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} | 2 | 2013-07-19T23:47:44Z | 2016-10-12T23:04:01Z | 2013-07-25T14:25:01Z | MEMBER | null | This has been suggested in the pydata mailing list. seems like a simple fix and would be a nice API improvement
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} | 0 | 2013-07-20T00:38:24Z | 2014-07-10T06:34:22Z | 2013-07-20T01:36:54Z | CONTRIBUTOR | null | closes #4292
essentially was doing a assignment of `np.nan` to a numpy array of dtype `datetime64[ns]`
which works correct (set's the value to `iNaT`) on little endian, for some reason on big
endian doesn't work, explicity change the `nan` to `iNaT`
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https://api.github.com/repos/pandas-dev/pandas/issues/4303 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4303/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4303/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4303/events | https://github.com/pandas-dev/pandas/issues/4303 | 17,001,261 | MDU6SXNzdWUxNzAwMTI2MQ== | 4,303 | TST: test_spam_url fails because of Invalid URL | {
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} | 6 | 2013-07-20T02:52:05Z | 2016-10-12T23:04:01Z | 2013-07-25T13:40:59Z | CONTRIBUTOR | null | Intermittently failing test in travis with data.py with the "unused imports" #4251. @cpcloud any ideas? (failed then passed with the same build)
``` python
======================================================================
ERROR: test_spam_url (pandas.io.tests.test_html.TestReadHtmlBase)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pandas-0.12.0.dev_d61aa5b-py2.7-linux-x86_64.egg/pandas/util/testing.py", line 763, in network_wrapper
return t(*args, **kwargs)
File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pandas-0.12.0.dev_d61aa5b-py2.7-linux-x86_64.egg/pandas/io/tests/test_html.py", line 131, in test_spam_url
df1 = self.run_read_html(url, '.*Water.*')
File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pandas-0.12.0.dev_d61aa5b-py2.7-linux-x86_64.egg/pandas/io/tests/test_html.py", line 91, in run_read_html
return read_html(*args, **kwargs)
File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pandas-0.12.0.dev_d61aa5b-py2.7-linux-x86_64.egg/pandas/io/html.py", line 906, in read_html
attrs)
File "/home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pandas-0.12.0.dev_d61aa5b-py2.7-linux-x86_64.egg/pandas/io/html.py", line 775, in _parse
raise retained
ValueError: Invalid URL: "http://ndb.nal.usda.gov/ndb/foods/show/1732?fg=&man=&lfacet=&format=&count=&max=25&offset=&sort=&qlookup=spam"
----------------------------------------------------------------------
```
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} | 9 | 2013-07-20T04:58:52Z | 2014-06-20T19:13:10Z | 2014-02-16T22:16:38Z | CONTRIBUTOR | null | This fixes #2971 and #4110. This also provides test coverage for the different values for the if_exists argument for io.sql.write_frame.
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} | 1 | 2013-07-20T19:54:59Z | 2013-07-20T20:59:21Z | 2013-07-20T20:59:21Z | CONTRIBUTOR | null | related #4299
cc @Komnomnomnom
weird that this doesn't break on travis (but did on my vagrant machine), and on debian testing....
any thoughts?
```
> /home/vagrant/pandas_master/pandas/io/tests/test_json/test_ujson.py(109)test_encodeDoubleTinyExponential()
-> self.assertEqual(num, ujson.decode(ujson.encode(num)))
(Pdb) l
104 num = 1e-100
105 self.assertEqual(num, ujson.decode(ujson.encode(num)))
106 num = -1e-45
107 self.assertEqual(num, ujson.decode(ujson.encode(num)))
108 num = -1e-145
109 -> self.assertEqual(num, ujson.decode(ujson.encode(num)))
110
111 def test_encodeDictWithUnicodeKeys(self):
112 input = { u"key1": u"value1", u"key1": u"value1", u"key1": u"value1", u"key1": u"value1", u"key1": u"value1", u"key1": u"value1" }
113 output = ujson.encode(input)
114
(Pdb) p num
-1e-145
(Pdb) ujson.decode(ujson.encode(num))
-1.0000000000000001e-145
```
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} | 3 | 2013-07-21T14:50:56Z | 2016-10-12T23:04:01Z | 2013-07-21T17:10:32Z | CONTRIBUTOR | null | @cpcloud
cc @jtratner
https://travis-ci.org/pydata/pandas/jobs/9323458
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} | 5 | 2013-07-21T21:32:43Z | 2019-11-20T09:52:40Z | 2018-06-21T19:22:41Z | CONTRIBUTOR | null | I don't _think_ this is possible atm, but would be a nice enhancement.
Similar to how you can pass list of columns to sort*.
http://stackoverflow.com/questions/17775935/sql-like-window-functions-in-pandas-row-numbering-in-python-pandas-dataframe
_\_probably there is a cheeky answer/hack to this q using sort...*
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} | 4 | 2013-07-22T01:10:53Z | 2020-09-21T08:15:01Z | 2013-08-21T23:15:01Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/17778139/pandas-unable-to-change-column-data-type/17778560#17778560
This might be trying to coerce `object` dtype to a real dtype (int/float) and is failing
Should prob raise for now (or work). Not working with iloc/loc.
```
In [66]: df = DataFrame([['1','2','3','.4',5,6.,'foo']],columns=list('ABCDEFG'))
In [67]: df.dtypes
Out[67]:
A object
B object
C object
D object
E int64
F float64
G object
dtype: object
In [68]: df.iloc[:,0:3] = df.iloc[:,0:3].astype(int)
In [69]: df.dtypes
Out[69]:
A object
B object
C object
D object
E int64
F float64
G object
dtype: object
In [70]: df.iloc[:,0:3] = df.iloc[:,0:3].convert_objects(convert_numeric=True)
In [71]: df.dtypes
Out[71]:
A object
B object
C object
D object
E int64
F float64
G object
dtype: object
```
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} | 21 | 2013-07-22T05:09:16Z | 2014-06-14T12:30:29Z | 2013-10-12T14:11:50Z | CONTRIBUTOR | null | This commit adds support for formatting datetime object output from to_csv()
closes #2583
``` python
In [3]: spx = DataReader('^GSPC', data_source='yahoo')
In [4]: spx.head()
Out[4]:
Open High Low Close Volume Adj Close
Date
2010-01-04 1116.56 1133.87 1116.56 1132.99 3991400000 1132.99
2010-01-05 1132.66 1136.63 1129.66 1136.52 2491020000 1136.52
2010-01-06 1135.71 1139.19 1133.95 1137.14 4972660000 1137.14
2010-01-07 1136.27 1142.46 1131.32 1141.69 5270680000 1141.69
2010-01-08 1140.52 1145.39 1136.22 1144.98 4389590000 1144.98
In [5]: spx.to_csv('spx_temp.csv', date_format='%Y%m%d')
In [6]: !head spx_temp.csv
Date,Open,High,Low,Close,Volume,Adj Close
20100104,1116.56,1133.87,1116.56,1132.99,3991400000,1132.99
20100105,1132.66,1136.63,1129.66,1136.52,2491020000,1136.52
20100106,1135.71,1139.19,1133.95,1137.14,4972660000,1137.14
20100107,1136.27,1142.46,1131.32,1141.69,5270680000,1141.69
20100108,1140.52,1145.39,1136.22,1144.98,4389590000,1144.98
20100111,1145.96,1149.74,1142.02,1146.98,4255780000,1146.98
20100112,1143.81,1143.81,1131.77,1136.22,4716160000,1136.22
20100113,1137.31,1148.4,1133.18,1145.68,4170360000,1145.68
20100114,1145.68,1150.41,1143.8,1148.46,3915200000,1148.46
```
The `date_format=` keyword will be applied to every element of a DatetimeIndex (index or columns) and DatetimeBlock (values). It works for both the Python engine and the new Cython engine:
``` python
In [7]: datetimes = DataFrame({spx.index[0]: spx.index}, index=spx.index).head()
In [8]: datetimes
Out[8]:
2010-01-04
Date
2010-01-04 2010-01-04 00:00:00
2010-01-05 2010-01-05 00:00:00
2010-01-06 2010-01-06 00:00:00
2010-01-07 2010-01-07 00:00:00
2010-01-08 2010-01-08 00:00:00
In [9]: datetimes.to_csv('datetimes_temp.csv', date_format='%m/%d/%Y')
In [10]: !head datetimes_temp.csv
Date,01/04/2010
01/04/2010,01/04/2010
01/05/2010,01/05/2010
01/06/2010,01/06/2010
01/07/2010,01/07/2010
01/08/2010,01/08/2010
In [11]: datetimes.to_csv('datetimes_temp.csv', date_format='%m/%d/%Y', engine='python')
In [12]: !head datetimes_temp.csvDate,01/04/2010
01/04/2010,01/04/2010
01/05/2010,01/05/2010
01/06/2010,01/06/2010
01/07/2010,01/07/2010
01/08/2010,01/08/2010
```
Let me know if there are any questions or issues.
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} | 14 | 2013-07-22T11:55:22Z | 2015-11-29T18:03:36Z | 2015-11-29T18:03:36Z | CONTRIBUTOR | null | xref #8184
to say round by minute
http://stackoverflow.com/questions/17781159/round-pandas-datetime-index
by hour
https://github.com/pydata/pandas/issues/4314
might be simpler to use numpy astype here
http://stackoverflow.com/questions/23315163/merging-two-pandas-timeseries-shifted-by-1-second
```
In [75]: index = pd.DatetimeIndex([ Timestamp('20120827 12:05:00.002'), Timestamp('20130101 12:05:01'), Timestamp('20130712 15:10:00'), Timestamp('20130712 15:10:00.000004') ])
In [79]: index.values
Out[79]:
array(['2012-08-27T08:05:00.002000000-0400',
'2013-01-01T07:05:01.000000000-0500',
'2013-07-12T11:10:00.000000000-0400',
'2013-07-12T11:10:00.000004000-0400'], dtype='datetime64[ns]')
In [78]: pd.DatetimeIndex(((index.asi8/(1e9*60)).round()*1e9*60).astype(np.int64)).values
Out[78]:
array(['2012-08-27T08:05:00.000000000-0400',
'2013-01-01T07:05:00.000000000-0500',
'2013-07-12T11:10:00.000000000-0400',
'2013-07-12T11:10:00.000000000-0400'], dtype='datetime64[ns]')
```
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} | 11 | 2013-07-22T14:10:54Z | 2016-07-21T15:44:08Z | 2015-11-16T00:02:02Z | NONE | null | related #3190
Currently it is only possible to specify justify settings for column headers. This would extremely useful when used in conjunction with IPython Notebook.
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} | 7 | 2013-07-22T14:42:51Z | 2014-06-26T17:39:32Z | 2013-08-26T19:14:23Z | NONE | null | Adds ability to set the bandwidth used for the kde and the indices at which it is evaluated. The first is passed to the bw_method kwarg for scipy.stats.gaussian_kde (for scipy >= 0.11.0); the second is passed to gkde.evaluate. For the case when scipy < 0.11.0 but bw_method is specified, bw_method is ignored (not passed to scipy.stats.gaussian_kde), but a warning is issued to alert the user.
https://github.com/pydata/pandas/issues/4298
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} | 8 | 2013-07-22T16:11:19Z | 2014-06-12T15:05:29Z | 2013-09-13T15:19:08Z | CONTRIBUTOR | null | The problem was basically an odd restriction to `list` ,`tuple`, `dict`, or `numpy.ndarray` for data structure given as input to any constructor for DataFrame. Much cruder than a possible issue around buffer protocols I thought (the whole issue report went off on a tangent anyway ;-) ).
The source did not have much (any, actually) comments in that region, and I could not find unit tests documenting the behaviour, but I am guessing that the restriction stemmed from the fact that a string is a sequence in Python while the constructors should consider one string passed as data a scalar rather than a sequence.
The change made goes the other way around and accepts any sequence (not just `tuple`, `list`, or `numpy.ndarray`), except `str` or `unicode` (the later only applies to Python 2.x). `bytes` and `bytearray` are allowed (but I am not sure they should).
The pull request was tested (only `tests/test_frame.py` was run) on Python 2.7.4 and 3.3.1
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} | 13 | 2013-07-22T17:16:59Z | 2013-07-30T00:07:39Z | 2013-07-30T00:07:39Z | NONE | null | better docs for `keep_default_na` and `na_values` when used together
I have a field set where "NA" is meaningful, but not meaning "not applicable" (it refers to the name of my school's Homeroom). There's no obvious way to handle this edge case...
On 0.11.0
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} | 5 | 2013-07-22T20:37:41Z | 2013-09-05T21:12:52Z | 2013-09-05T21:12:51Z | MEMBER | null | Strange bug, probably has to do with not propagating errors in Cython
``` python
import numpy as np
import numbers
def randbool(p=0.5, size=None):
if size is None:
size = ()
if isinstance(size, numbers.Integral):
size = (size,)
return np.random.rand(*size) <= p
```
```
In [1]: x = randbool(size=(10,5))
In [2]: s = Series([False]*5)
In [3]: andit = lambda x, s: x & s
In [4]: andit(x, s)
ValueError: operands could not be broadcast together with shapes (10) (10,5)
In [5]: from nose.tools import assert_raises
In [6]: assert_raises(ValueError, andit, x, s)
AssertionError: ValueError not raised
```
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} | 0 | 2013-07-22T20:42:44Z | 2014-07-16T08:20:11Z | 2013-07-25T01:15:06Z | NONE | null | #3376
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} | 5 | 2013-07-22T21:10:29Z | 2014-06-27T11:45:28Z | 2013-09-01T11:49:20Z | NONE | null | A wider span means a smaller alpha, which means each x_t has less impact on the ewma.
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} | 10 | 2013-07-22T21:36:55Z | 2013-09-24T00:04:00Z | 2013-08-23T17:51:59Z | CONTRIBUTOR | null | From [this SO question](http://stackoverflow.com/questions/17797384/date-and-time-in-pandas-read-csv), with input to include thousands:
```
In [1]: s = '06.02.2013;13:00;1.000,215;0,215;0,185;0,205;0,00'
In [2]: pd.read_csv(StringIO(s), sep=';', header=None, parse_dates={'Dates': [0, 1]}, index_col=0, decimal=',')
Out[2]:
2 3 4 5 6
Dates
2013-06-02 13:00:00 10.000,215 0.215 0.185 0.205 0
In [3]: pd.read_csv(StringIO(s), sep=';', header=None, parse_dates={'Dates': [0, 1]}, index_col=0, decimal=',', thousands='.')
Out[3]:
2 3 4 5 6
Dates
6022013 13:00 1.000,215 0.215 0.185 0.205 0
```
_Note: the Dates column (as well as the thousands not being converted._
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} | 22 | 2013-07-22T21:52:25Z | 2014-07-24T09:06:28Z | 2013-07-24T22:13:36Z | CONTRIBUTOR | null | partial solutions to #4163
Merge this into the sql branch so we can start incorporating bug fixes into the new file structure of `sql.py` / `sql_legacy.py`.
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} | 25 | 2013-07-22T23:45:56Z | 2013-10-28T01:32:24Z | 2013-09-29T20:03:21Z | CONTRIBUTOR | null | After #3482 is merged, some additional methods to fixup
- [x] Series.replace (use generic/replace) via f4fe5fd03816deab1dc71e958ebbb4d9c1b9d35d
- [x] Series align (use generic/align), PR #4800
- [x] Move Arithmetic/Comparison methods to generic, #4051, #5034
- [ ] Fillna column wise, #4514
- [x] Verify Copy for Series (deep=True), #2721, PR #4627
- [x] Verify that `deep=True` is implemented in `core/internals/copy`, related to #4039, PR #4627
- [ ] consolidate tests as appropriate to `tests/test_generic.py` (ones that should have a conforming API, e.g. `replace,filter`) see #4118, (test_generic is created via #4627)
- [x] add `AXIS_LEN` check in `filter`, PR #4855
- [x] rename consistency between series/frame, see #4605, PR #4627
- [x] clean up reindex / fix method= issues #4604, PR is #4610
- [x] interpolation, consolidate `Series/DataFrame` interpolation to
`internals.Block.interpolatte`, #4434, cc @TomAugspurger
- [x] change setitem_with_indexer to use `Block.setitem` (core/indexing), PR #4624
- [x] move _get_numeric_data/_get_bool_data to generic.py PR #4675
- [x] fix tools/plotting for series sub of NDFrame PR #4675
- [x] ~~remove use of common/_maybe_upcast_putmask and possibly_cast_item~~
- [x] clip ( see #2747), need to move from frame.py to generic.py (series is ok), PR #4798
- [x] take ( PR #4757 )
- [ ] update, #3025
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} | [] | closed | false | null | [] | null | 18 | 2013-07-22T23:50:28Z | 2013-07-24T03:26:37Z | 2013-07-23T00:03:23Z | CONTRIBUTOR | null | The store object does not display anymore the columns and data columns for me, but I don't know if this is design spec, I am just reporting a change of behavior, apologies if this is expected.
``` python
store = pd.HDFStore('/u/paige/maye/raid/rdr20_month_samples/nominal/C9.h5')
pd.__version__
'0.12.0rc1'
store
<class 'pandas.io.pytables.HDFStore'>
File path: /u/paige/maye/raid/rdr20_month_samples/nominal/C9.h5
/df frame_table (shape->390557601)
```
Also, when executing this in IPython it takes a lot of time (approx. 2 min), is IPython caching anything of this 390 mio lines object??
When using this path, it looks fine:
``` python
store.root.df.table.description
{
"index": Int64Col(shape=(), dflt=0, pos=0),
"values_block_0": Float64Col(shape=(13,), dflt=0.0, pos=1),
"values_block_1": Int64Col(shape=(7,), dflt=0, pos=2),
"values_block_2": StringCol(itemsize=12, shape=(2,), dflt='', pos=3),
"clat": Float64Col(shape=(), dflt=0.0, pos=4),
"clon": Float64Col(shape=(), dflt=0.0, pos=5),
"cloctime": Float64Col(shape=(), dflt=0.0, pos=6)}
```
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} | [] | closed | false | null | [] | null | 3 | 2013-07-23T04:48:32Z | 2013-07-23T09:08:45Z | 2013-07-23T09:08:45Z | NONE | null | I have a unittest set up that checks to make sure that a DataFrame has a NaN value in it, but am getting unexpected result. Here is code that shows the unexpected behavior:
from io import StringIO
def null():
""" return a nan: is there a better way to do this? """
return pd.read_csv(StringIO("NA\n"), header=None).loc[0][0]
null() == null() # returns False
So how do I ensure that I am getting the expected "missing data" with this behavior?
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} | 14 | 2013-07-23T06:54:57Z | 2016-01-08T14:07:46Z | 2016-01-08T14:07:46Z | NONE | null | Running Ubuntu 12.10. The recommended way of running tests (http://pandas.pydata.org/pandas-docs/stable/install.html#running-the-test-suite)
```
nosetests pandas
```
did not work. I had to use:
```
nosetests --exe pandas
```
It would also be useful to know how to avoid the:
```
OperationalError: (1046, 'No database selected')
```
errors.
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} | 5 | 2013-07-23T10:22:45Z | 2017-05-09T20:19:01Z | 2013-09-05T02:37:08Z | NONE | null | Using release 0.11.0 with Python 2.7.1, but it doesn't look like anything has changed in git since.
I can write DataFrames to a file using to_csv fine unless I pass the option: quoting=pandas.io.parsers.csv.QUOTE_NONE in which case I get exceptions of the form:
packages/pandas/core/format.py", line 1054, in _save_chunk
lib.write_csv_rows(self.data, ix, self.nlevels, self.cols, self.writer)
File "lib.pyx", line 832, in pandas.lib.write_csv_rows (pandas/lib.c:13466)
_csv.Error: need to escape, but no escapechar set
A short search reveals links like http://www.velocityreviews.com/forums/t612166-how-can-i-use-quotes-without-escaping-them-using-csv.html implying that due to some csv oddities we should be setting something like quotechar=None when calling csv.writer.
I tried modifying pandas/core/format.py to add quotechar=None to the call to csv.writer and this does indeed seem to fix the problem. Unfortunately I don't have everything set up to build from git so I can't submit a patch.
Thanks.
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} | 18 | 2013-07-23T10:29:55Z | 2018-07-06T19:37:10Z | 2018-07-06T19:37:10Z | CONTRIBUTOR | null | Now we have a neat read_json function (#3804), it'd be really neat if we could read in from mongo using this i.e. without have to create an intermediary python object.
_I wasn't even sure how to return a json (bson) string from a mongo query, though surely this ought to be relatively easy..._
I've no idea how much of an issue the BSON parts are, or whether these can be converted after the fact...
http://stackoverflow.com/questions/17805304/how-can-i-load-data-from-mongodb-collection-into-pandas-dataframe
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} | 5 | 2013-07-23T19:52:03Z | 2013-09-29T17:04:02Z | 2013-09-29T17:04:02Z | MEMBER | null | ```
In [11]: s = True
In [12]: df = DataFrame(rand(10, 5)> 0.5)
In [13]: df
Out[13]:
0 1 2 3 4
0 True False False True False
1 True False False True False
2 False False True False False
3 True False False True False
4 False False True True False
5 False False False True True
6 True True False True True
7 True True False False True
8 True True True True False
9 False False True False False
In [14]: s & df
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-14-cab092ff8433> in <module>()
----> 1 s & df
TypeError: unsupported operand type(s) for &: 'bool' and 'DataFrame'
In [15]: df & s
Out[15]:
0 1 2 3 4
0 True False False True False
1 True False False True False
2 False False True False False
3 True False False True False
4 False False True True False
5 False False False True True
6 True True False True True
7 True True False False True
8 True True True True False
9 False False True False False
```
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} | 33 | 2013-07-23T20:48:05Z | 2014-04-24T10:27:41Z | 2014-04-24T10:27:41Z | CONTRIBUTOR | null | ExcelFile should print out line or even cell warnings
Today I was spending quite some time debugging why a decoding error stopped the code from reading in a table.
I thought the skiprows counts from 0 (= Excel row 1). It was always failing.
In one line there were column headers, the next line contained units which couldn't probably be parsed.
I think it could be helpful if the parser would show the line number or even cell were it fails to ready (like due to decoding errors).
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} | 16 | 2013-07-23T21:43:53Z | 2014-06-23T17:14:24Z | 2013-07-24T22:12:57Z | CONTRIBUTOR | null | First step for #4163, replacing my PR #4323 aimed at the master branch.
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} | 2 | 2013-07-23T21:47:16Z | 2014-07-15T15:10:11Z | 2013-07-24T00:51:05Z | MEMBER | null | closes #4331
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} | 8 | 2013-07-23T21:57:51Z | 2013-10-17T14:18:58Z | 2013-10-17T14:18:58Z | NONE | null | Steps to reproduce:
``` python
>>> import pandas as pd
>>> pd.__version__
'0.10.1'
>>> import StringIO
>>> data = 'a,b,c,d\n1,2,3,foo\n4,5,6,bar\n7,8,9,baz'
>>> df = pd.read_csv(StringIO.StringIO(data), usecols=[1])
>>> df
b
0 2
1 5
2 8
>>> df = pd.read_csv(StringIO.StringIO(data), usecols=[1], skipfooter=1)
>>> df
a b c d
0 1 2 3 foo
1 4 5 6 bar
```
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} | 2 | 2013-07-23T23:17:32Z | 2013-09-27T02:26:30Z | 2013-09-27T02:26:30Z | MEMBER | null | A new module `core.align` would hold a couple of generic alignment functions, one of which will be `align_multiple` or possibly `align2` (taking 2 pandas objects) and then `align` would take a sequence of pandas objects to align.
Need to think about the API a bit more.
Psuedocode-ish:
``` python
df = DataFrame(...)
df2 = DataFrame(...) # possibly different index and columns than df
s = Series(...)
result = pd.align([df, df2, s], how='outer') # compute the union of the indexes
# smoke test 1
result.columns == df.columns.join(df2.columns, how='outer').join(s.index, how='outer')
result.index == df.index.join(df2.index, how='outer')
result2 = pd.align([df, df2, s], how='inner') # intersection
# smoke test 2
result2.columns == df.columns.join(df2.columns).join(s.index)
result2.index == df.index.join(df2.index)
```
input with `Index` types would return the union/intersection of the objects. not clear what would happen if aligning an `Index` and another non-`Index` pandas object
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} | [] | closed | false | null | [] | null | 13 | 2013-07-24T03:11:12Z | 2013-09-23T03:11:17Z | 2013-07-24T10:28:57Z | CONTRIBUTOR | null | On pandas v0.11.0, For example:
```
foo = pd.Series(np.array([np.datetime64(datetime.date(2012, 1, 1), 'M')]))
```
```
In [64]: foo.dtype
Out[64]: dtype('<M8[M]')
```
but then:
```
In [65]: pd.DataFrame({"date":foo,}).date.dtype
Out[65]: dtype('<M8[ns]')
```
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} | 11 | 2013-07-24T03:17:03Z | 2018-10-26T10:58:25Z | 2018-10-26T10:58:19Z | CONTRIBUTOR | null | For example:
```
In [77]: pd.Series([datetime.datetime(2012, 1, 1)]).dtype
Out[77]: dtype('<M8[ns]')
```
works as expected.
However:
```
In [78]: pd.Series([datetime.date(2012, 1, 1)]).dtype
Out[78]: dtype('O')
```
create a Series of type object rather than datetime64.
You can get the desired effect:
```
In [89]: pd.Series(np.array([np.datetime64(datetime.date(2012, 1, 1))])).dtype
Out[89]: dtype('<M8[D]')
```
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} | [] | closed | false | null | [] | null | 1 | 2013-07-24T08:54:49Z | 2013-07-24T22:05:18Z | 2013-07-24T22:05:18Z | CONTRIBUTOR | null | Hello,
my excel file contains units in a row:
"m²", "°", etc.
It appears that this generates unicode errors:
```
UnicodeDecodeError: 'utf8' codec can't decode byte 0xb0 in position 339: invalid start byte
```
Please see the discussion / documentation here:
- https://groups.google.com/forum/#!searchin/openpyxl-users/UnicodeDecodeError/openpyxl-users/dH0Vbkwuo_w/-fbN1nHifK0J
- http://openpyxl.readthedocs.org/en/latest/api.html?highlight=encode
So my suggestion would be to incorporate an encoding option in the openpyxl reader.
But I cannot see that it's used in:
https://github.com/pydata/pandas/blob/master/pandas/io/excel.py
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} | 7 | 2013-07-24T08:58:03Z | 2013-09-22T15:19:45Z | 2013-09-22T14:23:43Z | CONTRIBUTOR | null | Hello,
my excel file is like:
- metadata (10 rows)
- header (1 row)
- units (1 row)
- data (250 rows)
Now, I am trying to read it like:
df = xlsall.parse(sheetname='mydata', skiprows=11, header=10)
But it would not assign the information in row 11 as column labels but rather use the units in row 12 (counting from 1).
What could be the cause?
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} | 2 | 2013-07-24T10:27:37Z | 2019-07-24T00:42:15Z | 2019-07-24T00:42:15Z | CONTRIBUTOR | null | see #4337
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} | 4 | 2013-07-24T13:10:36Z | 2014-05-10T15:32:36Z | 2014-05-10T15:32:36Z | NONE | null | Here is the code:
``` python
import pandas as pd
idx1 = pd.PeriodIndex(["2013-01", "2013-01", "2013-02", "2013-02"], freq="M")
idx2 = ["a","b","a","b"]
value = [1,2,3,4]
idx = pd.MultiIndex.from_arrays([idx1, idx2])
s = pd.Series(value, index=idx)
s.unstack()
```
It will raise following exception:
```
anaconda/lib/python2.7/site-packages/pandas-0.12.0.dev_424e187-py2.7-linux-x86_64.egg/pandas/tseries/period.pyc in _from_arraylike(cls, data, freq, tz)
649
650 if freq is None:
--> 651 raise ValueError(('freq not specified and cannot be '
652 'inferred from first element'))
653
ValueError: freq not specified and cannot be inferred from first element
```
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} | 3 | 2013-07-24T14:15:27Z | 2017-03-10T14:36:34Z | 2017-03-10T14:36:33Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/17818783/populate-a-pandas-sparsedataframe-from-a-scipy-sparse-matrix
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} | 4 | 2013-07-24T14:17:35Z | 2013-07-24T22:00:37Z | 2013-07-24T22:00:37Z | CONTRIBUTOR | null | useful in comparsion to other series
easy with integers but tricky with say floats
anyone think of a better way?
http://stackoverflow.com/questions/17836105/finding-rows-in-pandas-data-frame-with-same-values
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} | 3 | 2013-07-24T16:33:17Z | 2013-07-30T20:28:58Z | 2013-07-30T20:28:58Z | CONTRIBUTOR | null | The reason is that it tests for particular line colors, but I have my matplotlib colors tweaked, so the default values aren't `r`, `g`, and `b`. The test should probably look at the default `color_cycle` in matplotlib and check against those values.
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} | 5 | 2013-07-24T19:10:18Z | 2014-06-25T22:22:35Z | 2013-09-24T01:30:08Z | CONTRIBUTOR | null | Here's a snippet depicting the, probably, buggy behaviour:
``` python
>>> import pandas as pd
>>> s = pd.Series()
>>> s.reindex([1,2,3])
1 NaN
2 NaN
3 NaN
dtype: float64
>>> s.reindex([1,2,3], fill_value=0.0)
1 NaN
2 NaN
3 NaN
dtype: float64
>>> pd.version.version
'0.12.0.dev-d7c6eb1'
```
I'd expect the last output to contain `0.0` values instead of `NaN`.
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} | 1 | 2013-07-24T20:37:43Z | 2018-07-06T20:21:01Z | 2017-07-11T09:55:12Z | CONTRIBUTOR | null | from the ML: https://groups.google.com/forum/#!topic/pystatsmodels/bCatleGJ4UU
```
In [15]: p = Panel(randn(10,100,5),major_axis=pd.date_range('20130101',periods=100))
In [16]: p
Out[16]:
<class 'pandas.core.panel.Panel'>
Dimensions: 10 (items) x 100 (major_axis) x 5 (minor_axis)
Items axis: 0 to 9
Major_axis axis: 2013-01-01 00:00:00 to 2013-04-10 00:00:00
Minor_axis axis: 0 to 4
In [17]: p.resample('M',axis=1)
Out[17]:
<class 'pandas.core.panel.Panel'>
Dimensions: 10 (items) x 4 (major_axis) x 5 (minor_axis)
Items axis: 0 to 9
Major_axis axis: 2013-01-31 00:00:00 to 2013-04-30 00:00:00
Minor_axis axis: 0 to 4
In [18]: p.resample('M',axis=1,loffset='-10D')
AttributeError: 'Panel' object has no attribute 'index'
```
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} | [] | closed | false | null | [] | null | 3 | 2013-07-24T22:52:07Z | 2013-07-24T23:29:30Z | 2013-07-24T23:26:39Z | CONTRIBUTOR | null | Currently, master creates a 0.12.0 egg for me without the usual '.dev_abcdefg' addition?
So it looks like it's going backwards (see below timestamps):
()[maye@luna6 .../python2.7/site-packages]$ ll pandas*
pandas-0.12.0.dev_4c2d050-py2.7-linux-x86_64.egg:
total 8.0K
drwxr-xr-x 2 maye 4.0K Jul 16 11:45 EGG-INFO
drwxr-xr-x 13 maye 4.0K Jul 16 11:45 pandas
pandas-0.12.0-py2.7-linux-x86_64.egg:
total 8.0K
drwxr-xr-x 2 maye 4.0K Jul 24 15:49 EGG-INFO
drwxr-xr-x 13 maye 4.0K Jul 24 15:49 pandas
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} | 12 | 2013-07-25T03:24:38Z | 2016-10-12T23:04:02Z | 2013-07-26T23:56:37Z | CONTRIBUTOR | null | just got those
```
======================================================================
ERROR: test_banklist_header (pandas.io.tests.test_html.TestReadHtmlBase)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.12.0/debian/tmp/usr/lib/python2.6/dist-packages/pandas/io/tests/test_html.py", line 387, in test_banklist_header
self.assertTupleEqual(df.shape, ground_truth.shape)
AttributeError: 'TestReadHtmlBase' object has no attribute 'assertTupleEqual'
======================================================================
ERROR: test_banklist_no_match (pandas.io.tests.test_html.TestReadHtmlBase)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.12.0/debian/tmp/usr/lib/python2.6/dist-packages/pandas/io/tests/test_html.py", line 164, in test_banklist_no_match
self.assertIsInstance(df, DataFrame)
AttributeError: 'TestReadHtmlBase' object has no attribute 'assertIsInstance'
======================================================================
ERROR: test_gold_canyon (pandas.io.tests.test_html.TestReadHtmlBase)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.12.0/debian/tmp/usr/lib/python2.6/dist-packages/pandas/io/tests/test_html.py", line 418, in test_gold_canyon
self.assertIn(gc, df.to_string())
AttributeError: 'TestReadHtmlBase' object has no attribute 'assertIn'
```
there are more points where used... if python2.6 is no longer supported that is ok too ;)
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} | 0 | 2013-07-25T03:35:07Z | 2014-06-23T07:28:09Z | 2013-07-26T23:56:38Z | MEMBER | null | closes #4351.
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} | 9 | 2013-07-25T11:02:53Z | 2013-07-25T16:44:04Z | 2013-07-25T16:44:04Z | NONE | null | Trying to run `nosetests3 pandas` on an installed pandas package, I get:
```
ERROR: test_read_dta10 (pandas.io.tests.test_stata.StataTests)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python3/dist-packages/pandas/io/tests/test_stata.py", line 207, in test_read_dta10
original.to_stata(path, {'datetime': 'tc'}, False)
File "/usr/lib/python3/dist-packages/pandas/core/frame.py", line 1503, in to_stata
writer = StataWriter(fname,self,convert_dates=convert_dates, encoding=encoding, byteorder=byteorder)
File "/usr/lib/python3/dist-packages/pandas/io/stata.py", line 745, in __init__
self._file = _open_file_binary_write(fname, self._encoding)
File "/usr/lib/python3/dist-packages/pandas/io/stata.py", line 577, in _open_file_binary_write
return open(fname, "wb")
PermissionError: [Errno 13] Permission denied: '/usr/lib/python3/dist-packages/pandas/io/tests/data/stata10.dta'
```
... and other similar errors. Maybe it'll be a good idea to fix the tests so that they can be run on an installed package without needing to modify the actually installed files.
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} | 8 | 2013-07-25T11:28:38Z | 2013-07-25T19:09:12Z | 2013-07-25T18:49:37Z | CONTRIBUTOR | null | nice interface, but need to update with docs in io.rst
see #2592
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} | 5 | 2013-07-25T12:59:39Z | 2014-06-19T16:27:30Z | 2013-07-25T16:44:04Z | MEMBER | null | closes #4353
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} | 23 | 2013-07-25T13:26:16Z | 2016-10-12T23:04:02Z | 2013-10-09T18:48:10Z | NONE | null | ``` python
pan = web.get_data_google(['GE', 'MSFT', 'INTC'], 'JAN-01-12', 'JAN-31-12')
result = pan.Open.ix['Jan-15-12':'Jan-20-12']
```
has shape (5, 3), not (4, 3) as claimed in the test:
```
GE INTC MSFT
Date
2012-01-16 17.65 NaN NaN
2012-01-17 17.85 25.18 28.40
2012-01-18 17.55 25.13 28.31
2012-01-19 17.60 25.52 28.16
2012-01-20 17.60 25.87 28.82
```
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} | 15 | 2013-07-25T14:19:36Z | 2014-06-23T03:12:15Z | 2013-08-12T13:21:40Z | CONTRIBUTOR | null | closes #3334 Brittle pivot margins
Issue was that pivot tables that use all columns of the original DataFrame in rows and cols failed on the margin computation. This is a special case for margins: one should use the index itself as the value.
```
>> df = DataFrame({'Response' : ['Y', 'N' ,'N', 'Y', 'Y', 'N'],
'Type' : ['A', 'A', 'B', 'B', 'B', 'C']})
>> pivot_table(df, rows='Response',cols='Type',aggfunc=len,margins=True)
>> Type A B C All
>> Response
>> N 1 1 1 3
>> Y 1 2 NaN 3
>> All 2 3 1 6
```
This is my first PR to Pandas, so apologies for anything out of the ordinary.
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} | 26 | 2013-07-25T15:03:57Z | 2013-07-29T13:00:30Z | 2013-07-28T15:42:43Z | CONTRIBUTOR | null | should raise when `orient='columns'` and index is non_unique
`orient='index'` and column is non_unique?
I'm trying out to_json and read_json on a data frame with 800k rows. However, after calling to_json on the file, read_json gets back only 2k rows. This happens if I call them in series or if I give to_json a filename and call the filename with read_json. Judging by the size of the file, all the data is being written (the json is roughly the size of the pickled data frame object). Any idea what's going on?
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} | 24 | 2013-07-25T15:10:46Z | 2014-06-26T03:36:51Z | 2013-07-30T20:28:58Z | CONTRIBUTOR | null | closes #4345
The `test_time_series_plot_color_with_empty_kwargs` method in `test_graphics.py` uses the matplotlib default color cycle rather than 'r', 'g', and 'b'.
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} | 4 | 2013-07-25T15:12:45Z | 2013-07-26T10:40:04Z | 2013-07-26T10:40:04Z | NONE | null | If possible, it would be really useful to allow for an extra parameter that can override the default behaviour of the `pandas.Series.value_counts()` function to allow for sorting by by another series. we are looking to push the result out to a bar plot, and it makes sense to have the category (x-axis) displayed in a particular order.
Thanks!
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} | 6 | 2013-07-25T15:27:16Z | 2013-08-15T01:14:39Z | 2013-08-15T01:14:12Z | NONE | null | According to the documentation for pandas 0.12
> date_format : type of date conversion (epoch = epoch milliseconds, iso = ISO8601)
> default is epoch
The json exporting function seems to be returning the epoch _nanoseconds_ rather than the _milliseconds_ as suggested by the documentation.
``` python
s = pd.Series(1, index=pd.date_range('2013-07-25', periods=2, ))
s.index.astype('int64')
>> array([1374710400000000000, 1374796800000000000])
s.to_json(orient='split', date_format='epoch')
>> '{"name":null,"index":[1374710400000000000,1374796800000000000],"data":[1,1]}'
```
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} | 5 | 2013-07-25T16:06:06Z | 2016-04-26T17:23:52Z | 2016-04-26T17:22:34Z | NONE | null | This gist demonstrates the problem:
https://gist.github.com/brentp/6066942
It's discussed in this thread:
https://groups.google.com/forum/#!topic/pydata/hIIZpZqbY5M
As discussed in the thread, there is some interaction when specifying dtype, index_col, and sep in read_csv.
As the gist shows, things work find with sep="\s+", but fail for the same set with sep="\t" even though the file is tab-delimited.
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} | 12 | 2013-07-25T16:57:51Z | 2013-07-26T14:17:24Z | 2013-07-25T18:48:29Z | CONTRIBUTOR | null | - DOC: Simple example of how to use Pandas' World Bank IO functions.
- ENH: Return a DataFrame full of floats instead of full of strings
RE: https://github.com/pydata/pandas/issues/4354
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} | 4 | 2013-07-25T16:59:18Z | 2013-09-09T16:18:28Z | 2013-07-26T13:59:23Z | MEMBER | null | ```
In [29]: df = DataFrame(randint(2, size=(8e6, 10)))
In [30]: time df.sum()
CPU times: user 1.21 s, sys: 0.03 s, total: 1.24 s
Wall time: 1.23 s
Out[30]:
0 3999325
1 3998264
2 4000047
3 3997902
4 4001078
5 4001965
6 4001701
7 4000482
8 3997581
9 4000691
dtype: int64
In [31]: time df.mean()
CPU times: user 0.38 s, sys: 0.00 s, total: 0.38 s
Wall time: 0.39 s
Out[31]:
0 0.5
1 0.5
2 0.5
3 0.5
4 0.5
5 0.5
6 0.5
7 0.5
8 0.5
9 0.5
dtype: float64
```
Should sum be 3.15x slower than mean?
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- change the `_quiet` field (default is `False`) to a warning (about duplicates in readback)
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cc @cpcloud
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https://api.github.com/repos/pandas-dev/pandas/issues/4370 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4370/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4370/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4370/events | https://github.com/pandas-dev/pandas/issues/4370 | 17,265,675 | MDU6SXNzdWUxNzI2NTY3NQ== | 4,370 | ER/DOC: Sorting in multi-index columns: misleading error message, unclear docs | {
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} | 4 | 2013-07-26T14:23:06Z | 2016-02-08T23:18:29Z | 2014-03-15T14:34:37Z | CONTRIBUTOR | null | related #739
Have a look at this example:
``` python
import pandas as pd
import numpy as np
from StringIO import StringIO
print "Pandas version %s\n\n" % pd.__version__
data1 = """idx,metric
0,2.1
1,2.5
2,3"""
data2 = """idx,metric
0,2.7
1,2.2
2,2.8"""
df1 = pd.read_csv(StringIO(data1))
df2 = pd.read_csv(StringIO(data2))
concatenated = pd.concat([df1, df2], ignore_index=True)
merged = concatenated.groupby("idx").agg([np.mean, np.std])
print merged
print merged.sort('metric')
```
and its output:
```
$ python test.py
Pandas version 0.11.0
metric
mean std
idx
0 2.40 0.424264
1 2.35 0.212132
2 2.90 0.141421
Traceback (most recent call last):
File "test.py", line 22, in <module>
print merged.sort('metric')
File "/***/Python-2.7.3/lib/python2.7/site-packages/pandas/core/frame.py", line 3098, in sort
inplace=inplace)
File "/***/Python-2.7.3/lib/python2.7/site-packages/pandas/core/frame.py", line 3153, in sort_index
% str(by))
ValueError: Cannot sort by duplicate column metric
```
The problem here is not that there is a duplicate column `metric` as stated by the error message. The problem is that there are still two sub-levels. The solution in this case is to use
``` python
merged.sort([('metric', 'mean')])
```
for sorting by the mean of the metric. It took myself quite a while to figure this out. First of all, the error message should be more clear in this case. Then, maybe I was too stupid, but I could not find the solution in the docs, but within a thread on StackOverflow. Looks like the error message above is the result of an over-generalized condition around https://github.com/pydata/pandas/blob/v0.12.0rc1/pandas/core/frame.py#L3269
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} | [] | closed | false | null | [] | null | 5 | 2013-07-26T15:12:35Z | 2013-07-28T04:56:00Z | 2013-07-27T17:58:25Z | NONE | null | My python code looks like
```
df = pandas.read_csv("a.csv")
print df
table = pandas.pivot_table(df, values=['Sales1'] ,rows='State' , aggfunc=np.sum)
print type(table)
table = pandas.pivot_table(df, values='Sales1' ,rows='State' , aggfunc=np.sum)
print type(table)
Contents of a.csv
City,Sales1
Mumbai,1
The Output
City Sales1
0 Mumbai 1
<class 'pandas.core.frame.DataFrame'>
<class 'pandas.core.series.Series'>
```
My question is why would a change in the datatype of the values parameter return a DataFrame instead of a Series. After all we use an array only if we have to pass multiple values.
Thank You.
Vijay Mukhi
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} | 5 | 2013-07-26T18:47:13Z | 2013-07-31T22:12:20Z | 2013-07-31T22:12:20Z | CONTRIBUTOR | null | as @dieterv77 points out, fix for this in #4220 of issue #4219
does not build on windows
cc @jtratner
can change to using a HashTable (I think we discussed this anyhow)
?
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} | 2 | 2013-07-26T19:03:05Z | 2014-06-16T23:53:36Z | 2013-07-30T00:07:39Z | CONTRIBUTOR | null | closes #4318
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} | 11 | 2013-07-26T23:53:52Z | 2013-08-15T13:34:34Z | 2013-07-29T23:49:59Z | MEMBER | null | - [x] deprecate `iterkv`
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} | 2 | 2013-07-27T02:47:08Z | 2021-06-01T10:18:20Z | 2013-07-28T15:41:54Z | CONTRIBUTOR | null | Issue #4359
to_json will raise ValueErrors where non-unique indices or columns would cause invalid JSON to be produced. Updated docstring and added some tests.
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} | 4 | 2013-07-27T02:56:53Z | 2013-08-05T02:06:36Z | 2013-07-30T00:17:42Z | CONTRIBUTOR | null | Came across this when creating #4376. Not sure what's going on as I was under the impression that DataFrame always has a `dtype` attribute.
The error only seems to occur when the columns are not unique:
``` python
In [5]: import pandas as pd
In [6]: df = pd.DataFrame([['a','b'],['c','d']], index=[1,2], columns=['x','y'])
In [7]: df
Out[7]:
x y
1 a b
2 c d
In [8]: pd.read_json(df.to_json(orient='split'), orient='split')
Out[8]:
x y
1 a b
2 c d
In [9]: df = pd.DataFrame([['a','b'],['c','d']], index=[1,2], columns=['x','x'])
In [10]: df
Out[10]:
x x
1 a b
2 c d
In [11]: pd.read_json(df.to_json(orient='split'), orient='split')
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-11-aaa9a429e682> in <module>()
----> 1 pd.read_json(df.to_json(orient='split'), orient='split')
/home/kieran/work/git/pandas_kom/pandas/io/json.pyc in read_json(path_or_buf, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, numpy, precise_float)
196 obj = None
197 if typ == 'frame':
--> 198 obj = FrameParser(json, orient, dtype, convert_axes, convert_dates, keep_default_dates, numpy).parse()
199
200 if typ == 'series' or obj is None:
/home/kieran/work/git/pandas_kom/pandas/io/json.pyc in parse(self)
241 if self.convert_axes:
242 self._convert_axes()
--> 243 self._try_convert_types()
244 return self.obj
245
/home/kieran/work/git/pandas_kom/pandas/io/json.pyc in _try_convert_types(self)
453 self._try_convert_dates()
454 for col in self.obj.columns:
--> 455 new_data, result = self._try_convert_data(col, self.obj[col], convert_dates=False)
456 if result:
457 self.obj[col] = new_data
/home/kieran/work/git/pandas_kom/pandas/io/json.pyc in _try_convert_data(self, name, data, use_dtypes, convert_dates)
282 result = False
283
--> 284 if data.dtype == 'object':
285
286 # try float
/home/kieran/work/git/pandas_kom/pandas/core/frame.pyc in __getattr__(self, name)
2086 return self[name]
2087 raise AttributeError("'%s' object has no attribute '%s'" %
-> 2088 (type(self).__name__, name))
2089
2090 def __setattr__(self, name, value):
AttributeError: 'DataFrame' object has no attribute 'dtype'
```
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} | 6 | 2013-07-27T13:06:05Z | 2013-09-12T16:15:31Z | 2013-09-12T16:15:05Z | CONTRIBUTOR | null | Though read_csv can't read these in yet....
see this: https://github.com/playpauseandstop/kikola/blob/master/kikola/utils/timedelta.py
```
In [58]: data
Out[58]: ' completed deadline\n15-07-2013 23:10 15-07-2013 23:15\n16-07-2013 00:20 16-07-2013 00:15\n16-07-2013 00:20 16-07-2013 00:15\n16-07-2013 21:04 16-07-2013 21:30\n16-07-2013 21:58 16-07-2013 22:00\n16-07-2013 23:21 16-07-2013 23:15\n16-07-2013 23:21 16-07-2013 23:15\n17-07-2013 00:19 17-07-2013 00:15\n17-07-2013 00:19 17-07-2013 00:15\n17-07-2013 21:18 17-07-2013 21:30\n17-07-2013 22:07 17-07-2013 22:00'
In [59]: df = read_csv(StringIO(data),index_col=None,skiprows=1,header=None,parse_dates=[[0,1],[2,3]],sep='\s+')
In [60]: df.columns = ['completed','deadline']
In [61]: df['delta'] = df['completed']-df['deadline']
In [62]: df
Out[62]:
completed deadline delta
0 2013-07-15 23:10:00 2013-07-15 23:15:00 -00:05:00
1 2013-07-16 00:20:00 2013-07-16 00:15:00 00:05:00
2 2013-07-16 00:20:00 2013-07-16 00:15:00 00:05:00
3 2013-07-16 21:04:00 2013-07-16 21:30:00 -00:26:00
4 2013-07-16 21:58:00 2013-07-16 22:00:00 -00:02:00
5 2013-07-16 23:21:00 2013-07-16 23:15:00 00:06:00
6 2013-07-16 23:21:00 2013-07-16 23:15:00 00:06:00
7 2013-07-17 00:19:00 2013-07-17 00:15:00 00:04:00
8 2013-07-17 00:19:00 2013-07-17 00:15:00 00:04:00
9 2013-07-17 21:18:00 2013-07-17 21:30:00 -00:12:00
10 2013-07-17 22:07:00 2013-07-17 22:00:00 00:07:00
In [63]: df.dtypes
Out[63]:
completed datetime64[ns]
deadline datetime64[ns]
delta timedelta64[ns]
dtype: object
In [64]: df.to_csv('test.csv')
In [65]: !cat test.csv
,completed,deadline,delta
0,2013-07-15 23:10:00,2013-07-15 23:15:00,-300000000000
1,2013-07-16 00:20:00,2013-07-16 00:15:00,300000000000
2,2013-07-16 00:20:00,2013-07-16 00:15:00,300000000000
3,2013-07-16 21:04:00,2013-07-16 21:30:00,-1560000000000
4,2013-07-16 21:58:00,2013-07-16 22:00:00,-120000000000
5,2013-07-16 23:21:00,2013-07-16 23:15:00,360000000000
6,2013-07-16 23:21:00,2013-07-16 23:15:00,360000000000
7,2013-07-17 00:19:00,2013-07-17 00:15:00,240000000000
8,2013-07-17 00:19:00,2013-07-17 00:15:00,240000000000
9,2013-07-17 21:18:00,2013-07-17 21:30:00,-720000000000
10,2013-07-17 22:07:00,2013-07-17 22:00:00,420000000000
```
work-around (but should be handled in the datetimeblock though)
```
In [66]: df['delta'] = df['delta'].apply(pd.lib.repr_timedelta64)
In [67]: df.to_csv('test.csv')
In [68]: !cat test.csv
,completed,deadline,delta
0,2013-07-15 23:10:00,2013-07-15 23:15:00,-00:05:00
1,2013-07-16 00:20:00,2013-07-16 00:15:00,00:05:00
2,2013-07-16 00:20:00,2013-07-16 00:15:00,00:05:00
3,2013-07-16 21:04:00,2013-07-16 21:30:00,-00:26:00
4,2013-07-16 21:58:00,2013-07-16 22:00:00,-00:02:00
5,2013-07-16 23:21:00,2013-07-16 23:15:00,00:06:00
6,2013-07-16 23:21:00,2013-07-16 23:15:00,00:06:00
7,2013-07-17 00:19:00,2013-07-17 00:15:00,00:04:00
8,2013-07-17 00:19:00,2013-07-17 00:15:00,00:04:00
9,2013-07-17 21:18:00,2013-07-17 21:30:00,-00:12:00
10,2013-07-17 22:07:00,2013-07-17 22:00:00,00:07:00
```
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} | 4 | 2013-07-27T18:05:20Z | 2014-06-24T19:44:31Z | 2013-07-30T06:53:35Z | MEMBER | null | This PR fixes an issue where joining a PeriodIndex with itself resulted in a
new object.
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https://api.github.com/repos/pandas-dev/pandas/issues/4380 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4380/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4380/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4380/events | https://github.com/pandas-dev/pandas/pull/4380 | 17,302,881 | MDExOlB1bGxSZXF1ZXN0NzIxNTc2Mw== | 4,380 | Fix typo. | {
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} | 1 | 2013-07-27T20:13:21Z | 2014-07-16T08:20:41Z | 2013-07-27T20:42:02Z | CONTRIBUTOR | null | Change 'highlites' to 'highlights'.
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} | 3 | 2013-07-27T20:27:09Z | 2013-09-24T20:37:17Z | 2013-09-24T20:37:17Z | CONTRIBUTOR | null | For example:
```
pd.io.parsers.read_csv("http://www.datazoa.com/publish/export.asp?hash=yjPceG6fHL&uid=dzadmin&a=exportcsv",skiprows=range(1,13+1),skipfooter=4,parse_dates={"date":[0]})
```
has 907 rows.
As does:
```
pd.io.parsers.read_csv("http://www.datazoa.com/publish/export.asp?hash=yjPceG6fHL&uid=dzadmin&a=exportcsv",skipfooter=4,parse_dates={"date":[0]})
```
whereas:
```
pd.io.parsers.read_csv("http://www.datazoa.com/publish/export.asp?hash=yjPceG6fHL&uid=dzadmin&a=exportcsv",skiprows=range(1,13+1),skipfooter=4,parse_dates=[0])
```
has 894 rows.
I am on Pandas v0.11.0
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} | 7 | 2013-07-27T20:28:02Z | 2013-07-27T23:38:50Z | 2013-07-27T23:38:50Z | CONTRIBUTOR | null | This is `_is_url`, it has a blanket `except` statement, I'm thinking it would be better to specify the exceptions here (caused an issue for me while editing earlier). Not sure which exceptions should go here (ValueError, TypeError?), anyone have an idea?
``` python
def _is_url(url):
try:
return urlparse.urlparse(url).scheme in _VALID_URLS
except:
return False
```
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} | 175 | 2013-07-27T20:58:22Z | 2014-06-19T03:06:39Z | 2013-07-29T23:49:59Z | CONTRIBUTOR | null | Fixes #4375 and #4372.
Many changes to make codebase compatible in 2 and 3. For `range`, `zip`, `map`, etc. tried to favor iterators over needing to use lists.
Changes:
- No more 2to3 in setup.py
- merges util/compat and util/py3compat into pandas/compat
- incorporates useful parts of the six library into compat (+ adds SIX to LICENSES)
- defaults to using iterators in both Python 2 and Python 3 for range, zip, map, and filter
- Adds lrange, lzip, lmap, and lfilter, which wrap corresponding iterator methods with lists
- Improved type checks (where appropriate --> many need to be rigid to work with pandas)
- various other utilities to be Py2/3 compatible (iteritems, iterkeys, itervalues, etc.)
- deprecates iterkv with a warning (no longer necessary because library no longer uses 2to3)
- compatibility wrapper around `dateutil` for handling unicode when version <= 2.0 + 1.5 dateutil build in Travis
Switches everything to use special iteritems, so can deprecate as discussed in #4372.
This iteritems use "iteritems" method if available and otherwise uses items.
Checklist of packages to run through 2to3 to check:
- [x] compat
- [x] core
- [x] io
- [x] rpy
- [x] sandbox
- [x] sparse
- [x] src (python files only)
- [x] stats
- [x] tests
- [x] tools
- [x] tseries
- [x] util
Other tasks:
- [x] alias `__bool__` to `__nonzero__`
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} | [] | closed | false | null | [] | null | 3 | 2013-07-28T00:05:41Z | 2013-07-28T00:20:17Z | 2013-07-28T00:20:17Z | CONTRIBUTOR | null | This codepath (I think) does not get tested in the test suite. (if you add a `raise AssertionError("I SHOULD FAIL")`, after `if _is_s3_url(filepath_or_buffer):`, it does not fail).
``` python
if _is_s3_url(filepath_or_buffer):
try:
import boto
except:
raise ImportError("boto is required to handle s3 files")
# Assuming AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY
# are environment variables
parsed_url = urlparse.urlparse(filepath_or_buffer)
conn = boto.connect_s3()
b = conn.get_bucket(parsed_url.netloc)
k = boto.s3.key.Key(b)
k.key = parsed_url.path
filepath_or_buffer = StringIO(k.get_contents_as_string())
return filepath_or_buffer, None
```
This is _is_s3_url, which gets tested, but only with non s3 urls.
``` python
def _is_s3_url(url):
"""Check for an s3 url"""
try:
return urlparse.urlparse(url).scheme == 's3'
except:
return False
```
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} | 6 | 2013-07-28T04:56:00Z | 2017-04-18T22:37:17Z | 2017-04-18T22:37:17Z | CONTRIBUTOR | null | The docstrings and other documentation say that the `pivot_table` function **returns a DataFrame**. However, this likely leads to confusion like #4371, because under narrow circumstances, passing a certain set of argument dtypes results in the function **returning a Series** (see ipython examples at end):
1. values is single string (not a list, not even a single valued list)
2. cols=None
3. aggfunc is single string/function (not a list, not even a single valued list)
Unfortunately, this is not clear from the docs or from normal use (except for condition 1).
Should this:
1. eventually be fixed to only return a DataFrame no matter the circumstances to be less confusing
2. be documented correctly (seems a little difficult to convey in the docstring and other docs without a lot of bulk).
My thoughts are changing the function to return only a DataFrame in future versions (> 0.13) and providing some deprecation warning in the meantime is better than trying to explain this in the docs.
I would be happy to provide the deprecation warning and document notes as a pull request.
Thanks.
``` python
Python 2.7.2 (v2.7.2:8527427914a2, Jun 11 2011, 15:22:34)
Type "copyright", "credits" or "license" for more information.
IPython 1.0.dev -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
%guiref -> A brief reference about the graphical user interface.
In [1]: import pandas as pd
...: import numpy as np
...: pd.__version__
...:
Out[1]: '0.12.0-57-g7bf2a7d'
In [2]: df = pd.DataFrame({'col1': [3, 4, 5], 'col2': ['C', 'D', 'E'], 'col3': [1, 3, 9]})
In [3]: df
Out[3]:
col1 col2 col3
0 3 C 1
1 4 D 3
2 5 E 9
In [4]: # Case 1: (a) values is single string label (b) cols is unspecified
...: # (c) aggfunc is single lable/function (not a list)
...: # Expect: Series type
...: pivoted_1 = df.pivot_table('col1', rows=['col3', 'col2'], aggfunc=np.sum)
...: print pivoted_1
...: print type(pivoted_1)
col3 col2
1 C 3
3 D 4
9 E 5
Name: col1, dtype: int64
<class 'pandas.core.series.Series'>
In [5]: # Case 2: (a) values is single string label (b) cols is single string label
...: # Expected: DataFrame
...: pivoted_2 = df.pivot_table('col1', rows='col3', cols='col2', aggfunc=np.sum)
...: print pivoted_2
...: print type(pivoted_2)
col2 C D E
col3
1 3 NaN NaN
3 NaN 4 NaN
9 NaN NaN 5
<class 'pandas.core.frame.DataFrame'>
In [6]: # Case 3: (a) values is single string label (b) cols is unspecified
...: # (c) aggfunc is a list
...: # Expect: DataFrame
...: pivoted_3 = df.pivot_table('col1', rows='col3', aggfunc=[np.sum])
...: print pivoted_3
...: print type(pivoted_3)
sum
col3
1 3
3 4
9 5
<class 'pandas.core.frame.DataFrame'>
```
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} | 2 | 2013-07-28T05:43:36Z | 2021-05-10T17:42:17Z | null | NONE | null | Following issue #3598,
```
df = pd.DataFrame(np.random.uniform(size=10).reshape(5,2),columns=['A','B'])
df['A'] = df['A'] * 100
df.A.plot()
df.B.plot(kind='bar',secondary_y=True)
plt.show()
```
This gives the right plot, with `A` plotted as line in the primary axis, and `B` is plotted as bar in the secondary axis.
But if I index the dataframe by datetime
```
df = pd.DataFrame(np.random.uniform(size=10).reshape(5,2),columns=['A','B'])
df = df.set_index(pd.date_range('20130101',periods=5))
df['A'] = df['A'] * 100
df.A.plot()
df.B.plot(kind='bar',secondary_y=True)
plt.show()
```
This wont plot A as line in the primary axis, only the B as bar in the secondary axis. Not sure why.
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} | 0 | 2013-07-28T16:55:41Z | 2014-07-16T08:20:50Z | 2013-07-30T00:17:43Z | CONTRIBUTOR | null | closes #4377
- Fixed bug with `.values` raising an error on a DataFrame with duplicate columns and
mixed dtypes, surfaced in GH4377
- Fixed bug with duplicate columns and type conversion in `read_json` when
`orient='split'`
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} | 6 | 2013-07-28T17:06:40Z | 2013-09-21T12:00:22Z | 2013-09-20T23:09:13Z | CONTRIBUTOR | null | I think this is pretty duplicative and unless its a _major_ bug fix should be avoided (in whatsnew), though I am +1 for API/enhancements...etc
need a 1-time cleanout of v0.13.0 txt...then (let's leave prior version alone)
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} | 2 | 2013-07-28T20:00:35Z | 2013-07-30T00:27:21Z | 2013-07-30T00:27:21Z | MEMBER | null | ```
In [9]: s = Series(randn(10), index=period_range('1/1/2001', periods=10))
In [10]: s
Out[10]:
2001-01-01 -0.677
2001-01-02 0.058
2001-01-03 -1.200
2001-01-04 -1.152
2001-01-05 -2.646
2001-01-06 -1.068
2001-01-07 -1.232
2001-01-08 0.452
2001-01-09 0.476
2001-01-10 -1.387
Freq: D, dtype: float64
In [11]: s.iat[0] # throws a KeyError
```
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} | 1 | 2013-07-28T21:01:55Z | 2014-06-19T05:25:32Z | 2013-07-28T21:02:07Z | NONE | null | related https://github.com/pydata/pandas/issues/4026?source=cc
The `display.height` option is dperecated. It's value is interogated by
`get_conosole_size()`, whose only caller in the codebase summarily ignores
that bit of the `(width,height)` retval.
Another ugly ricochet from the `repr()` cluster***\* of 0.11. a tar pit if ever I saw one.
rather then reprecate (?) the depecrated option (As in #4027), just silence the deprecation
warning at the root and quickly look away.
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} | 3 | 2013-07-28T21:03:04Z | 2014-06-21T18:41:15Z | 2013-07-29T01:13:07Z | MEMBER | null | Let users pass nice git names so that they don't have to manually pass sha1
hashes to test between commits.
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} | 2 | 2013-07-28T23:42:30Z | 2014-06-26T01:31:52Z | 2013-07-30T00:27:21Z | CONTRIBUTOR | null | closes #4390
Interesting fixed this as well (only affects `get`)
```
In [1]: s = Series(randn(10),date_range('20130101',periods=10))
In [2]: s[0]
Out[2]: 1.1755210838393764
In [3]: s
Out[3]:
2013-01-01 1.175521
2013-01-02 0.007910
2013-01-03 -1.235595
2013-01-04 0.478245
2013-01-05 -0.140616
2013-01-06 -2.317585
2013-01-07 0.609555
2013-01-08 1.204914
2013-01-09 -0.589893
2013-01-10 -2.017237
Freq: D, dtype: float64
In [4]: s[-1]
Out[4]: -2.0172365222181257
In [5]: s[-5]
Out[5]: -2.3175847263447964
```
This was before this PR returning `None` (while `s[-5]` worked)
```
In [6]: s.get(-5)
```
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} | 0 | 2013-07-29T00:49:05Z | 2014-06-13T13:40:41Z | 2013-08-09T03:45:37Z | CONTRIBUTOR | null | The docstring for DataReader is not showing the "google" option, even though it is already in the source code
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} | 10 | 2013-07-29T00:50:42Z | 2014-01-27T09:52:34Z | 2013-09-29T17:07:57Z | CONTRIBUTOR | null | See at http://nipy.bic.berkeley.edu/builders/pandas-py2.x-sid-sparc/builds/18/steps/shell_4/logs/stdio
and that assert better has some descriptive failure message (or to be replaced with assertEqual/eq_)?
```
======================================================================
FAIL: test_round_trip_frame (pandas.io.tests.test_clipboard.TestClipboard)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py2_x-sid-sparc/build/venv/local/lib/python2.7/site-packages/pandas-0.12.0_56_g0d80c79-py2.7-linux-sparc64.egg/pandas/io/tests/test_clipboard.py", line 50, in test_round_trip_frame
self.check_round_trip_frame(dt)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py2_x-sid-sparc/build/venv/local/lib/python2.7/site-packages/pandas-0.12.0_56_g0d80c79-py2.7-linux-sparc64.egg/pandas/io/tests/test_clipboard.py", line 46, in check_round_trip_frame
tm.assert_frame_equal(data, result)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py2_x-sid-sparc/build/venv/local/lib/python2.7/site-packages/pandas-0.12.0_56_g0d80c79-py2.7-linux-sparc64.egg/pandas/util/testing.py", line 236, in assert_frame_equal
check_less_precise=check_less_precise)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py2_x-sid-sparc/build/venv/local/lib/python2.7/site-packages/pandas-0.12.0_56_g0d80c79-py2.7-linux-sparc64.egg/pandas/util/testing.py", line 197, in assert_series_equal
assert(left.dtype == right.dtype)
AssertionError
======================================================================
FAIL: test_unsorted_index (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py2_x-sid-sparc/build/venv/local/lib/python2.7/site-packages/pandas-0.12.0_56_g0d80c79-py2.7-linux-sparc64.egg/pandas/tests/test_graphics.py", line 445, in test_unsorted_index
tm.assert_series_equal(rs, df.y)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py2_x-sid-sparc/build/venv/local/lib/python2.7/site-packages/pandas-0.12.0_56_g0d80c79-py2.7-linux-sparc64.egg/pandas/util/testing.py", line 197, in assert_series_equal
assert(left.dtype == right.dtype)
AssertionError
----------------------------------------------------------------------
Ran 3695 tests in 3708.536s
```
As you might know you can see results of builds/tests on sparc at
http://nipy.bic.berkeley.edu/waterfall?category=pandas . ATM it is polling GIT only once in 5h atm.
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} | 4 | 2013-07-29T00:58:22Z | 2013-08-02T13:01:40Z | 2013-08-02T13:01:40Z | CONTRIBUTOR | null | e.g.
`assert (left.dtype == right.dtype),
"Left Dtype: {0}, Right Dtype: {1}".format(left.dtype,right.dtype)`
in `assert_*_equal`
will be useful in seeing what is actually failing as the 2nd parameter
gets raised in the `AssertionError(2nd parm)`
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https://api.github.com/repos/pandas-dev/pandas/issues/4398 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4398/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4398/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4398/events | https://github.com/pandas-dev/pandas/issues/4398 | 17,321,825 | MDU6SXNzdWUxNzMyMTgyNQ== | 4,398 | Rewrite pandas.util.testing.assert_almost_equal for performance | {
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} | 3 | 2013-07-29T03:09:20Z | 2013-10-21T21:35:44Z | 2013-10-21T21:35:44Z | MEMBER | null | I don't know if anyone's profiled the test suite, but we'd get major performance boosts if we wrote a Cython version of assert_almost_equal. Is there another open issue for this already?
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} | 6 | 2013-07-29T16:07:06Z | 2016-04-11T12:47:31Z | 2016-04-11T12:47:31Z | NONE | null | related is #6076 (`loc` not working either)
It would be nice if common operations like indexing/slicing is available for sparse dataframes as for their dense counterparts.
Thanks!
``` python
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: A = np.zeros((3,4))
In [4]: A[:] = np.nan
In [5]: A[2,3] = 1
In [6]:
In [6]: Y = pd.DataFrame(A)
In [7]: print Y.iloc[2,3] # print 1.0
1.0
In [8]: sY = Y.to_sparse()
In [9]: print sY.iloc[2,3]
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-9-0674f68ef869> in <module>()
----> 1 print sY.iloc[2,3]
/usr/local/Cellar/python/2.7.5/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/indexing.pyc in __getitem__(self, key)
667 def __getitem__(self, key):
668 if type(key) is tuple:
--> 669 return self._getitem_tuple(key)
670 else:
671 return self._getitem_axis(key, axis=0)
/usr/local/Cellar/python/2.7.5/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/indexing.pyc in _getitem_tuple(self, tup)
774 continue
775
--> 776 retval = getattr(retval,self.name)._getitem_axis(key, axis=i)
777
778 return retval
/usr/local/Cellar/python/2.7.5/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/indexing.pyc in _getitem_axis(self, key, axis)
803 raise ValueError("Cannot index by location index with a non-integer key")
804
--> 805 return self._get_loc(key,axis=axis)
806
807 def _convert_to_indexer(self, obj, axis=0):
/usr/local/Cellar/python/2.7.5/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/indexing.pyc in _get_loc(self, key, axis)
61
62 def _get_loc(self, key, axis=0):
---> 63 return self.obj._ixs(key, axis=axis)
64
65 def _slice(self, obj, axis=0, raise_on_error=False):
/usr/local/Cellar/python/2.7.5/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/frame.pyc in _ixs(self, i, axis, copy)
1880 new_values = self._data.fast_2d_xs(i, copy=copy)
1881 except:
-> 1882 new_values = self._data.fast_2d_xs(i, copy=True)
1883 return Series(new_values, index=self.columns,
1884 name=self.index[i])
AttributeError: '_SparseMockBlockManager' object has no attribute 'fast_2d_xs'
```
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