQ== | 5,060 | VIS: let scatter plots obey mpl color scheme (#3338) | {
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} | 9 | 2013-09-30T18:36:20Z | 2014-07-16T08:32:22Z | 2013-10-04T23:17:53Z | MEMBER | null | Closes #3338.
Fixes the remaining plot functions that don't follow the matplotlib style (functions based on scatter plots).
Rationale:
- set the color keyword `c` to default one defined in matplotlibs rcParams if `c` was not specified by the user (because `c='b'` is harcoded in matplotlib `scatter` method).
- the `color` keyword is not checked because, if specified by the user, it already is given preference by matplotlib over `c` if both are given.
There is a new PR on scatter based plotting methods (#3473), but I suppose that it can be handled in that PR to ensure this new plotting method also follows this approach?
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} | [] | closed | false | null | [] | null | 5 | 2013-09-30T19:06:35Z | 2013-10-02T22:36:19Z | 2013-10-02T22:36:19Z | NONE | null | I am trying to create a code mandatory for work (though I have never used Python or Pandas so I am in over my head!) where I want to compare two rows of two data sets and extract the shared information.
I can used this method in Access through creating a Query but need to do so in Pandas. I imported two csv files and opened them through pandas so as to make them DataFrames that I can compare.
I want to take two rows, say B and D of one file(File 1) and match them with B and D of File 2. If the content matches, then I want to create a new array of all of the columns of that specific row.
So for example, I have two DataFrames with 20 rows and 18 out of the 20 rows have matching items in B and D, I then want an array consisting of those 18 rows with the same data in File 1 and 2.
Can someone please guide me as to how to get started.
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} | 17 | 2013-09-30T20:31:42Z | 2020-04-30T05:37:39Z | null | CONTRIBUTOR | null | I had to debug a problem which was caused by a NaN in a string column:
``` python
cit = citations[1655:1660][["names"]]
print cit
names
1655 A;B
1656 NaN
1657 C;D
1658 E;F
1659 G;H
s = cit.apply(lambda x: pandas.Series(x.split(';'))).stack()
[...]
C:\portabel\Python27\lib\site-packages\pandas\core\series.pyc in apply(self, func, convert_dtype, args, **kwds)
1952 values = lib.map_infer(values, lib.Timestamp)
1953
-> 1954 mapped = lib.map_infer(values, f, convert=convert_dtype)
1955 if len(mapped) and isinstance(mapped[0], Series):
1956 from pandas.core.frame import DataFrame
C:\portabel\Python27\lib\site-packages\pandas\lib.pyd in pandas.lib.map_infer (pandas\lib.c:43742)()
<timed exec> in <lambda>(x)
AttributeError: 'float' object has no attribute 'split'
```
I would be nice if pandas could in that case spit out a more helpfull error messages or check if the error occured on a NaN cell and simple set that cell to NaN (Does R this?) or rethrow the error as a "NaNError" or something like that.
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} | 13 | 2013-09-30T21:33:50Z | 2014-06-16T16:13:45Z | 2014-03-09T15:03:57Z | NONE | null | closes #1014
I've added a check for dtype=object and try to coerce it to a float in frame.to_sparse
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} | 0 | 2013-09-30T23:00:52Z | 2014-06-24T06:52:45Z | 2013-10-01T01:21:47Z | CONTRIBUTOR | null | closes #4544
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} | 8 | 2013-10-01T01:36:55Z | 2016-07-26T08:35:30Z | 2016-07-26T08:35:00Z | CONTRIBUTOR | null | This test appears to be incomplete: https://github.com/pydata/pandas/blob/master/pandas/io/tests/test_parsers.py#L1421
The DataFrame returned has only 1 column:
```
In [5]: df
Out[5]:
E
one two three four
a b 10.0032 5 0.3640
q 20.0000 4 0.1744
x q 30.0000 3 2.5838
```
But the data has 5:
```
In [8]: print text
A B C D E
one two three four
a b 10.0032 5 -0.5109 -2.3358 -0.4645 0.05076 0.3640
a q 20 4 0.4473 1.4152 0.2834 1.00661 0.1744
x q 30 3 -0.6662 -0.5243 -0.3580 0.89145 2.5838
```
Unless I'm mistaken, there should be 5 in the outcome too, no? I came across this fixing a different bug but wanted to make sure it's marked.
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} | 1 | 2013-10-01T03:12:10Z | 2016-10-12T23:04:15Z | 2013-10-03T02:26:05Z | CONTRIBUTOR | null | ```
pd.read_html("http://www.camacau.com/changeLang?lang=en_US&url=/statistic_list",infer_types=False,header=0,index_col=0)
```
yields:
```
Traceback (most recent call last)
<ipython-input-114-a13f8ac8a77b> in <module>()
----> 1 foo2 = pd.read_html("http://www.camacau.com/changeLang?lang=en_US&url=/statistic_list",infer_types=False,header=0,index_col=0)
/usr/local/lib/python2.7/dist-packages/pandas/io/html.pyc in read_html(io, match, flavor, header, index_col, skiprows, infer_types, attrs)
904 'data (you passed a negative value)')
905 return _parse(flavor, io, match, header, index_col, skiprows, infer_types,
--> 906 attrs)
/usr/local/lib/python2.7/dist-packages/pandas/io/html.pyc in _parse(flavor, io, match, header, index_col, skiprows, infer_types, attrs)
776
777 return [_data_to_frame(table, header, index_col, infer_types, skiprows)
--> 778 for table in tables]
779
780
/usr/local/lib/python2.7/dist-packages/pandas/io/html.pyc in _data_to_frame(data, header, index_col, infer_types, skiprows)
674
675 # drop by default
--> 676 df.set_index(cols, inplace=True)
677 if df.index.nlevels == 1:
678 if isnull(df.index.name) or not df.index.name:
/usr/local/lib/python2.7/dist-packages/pandas/core/frame.pyc in set_index(self, keys, drop, append, inplace, verify_integrity)
2833 arrays.append(level)
2834
-> 2835 index = MultiIndex.from_arrays(arrays, names=names)
2836
2837 if verify_integrity and not index.is_unique:
/usr/local/lib/python2.7/dist-packages/pandas/core/index.pyc in from_arrays(cls, arrays, sortorder, names)
1763 if len(arrays) == 1:
1764 name = None if names is None else names[0]
-> 1765 return Index(arrays[0], name=name)
1766
1767 cats = [Categorical.from_array(arr) for arr in arrays]
/usr/local/lib/python2.7/dist-packages/pandas/core/index.pyc in __new__(cls, data, dtype, copy, name, **kwargs)
108 return Int64Index(data, copy=copy, dtype=dtype, name=name)
109
--> 110 subarr = com._asarray_tuplesafe(data, dtype=object)
111 elif np.isscalar(data):
112 raise ValueError('Index(...) must be called with a collection '
/usr/local/lib/python2.7/dist-packages/pandas/core/common.pyc in _asarray_tuplesafe(values, dtype)
1489 # in numpy, leading to the following
1490 result = np.empty(len(values), dtype=object)
-> 1491 result[:] = values
1492
1493 return result
ValueError: could not broadcast input array from shape (11,2) into shape (11)
```
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} | 5 | 2013-10-01T07:58:56Z | 2013-10-15T18:42:58Z | 2013-10-15T18:42:58Z | CONTRIBUTOR | null | I tried to find the API reference for the methods of `Series.str`, but they are not linked from http://pandas.pydata.org/pandas-docs/dev/api.html
It seems "new" thingies like `df.iloc` or `df.loc` are also not linked from there (`DataFrame.ix` is!)
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} | 1 | 2013-10-01T14:50:59Z | 2014-01-24T15:17:25Z | 2014-01-24T15:17:25Z | NONE | null | I recently had a look at this code and spotted some potential improvements to be made.
1. When the user specifies `sort=False` (the default), the code
still performs an interim sort on the keys (the arg isn't passed down), That
can be probably be omitted without harm.
This is fairly trivial to do, just requires some testing.
2. Wes came up with a really lovely "counting-sort"-like trick for matching
up keys from each frame when the join is performed on more then one
column. Basically, he treats each tuple of keys as being totally-ordered
based a cartesian product (forgive me... the code is clearer).
However, this only works as long as the size of the keyspace fits
inside platform int, for larger keyspaces (less common since it requires
many columns with many distinct keys each) he reverts to an algo which
proves much slower (zipping together the keys). I think there might be a
faster way to code this slow-path (perhaps by binning or some other
cleverness). Using python bignums instead of platform int seems to hurt
perf too much to make it worthwhile.
3. When performing an `inner`, `left` or `right` join, a `factorize`
is performed fully on all keys of both frames. In the case where
dataframe sizes are highly asyemmtric (df1 >> df2), many of the
keys for the larger frame will potentially be discarded, if they
have no corresponding keys in the smaller frame. Marking those
keys with a sentinal (or discarding beforehand in some other way, perhaps
a masked array?) can reduce the working set significantly. klib uses open-addressing so the sentinal can generate lots of collisions, but
I've still seen about 30% improvement in some cases.
If leveraging this "wasted work" can be used to reduce the keyspace
significantly, it makes it more likely that the slow path mentioned in 2)
can be avoided which can make a significant difference (2x or more).
I've played with this and have seen some real perf boosts but I won't be able to do this properly
any time soon.
If there's anyone interested in algos who'd like to take this on and work through the subtleties (NaNs', Perhaps having to unshare some routines) you're more then welcome to give it a go.
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} | 10 | 2013-10-01T17:32:57Z | 2018-04-16T14:54:19Z | 2018-04-16T14:54:04Z | CONTRIBUTOR | null | Returning a Series: http://stackoverflow.com/questions/19121854/using-rolling-apply-on-a-dataframe-object
Returning a Scalar: http://stackoverflow.com/questions/21040766/python-pandas-rolling-apply-two-column-input-into-function/21045831#21045831
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} | 12 | 2013-10-01T21:30:23Z | 2014-05-15T18:54:17Z | 2013-10-07T21:25:49Z | CONTRIBUTOR | null | Test case:
```
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: index = pd.MultiIndex.from_arrays([
['a', 'b', 'b'],
[1, np.nan, 2]
])
In [4]: index.get_level_values(1)
Out[4]: Float64Index([1.0, 2.0, 2.0], dtype=object)
```
The expected output is
`Float64Index([1.0, nan, 2.0], dtype=object)`
This happens because NA values are not stored in the MultiIndex levels and the corresponding label is set to -1. Then when labels are used as indexes to values in `get_level_values()` that -1 points to the last (not null) value.
I tried to fix this by appending a NA to the values if -1 is in levels.
https://github.com/goyodiaz/pandas/commit/f028513ad96a
It needs to be improved in order to return the proper NA value (NaN, None, maybe NaT?) depending on the index type. Does this approach makes sense?
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} | 18 | 2013-10-01T23:22:03Z | 2013-10-31T02:15:28Z | 2013-10-31T02:15:28Z | CONTRIBUTOR | null | This is the whatsnew example. I think for compat, the 3rd entry should be `(np.nan, 3)` ?
```
In [1]: Series(['a1', 'b2', '3']).str.match('(?P<letter>[ab])?(?P<digit>\d)')
Out[1]:
0 (a, 1)
1 (b, 2)
2 (None, 3)
dtype: object
```
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} | 0 | 2013-10-02T00:15:10Z | 2013-10-02T23:36:29Z | 2013-10-02T23:36:29Z | CONTRIBUTOR | null | I added a FrozenNDArray and FrozenList class for MultiIndex internals for 0.13. They shouldn't end up in pickles, so we don't need to support them. Trivial fix but needs to happen before 0.13 is released.
Current less than optimal behavior:
```
In [2]: mi = MultiIndex.from_tuples(zip(range(10), range(10)))
In [3]: mi
Out[3]:
MultiIndex(levels=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]
labels=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]])
In [4]: mi.__reduce__()
Out[4]:
(<function numpy.core.multiarray._reconstruct>,
(pandas.core.index.MultiIndex, (0,), 'b'),
((1, (0,), dtype('O'), False, []),
([Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int64),
Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int64)],
[FrozenNDArray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int64),
FrozenNDArray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int64)],
None,
[None, None])))
```
Correct output (or could just be ndarrays too):
```
In [2]: mi = MultiIndex.from_tuples(zip(range(10), range(10)))
In [3]: mi
Out[3]:
MultiIndex(levels=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]
labels=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]])
In [4]: mi.__reduce__()
Out[4]:
(<function numpy.core.multiarray._reconstruct>,
(pandas.core.index.MultiIndex, (0,), 'b'),
((1, (0,), dtype('O'), False, []),
([array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])],
[array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])],
None,
[None, None])))
```
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} | 3 | 2013-10-02T00:43:52Z | 2014-09-02T13:21:29Z | 2013-10-12T17:21:01Z | CONTRIBUTOR | null | MI repr was missing a comma between its arguments and Index reprs needed
to quote their dtypes.
Only talking about Index, Int64Index and
MultiIndex. Tseries indices (like PeriodIndex and DatetimeIndex) are
more complicated and could be covered separately.
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} | 2 | 2013-10-02T00:46:58Z | 2019-09-18T14:02:17Z | 2019-09-18T14:02:17Z | NONE | null | ``` python
>>>import pandas as pd
>>> pd.__version__
'0.12.0-660-gec77315'
>>> import numpy as np
>>> np.version.version
'1.7.1'
>>> df = pd.DataFrame({'a': [0, 1, 0, 0], 'b': [0, 1, 0, 0]})
>>> sdf = df.to_sparse(fill_value=0)
>>> df.groupby('a').mean()
b
a
0 0
1 1
>>> sdf.groupby('a').mean()
b
a
1 0
```
I'm not surpised that the mean for group `a == 0` was not returned. It is surprising that the result for group `a == 1` was incorrect.
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} | 1 | 2013-10-02T01:21:00Z | 2014-08-05T12:07:55Z | 2013-10-02T01:30:08Z | CONTRIBUTOR | null | Treats it like 2d array rather than array of tuples. I like it because
it's more informative - but we could also just describe `.values`.
Fixes #4842.
Still working out some kinks.
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} | 0 | 2013-10-02T01:55:43Z | 2014-08-07T11:34:54Z | 2014-08-07T11:34:54Z | CONTRIBUTOR | null | Changing to not be an ndarray subclass should make a number of things simpler (especially the API unification discussed in #3268). Plus, it will clarify the required interface for an Index object. It avoids all the workarounds to make Index immutable (instead, will define `__array__` and `__array_wrap__` and choose which other functions should be allowed).
- [ ] Move Index underlying data to `_data` to mirror other objects. Keep metadata on object itself. (MI should work with only minor tweaks, given that it was always a fake ndarray)
- [ ] Reimplement fastpath. (figure out how to do this inference again).
- [ ] Move MI underlying data to `_data` (potentially turn levels into 2d int64 ndarray and keep labels as list of lists or as ordered-dict-like. not sure whether row-major or column-major makes more sense. I'm guessing row-major since that would speed key lookups)
---
How much backwards compatibility do we need to support? (pickle compat should be simple). E.g. do we want to do something like this:
``` python
def __getattr__(self, attr):
res = getattr(self._data, attr)
warnings.warn("Accessing %s on %s is deprecated. Convert to an ndarray first." %
(attr, self.__class__.__name__))
return res
```
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} | 53 | 2013-10-02T04:04:18Z | 2021-04-11T03:34:29Z | null | CONTRIBUTOR | null | Looking at these three methods on DatetimeIndex:
``` python
# alias to offset
@property
def freq(self):
return self.offset
@cache_readonly
def inferred_freq(self):
try:
return infer_freq(self)
except ValueError:
return None
@property
def freqstr(self):
return self.offset.freqstr
```
you would think that `freqstr` returns a string and `inferred_freq` and `freq` return "frequencies" (i.e. offsets). In reality `inferred_freq` is a string as well.
I am not sure of the best way to deal with this issue. At the very least I would suggest adding a `inferred_freq_offset` property. Perhaps also deprecate `inferred_freq` and create a `inferred_freqstr`.
Tracing this method, you find `_FrequencyInferer::get_freq` which returns a string rather than an Offset.
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} | 3 | 2013-10-02T12:01:23Z | 2014-06-25T18:39:41Z | 2013-10-02T23:36:29Z | CONTRIBUTOR | null | FrozenNDArray had made it into the MI pickle. No reason to do that and just
complicates pickle compat going forward. Now they just output ndarray
instead (which also avoids the unnecessary nested pickle previously
occurring).
Fixes #5076.
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} | [] | closed | false | null | [] | null | 1 | 2013-10-02T13:54:17Z | 2013-10-08T11:46:21Z | 2013-10-08T11:46:21Z | NONE | null | I use the following code to show the force of wind as a function of the direction.
fig = plt.figure()
ax = fig.add_axes([0.0, 0.0, 1., 1.], polar=True)
ax.plot(temp.direction, temp.force, color='blue', lw = 0, style = 'o');
How can I tell chaco to just draw the points and no lines between the points
thanks Mathias
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} | 25 | 2013-10-02T14:13:43Z | 2014-06-16T06:29:44Z | 2013-10-08T03:52:50Z | CONTRIBUTOR | null | ...ting
:sparkles:
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} | 1 | 2013-10-02T15:49:19Z | 2014-02-20T14:24:06Z | 2014-02-20T14:24:06Z | MEMBER | null | Proper message for overlapping locals/frame (tested):
```
In [28]: a = Series(randint(3, size=5), name='a')
In [29]: b = Series(randint(10, size=5), name='b')
In [30]: df = DataFrame({'a': a, 'b': b})
In [31]: df.query('b - 1 in a')
NameResolutionError: resolvers and locals overlap on names ['b']
```
Expression works when there's no local (tested)
```
In [50]: del b
In [51]: a
Out[51]:
0 2
1 2
2 0
3 1
4 2
Name: a, dtype: int64
In [52]: df.b - 1
Out[52]:
0 3
1 4
2 7
3 7
4 -1
Name: b, dtype: int64
In [53]: df.query('b - 1 in a')
Out[53]:
Empty DataFrame
Columns: [a, b]
Index: []
```
Works fine when I use the local by itself (tested):
```
In [34]: df.query('@b in a')
Out[34]:
a b
4 2 0
```
However, when I try to reference the local `b` using `@b` _in an expression_, things go awry (not tested):
```
In [55]: b = Series(randint(10, size=5), name='b')
In [56]: df.query('@b - 1 in a')
UndefinedVariableError: local variable '@b' is not defined
```
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} | 0 | 2013-10-02T16:16:55Z | 2014-06-24T23:01:59Z | 2013-10-02T19:10:22Z | CONTRIBUTOR | null | closes #4294
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} | 3 | 2013-10-02T16:51:14Z | 2014-06-23T22:01:54Z | 2013-10-02T20:54:59Z | CONTRIBUTOR | null | closes #4078
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} | 13 | 2013-10-02T21:44:18Z | 2014-06-21T03:19:29Z | 2013-10-07T21:25:49Z | CONTRIBUTOR | null | closes #5074
Do you really prefer using a mask? It seems to have a lower memory footprint but it requires some tricks for corner cases.
```
num = self._get_level_number(level)
unique_vals = self.levels[num] # .values
labels = self.labels[num]
mask = labels == -1
if len(unique_vals) > 0:
values = unique_vals.take(labels)
else:
values = np.empty(len(labels))
values[:] = np.nan
values = pd.Index(values)
if mask.any():
values = values.get_values()
values[mask] = np.nan
values = pd.Index(values)
values.name = self.names[num]
return values
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5091 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5091/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5091/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5091/events | https://github.com/pandas-dev/pandas/issues/5091 | 20,426,508 | MDU6SXNzdWUyMDQyNjUwOA== | 5,091 | Missing Periods for some DateOffsets | {
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} | 1 | 2013-10-02T22:28:23Z | 2021-04-11T03:36:47Z | null | CONTRIBUTOR | null | There are not corresponding `Period`s for many `DateOffsets`.
For example see how the freq changes:
``` python
ts = pd.date_range('1/1/2012', periods=4, freq=pd.offsets.BQuarterEnd())
per = ts.to_period()
ts_per = per.to_timestamp()
In [210]: ts
Out[210]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-03-30 00:00:00, ..., 2012-12-31 00:00:00]
Length: 4, Freq: BQ-MAR, Timezone: None
In [211]: per
Out[211]:
<class 'pandas.tseries.period.PeriodIndex'>
freq: Q-DEC
[2012Q1, ..., 2012Q4]
length: 4
In [212]: ts_per
Out[212]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-01-01 00:00:00, ..., 2012-10-01 00:00:00]
Length: 4, Freq: QS-OCT, Timezone: None
```
This issue is alluded to in comment: https://github.com/pydata/pandas/blob/master/pandas/tseries/frequencies.py#L329
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] | closed | false | null | [] | null | 3 | 2013-10-03T00:01:59Z | 2014-07-09T15:51:54Z | 2013-10-08T11:47:27Z | NONE | null | Hi,
I was trying to highlight some data in a DataFrame using the `axvspan` method from matplotlib and I ran into some ill behavior. Here's a minimal example:
```
import numpy as np
import pandas as pd
import matplotlib.dates as mdates
from pylab import *
ion()
dates = pd.date_range(start="10/01/2013", end="10/04/2013", freq='H')
data = np.random.random_sample(len(dates))
my_df = pd.DataFrame({'r': data}, index=dates)
figure()
my_df.plot()
axvspan(*mdates.datestr2num(["10/02/2013", "10/03/2013"]),
facecolor='red', alpha=0.5)
```
This produces the following figure:
However, when I instead invoke the plotter via `my_df.plot(x_compat=True)`, things are kosher:
save for the fact that the advanced x-axis formatting has been lost (easy enough to replace). I'm not sure if this is an unintended feature or a bug, but it's not documented anywhere.
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} | [] | closed | false | null | [] | null | 5 | 2013-10-03T00:44:30Z | 2013-10-03T01:16:06Z | 2013-10-03T01:16:06Z | NONE | null | I got caught on this in my code and it took a while to figure out the issue. Is it expected behavior that switching the order of column and row selection on a data frame will not always make the assignment?
``` python
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: columns = list('abcdefg')
In [4]: rows = list('12345678')
In [5]: df = pd.DataFrame(np.random.random((len(rows), len(columns))), columns=columns, index=rows)
In [6]: df[['b', 'c']].iloc[4] = np.array([10.0, 10.0])
In [7]: df
Out[7]:
a b c d e f g
1 0.785981 0.267089 0.146241 0.859770 0.910126 0.434754 0.948456
2 0.808450 0.275487 0.491411 0.252733 0.887204 0.892640 0.989163
3 0.304675 0.516701 0.127553 0.930740 0.983090 0.768808 0.742200
4 0.315953 0.757930 0.985903 0.018819 0.612413 0.590886 0.940801
5 0.891840 0.667422 0.350970 0.825626 0.624634 0.439662 0.765968
6 0.191476 0.578559 0.798281 0.778773 0.888730 0.308247 0.408610
7 0.167602 0.602783 0.103249 0.673498 0.729862 0.748524 0.765943
8 0.591429 0.952579 0.916077 0.905369 0.214884 0.289155 0.544441
In [8]: df.iloc[4][['b', 'c']] = np.array([10.0, 10.0])
In [9]: df
Out[9]:
a b c d e f g
1 0.785981 0.267089 0.146241 0.859770 0.910126 0.434754 0.948456
2 0.808450 0.275487 0.491411 0.252733 0.887204 0.892640 0.989163
3 0.304675 0.516701 0.127553 0.930740 0.983090 0.768808 0.742200
4 0.315953 0.757930 0.985903 0.018819 0.612413 0.590886 0.940801
5 0.891840 10.000000 10.000000 0.825626 0.624634 0.439662 0.765968
6 0.191476 0.578559 0.798281 0.778773 0.888730 0.308247 0.408610
7 0.167602 0.602783 0.103249 0.673498 0.729862 0.748524 0.765943
8 0.591429 0.952579 0.916077 0.905369 0.214884 0.289155 0.544441
```
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...which was apparently fixed by #4657. More tests looking for shape-related bugs in filter would be nice. So far I have no discovered any.
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closes #4960
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} | 1 | 2013-10-03T16:19:12Z | 2015-09-20T21:15:40Z | 2015-09-20T21:15:40Z | CONTRIBUTOR | null | xref #9252
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} | 18 | 2013-10-03T19:40:15Z | 2014-07-16T08:33:01Z | 2013-10-28T23:20:03Z | CONTRIBUTOR | null | Rebased on master (unable to use same pull request). Closes #4580.
see http://stackoverflow.com/questions/11687478/convert-a-filename-to-a-file-url
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