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| null | 3 | 2014-09-27T02:41:25Z | 2014-09-28T03:56:16Z | 2014-09-27T02:46:24Z | CONTRIBUTOR | null | Adds support for method-based fillna along the specified axis of a Panel. Also adds some additional validation when calling fillna on DataFrame.
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} | 3 | 2014-09-27T13:10:31Z | 2018-11-28T11:30:52Z | 2018-11-28T11:30:47Z | CONTRIBUTOR | null | Follow up to https://github.com/pydata/pandas/pull/8398
This can be done now.
Remove https://github.com/pandas-dev/pandas/blob/30eb48cc485bb7c7c47a4e7e26f87e469fbd8b8a/pandas/tests/plotting/test_boxplot_method.py#L24-L31
and its use in
https://github.com/pandas-dev/pandas/blob/30eb48cc485bb7c7c47a4e7e26f87e469fbd8b8a/pandas/tests/plotting/test_boxplot_method.py#L158
Keep an eye on https://github.com/matplotlib/matplotlib/pull/3571 and https://github.com/matplotlib/matplotlib/issues/3544
- [x] Number of fliers (https://github.com/pydata/pandas/pull/8429)
- [ ] Empty col / all NaN
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| null | 0 | 2014-09-27T15:41:12Z | 2014-09-29T00:27:22Z | 2014-09-29T00:27:22Z | NONE | null | Hi,
when using the resample method, additional memory is allocated which seems to remain after the call. Consider the following example:
``` python
import gc
import pandas as pd
import numpy as np
import objgraph
# taken from http://pandas.pydata.org/pandas-docs/stable/10min.html?highlight=resample
rng = pd.date_range('1/1/2012', periods=100, freq='S')
ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
# make use of objgraph
objgraph.show_growth(limit=20)
print ""
ts = ts.resample('5Min', how='sum')
ts = None
gc.collect()
objgraph.show_growth(limit=20)
```
Output on my machine (Ubuntu 14.04.1, 64bit):
```
function 16127 +16127
tuple 4324 +4324
dict 4025 +4025
list 2170 +2170
cell 2151 +2151
weakref 2131 +2131
wrapper_descriptor 1795 +1795
builtin_function_or_method 1635 +1635
type 1447 +1447
getset_descriptor 1427 +1427
method_descriptor 1056 +1056
property 780 +780
module 641 +641
instancemethod 581 +581
member_descriptor 469 +469
set 335 +335
instance 282 +282
classobj 247 +247
cache_readonly 167 +167
classmethod 151 +151
wrapper_descriptor 1804 +9
getset_descriptor 1431 +4
dict 4029 +4
member_descriptor 472 +3
function 16128 +1
Day 1 +1
cell 2152 +1
DatetimeEngine 1 +1
Minute 1 +1
```
Thus, the space is still allocated for certain objects. Have I missed something?
Module versions:
pandas: 0.14.1
scipy: 0.13.3
numpy: 1.8.1
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| null | 2 | 2014-09-27T17:05:54Z | 2014-10-04T16:49:11Z | 2014-10-04T16:49:11Z | NONE | null | First attempt at adding coveralls to pandas
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} | 1 | 2014-09-27T18:03:29Z | 2014-09-29T22:07:51Z | 2014-09-29T14:52:54Z | CONTRIBUTOR | null | Fix to issue https://github.com/pydata/pandas/issues/6780
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} | 1 | 2014-09-27T19:37:12Z | 2014-09-29T18:08:00Z | 2014-09-29T13:21:50Z | CONTRIBUTOR | null | Fixes https://github.com/pydata/pandas/issues/8359
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| null | 2 | 2014-09-27T20:09:11Z | 2014-11-28T15:41:45Z | 2014-09-27T21:06:30Z | CONTRIBUTOR | null | Closes #5748
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| null | 1 | 2014-09-27T20:57:09Z | 2014-09-28T22:04:46Z | 2014-09-28T22:04:46Z | NONE | null | I see the apply() method is used in both Series and DataFrame. Is it better to put apply() method in generic.NDFrame, so this method can be passed to its sub classes?
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} | 1 | 2014-09-27T21:11:34Z | 2014-09-29T13:24:23Z | 2014-09-29T13:23:48Z | CONTRIBUTOR | null | Possible solution for https://github.com/pydata/pandas/pull/8107#issuecomment-57017595
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} | 3 | 2014-09-28T17:52:25Z | 2014-09-30T15:59:30Z | 2014-09-30T15:53:52Z | CONTRIBUTOR | null | closes #6787
Uses ngroups=10000 as suggested in the issue, which takes about 1 hour on my desktop.
For results (vb_suite.log, pkl file) see: https://gist.github.com/dlovell/ea3400273314e7612f6e
Note: gist references a different commit hash. I changed the commit message and added modification to doc/source/v0.15.0.txt, but the actual modifications to vb_suite/groupby.py are principally the same.
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} | 0 | 2014-09-28T22:04:22Z | 2014-09-29T21:43:23Z | 2014-09-29T21:43:23Z | CONTRIBUTOR | null | ```
In [48]: s
Out[48]:
0 1411161507178
1 1411138436009
2 1411123732180
3 1411167606146
4 1411124780140
5 1411159331327
6 1411131745474
7 1411151831454
8 1411152487758
9 1411137160544
Name: 1, dtype: int64
In [49]: pd.DatetimeIndex(pd.to_datetime(s,unit='ms')).tz_localize('UTC').tz_convert('US/Eastern')
Out[49]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2014-09-19 17:18:27.178000-04:00, ..., 2014-09-19 10:32:40.544000-04:00]
Length: 10, Freq: None, Timezone: US/Eastern
```
Which should be equivalent to
```
In [50]: pd.to_datetime(s,unit='ms').dt.tz_localize('UTC').dt.tz_convert('US/Eastern')
TypeError: Cannot convert tz-naive timestamps, use tz_localize to localize
```
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| null | 2 | 2014-09-29T11:53:54Z | 2014-09-30T13:37:14Z | 2014-09-30T13:37:14Z | NONE | null | I sometimes find myself unable to join two dataframes on a column with large numbers when one is int64 and the other is float64. Apparently, having a NaN value, forces a column that would have otherwise been int64 be cast into float64 (when using the read_csv function), and then joining doesn't work. Here is a minimal example which shows the problem:
``` python
import pandas as pd
from tempfile import mkstemp
import os
d1 = pd.DataFrame([9222520091583672535],index=["a"],columns=["user"])
_,temp_path = mkstemp()
with open(temp_path,"w") as f:
f.write("user,account_balance"+"\n")
f.write("9222520091583672535,228"+"\n") # exact same user id as before
f.write(",500"+"\n") # nan value - if this weren't added, the join would work
d2 = pd.read_csv(temp_path)
print d1.dtypes # d1.user is int64
print d2.dtypes # d2.user is float64 (somehow, having a NaN value forces it to be so)
os.remove(temp_path)
assert(len(pd.merge(d1,d2,on="user"))==1) # this fails
```
I'm using pandas version 0.14.1 (the bug was there in previous versions as well) and the complete show_versions() output is:
## INSTALLED VERSIONS
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.17.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.1
nose: 1.3.3
Cython: 0.20.1
numpy: 1.9.0
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.1.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2014.7
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.5
lxml: 3.3.5
bs4: 4.3.1
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: 2.5.2 (dt dec pq3 ext)
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} | 3 | 2014-09-29T13:04:23Z | 2014-09-29T21:36:53Z | 2014-09-29T21:36:42Z | CONTRIBUTOR | null | Minor doc updates as part of #8375
- docstring in in cut/qcut + docstring examples #8077 (comment)
- whatsnew: In the note change "levels" to "categories" and s/Schultz/Schulz/
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} | 2 | 2014-09-29T13:14:15Z | 2014-09-29T14:05:50Z | 2014-09-29T14:05:45Z | CONTRIBUTOR | null | Contribution to https://github.com/pydata/pandas/pull/7619
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} | 1 | 2014-09-29T14:03:46Z | 2014-09-29T21:46:17Z | 2014-09-29T21:43:23Z | CONTRIBUTOR | null | closes #8411
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} | 0 | 2014-09-29T14:08:51Z | 2014-09-30T14:10:34Z | 2014-09-30T14:10:34Z | CONTRIBUTOR | null | https://github.com/ContinuumIO/blaze/issues/670 (near the end)
I guess just needs maybe a bit more references at the top-level some how.
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} | 0 | 2014-09-29T15:51:59Z | 2014-09-30T19:39:45Z | 2014-09-30T19:39:45Z | CONTRIBUTOR | null | closes #7997
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} | 1 | 2014-09-29T16:21:27Z | 2014-09-29T20:55:23Z | 2014-09-29T20:55:13Z | CONTRIBUTOR | null | based on #8407 and #5748
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} | 4 | 2014-09-29T18:00:14Z | 2016-12-14T16:08:04Z | 2016-12-14T16:08:04Z | NONE | null | I'm new to pandas, so maybe I'm missing something, but I'd expected freq='.5s' to give me 500ms intervals, not 5s intervals:
> > > pd.date_range(0, periods=5, freq='.5s')[1]
> > > Timestamp('1970-01-01 00:00:05', offset='5S')
> > > pd.date_range(0, periods=5, freq='500ms')[1]
> > > Timestamp('1970-01-01 00:00:00.500000', offset='500L')
> > > pd.**version**
> > > '0.14.1'
> > > import sys; sys.version
> > > '3.4.1 (default, Jul 6 2014, 20:01:46) \n[GCC 4.9.0]'
Behaviour is the same under Python 2 and 3.
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} | 0 | 2014-09-29T18:06:20Z | 2014-09-29T20:52:03Z | 2014-09-29T20:52:02Z | CONTRIBUTOR | null | For https://github.com/pydata/pandas/pull/7447, add a searchsorted
stub, which simple raises `NotImplementedError`, so that we raise a
more clear error than attribute not found.
xref #8420
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| null | 3 | 2014-09-29T19:02:12Z | 2014-09-29T22:02:26Z | 2014-09-29T22:01:06Z | NONE | null | I am trying to use read_fwf to read in a fixed with text data file. I am hoping to use the dtype argument, which only works with the 'c' engine parser. I tried something like the following:
df = pd.read_fwf(dpath,widths=widths,header=0,skiprows=[0,2,3],thousands=',',engine='c',dtype={'cusip':str})
I received an error that the dtype option is not supported with the 'python-fwf' engine. As I understand, in the API docs, it says the c engine is necessary for the dtype argument. Is there no 'c' engine for read_fwf at the current moment, is is something else going on? I am using Pandas 14.1, python 3.4 as part of the Ancacondas distribution.
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} | 9 | 2014-09-29T21:51:15Z | 2014-09-30T14:10:34Z | 2014-09-30T14:10:34Z | CONTRIBUTOR | null | closes #8416
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} | 24 | 2014-09-30T03:26:27Z | 2016-07-11T13:18:57Z | 2016-01-02T23:17:41Z | CONTRIBUTOR | null | Close #6929. Hat tip to @behzadnouri for the z-scores idea in #8270, and to @seth-p for the general structure of the code, separating removal and addition of observations.
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} | 8 | 2014-09-30T08:43:29Z | 2019-07-08T01:26:50Z | 2019-07-08T01:26:50Z | NONE | null | Hello,
I have been experimenting with OrderedDicts lately, and found a bug with the DataFrame from_dict constructor. Here is a sample code.
``` python
import collections
import pandas as pd
firstrow={}
firstrow['foo'] = 'bar'
firstrow['baz'] = 'buzz'
row1 = pd.Series(firstrow)
secondrow={}
secondrow['foo'] = 'bar2'
secondrow['baz'] = 'buzz2'
row2 = pd.Series(secondrow)
roworder = collections.OrderedDict()
roworder['zShould be first'] = row1
roworder['Should be second'] = row2
# Ordering is respected when sorting on columns
df = pd.DataFrame.from_dict(data=roworder, orient='columns')
# But not when sorting on rows
incorrectdf = pd.DataFrame.from_dict(data=roworder, orient='index')
correctdf = df.transpose()
```
## INSTALLED VERSIONS
commit: None
python: 3.3.5.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 26 Stepping 5, GenuineIntel
byteorder: little
LC_ALL: None
LANG: fr_CH
pandas: 0.14.1
nose: 1.3.4
Cython: 0.20.1
numpy: 1.9.0
scipy: 0.13.3
statsmodels: 0.5.0
IPython: 2.2.0
sphinx: 1.2.3
patsy: 0.3.0
scikits.timeseries: None
dateutil: 2.2
pytz: 2013.9
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.0
openpyxl: None
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.5.7
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
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https://api.github.com/repos/pandas-dev/pandas/issues/8426 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8426/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8426/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8426/events | https://github.com/pandas-dev/pandas/issues/8426 | 44,431,869 | MDU6SXNzdWU0NDQzMTg2OQ== | 8,426 | Performance issues with groupby for large values of ngroups | {
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} | 1 | 2014-09-30T12:04:01Z | 2018-07-06T21:49:27Z | 2018-07-06T21:49:21Z | CONTRIBUTOR | null | Per @jreback: https://github.com/pydata/pandas/pull/8410#issuecomment-57100206
Groupby for large values of ngroups, 10000 in this case, is very slow.
```
Invoked with :
--ncalls: 3
--repeats: 3
---------------------------------------------------------
Test name | #0 |
---------------------------------------------------------
```
...
```
groupby_large_ngroups_value_counts | 23527.0356 |
groupby_large_ngroups_nunique | 19496.5300 |
groupby_large_ngroups_describe | 82329.8963 |
groupby_large_ngroups_mad | 21441.5947 |
groupby_large_ngroups_pct_change | 18865.5750 |
```
See also: https://gist.github.com/dlovell/ea3400273314e7612f6e
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} | 8 | 2014-09-30T15:41:38Z | 2021-04-16T19:43:01Z | null | CONTRIBUTOR | null | This is incorrect, as this is applied column by column (as they are different dtypes)
so `_first_compat` should first compute the mask then use it.
from [SO](http://stackoverflow.com/questions/26108181/selecting-first-row-with-groupby-and-nan-columns)
```
In [18]: df = pd.DataFrame([{'id':"a",'val':np.nan, 'val2':-1},{'id':"a",'val':'TREE','val2':15}])
In [19]: df
Out[19]:
id val val2
0 a NaN -1
1 a TREE 15
In [20]: df.groupby('id').first()
Out[20]:
val val2
id
a TREE -1
```
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} | 1 | 2014-09-30T17:06:42Z | 2014-10-30T11:19:43Z | 2014-10-29T23:45:11Z | CONTRIBUTOR | null | I find the behavior of `str.split` a bit odd, and it should by default just return a `DataFrame` (or maybe have a new function / option). Its straightforward to coerce it, but could/should be done internally.
(and `str.extract` does return a DataFrame IIRC)
```
In [22]: s
Out[22]:
0 apple
1 apple, orange
2 orange
dtype: object
In [23]: s.str.split(',\s+')
Out[23]:
0 [apple]
1 [apple, orange]
2 [orange]
dtype: object
In [24]: s.str.split(',\s+').apply(Series)
Out[24]:
0 1
0 apple NaN
1 apple orange
2 orange NaN
```
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https://api.github.com/repos/pandas-dev/pandas/issues/8429 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8429/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8429/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8429/events | https://github.com/pandas-dev/pandas/pull/8429 | 44,492,564 | MDExOlB1bGxSZXF1ZXN0MjIwMzA1MDI= | 8,429 | TST: Adjust boxplot tests following MPL API change | {
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| null | 0 | 2014-09-30T19:54:44Z | 2017-04-05T02:05:59Z | 2014-10-04T14:09:10Z | CONTRIBUTOR | null | fliers on a boxplot are now 1 object instead of 2. [See here](https://github.com/matplotlib/matplotlib/issues/3544#issuecomment-57360910).
Takes care of one part of https://github.com/pydata/pandas/issues/8402
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} | 2 | 2014-09-30T21:35:39Z | 2014-10-01T13:04:11Z | 2014-10-01T13:03:57Z | CONTRIBUTOR | null | ``` python
import pandas as pd
import numpy as np
from pandas.tseries.resample import TimeGrouper
import pandas.util.testing as tm
df = tm.makeTimeDataFrame()
tg = TimeGrouper('M')
func = lambda x: (x - 1)
df.groupby(tg).transform(func)
```
```
/externals/pandas/pandas/core/groupby.py in _get_compressed_labels(self)
1360 def _get_compressed_labels(self):
-> 1361 all_labels = [ping.labels for ping in self.groupings]
1362 if self._overflow_possible:
TypeError: 'NoneType' object is not iterable
```
Seems related to #8049 as that check group_info which errors on BinGrouper since there are no `groupings`
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} | 7 | 2014-10-01T04:26:01Z | 2014-10-03T19:12:07Z | 2014-10-03T19:12:07Z | CONTRIBUTOR | null | So I'm running into an issue with creating a `Panel` out of legacy `DataFrame` pickles. The issue is that `_id` is set to `None` and the current `Index.is_` logic treats any two Indexes with unset `_id` as the same.
I thought the issue was with the unpickling. But it looks like there was an attempt to address this
``` python
def is_(self, other):
return self._id is getattr(other, '_id', Ellipsis)
```
I'm guessing whoever thought that `getattr(other, '_id', Ellipsis)` would return `Ellipsis` if the `_id` was not set.
So I'm seeing:
1. Make unpickling make a new `_id` if it's not found in state
2. Make any `Index.is_` always return `False` if either one has an unset id.
Maybe both?
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| null | 7 | 2014-10-01T08:32:36Z | 2014-10-17T18:28:00Z | 2014-10-01T11:26:03Z | CONTRIBUTOR | null | I noticed that indexing via dict is ~100 times faster than through `Series` in 0.14.1. Is this known (to be expected)?
```
mydict = {'a': 3, 'b': 4}
myser = pd.Series(mydict)
%timeit mydict['a']
%timeit myser['a']
10000000 loops, best of 3: 39.4 ns per loop
100000 loops, best of 3: 4.9 µs per loop
```
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} | 3 | 2014-10-01T10:21:34Z | 2014-11-06T11:26:17Z | 2014-11-06T11:26:17Z | NONE | null | I have pandas version: pandas-0.13.1-py2.7-win32.egg
Routine pandas.io.data._dl_mult_symbols has an "except IOError" where the following is executed:
stocks[sym] = np.nan
The next thing that happens is:
try:
return Panel(stocks).swapaxes('items', 'minor')
However, if any of the values in stocks equals nan, then "return Panel(stocks).swapaxes('items', 'minor')" throws an error that is not handled and terminates the script.
I discovered this by calling pandas.io.data.get_data_yahoo(['EQQQ.F'],'20140930','20140930',3,0.001,False,False,5)
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} | 0 | 2014-10-01T12:34:42Z | 2014-10-01T13:03:57Z | 2014-10-01T13:03:57Z | CONTRIBUTOR | null | closes #8430
xref #8046
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} | 17 | 2014-10-01T14:32:33Z | 2021-04-11T05:12:36Z | null | NONE | null | In[43]: datetime(2014,10,10) + BQuarterBegin()
Out[43]: Timestamp('2014-12-01 00:00:00')
In[45]: datetime(2014,10,10) + QuarterBegin()
Out[45]: Timestamp('2014-12-01 00:00:00')
Expected output is 2015-01-01.
(Note QuarterEnd and BQuarterEnd _do_ produce the expected output of 2014-12-31)
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} | 2 | 2014-10-01T17:06:49Z | 2018-07-06T21:36:48Z | 2018-07-06T21:36:42Z | CONTRIBUTOR | null | Hello,
I have a Pandas DataFrame `df` like this:
```
name value
2014-01-01 temp 10.0
2014-01-01 p 101.0
2014-01-02 temp 11.5
2014-01-02 p 100.0
```
I wanted to get a new DataFrame `df2`like:
```
temp p
2014-01-01 10.0 101.0
2014-01-02 11.5 100.0
```
I did this:
```
df2 = df.pivot(columns='name', values='value')
```
but it raises `cannot label index with a null key`
I need to do this
```
df2 = df.pivot(index=df.index, columns='name', values='value')
```
see http://stackoverflow.com/questions/26101246/spread-data-across-multiple-columns
In my mind, passing index parameter shouldn't be necessary when applying pivot function in such a case.
Kind regards
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} | 9 | 2014-10-01T20:38:52Z | 2014-10-02T15:19:31Z | 2014-10-02T15:19:31Z | CONTRIBUTOR | null | I still don't have a good enough grasp on the internals to go diving for this one quickly, but here's the observed behavior:
``` python
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: temp = [pd.Series([False, np.nan]), pd.Series([False, np.nan]), pd.Series(index=range(2)), pd.Series(index=range(2)), pd.Series(index=range(2)), pd.Series(index=range(2))]
In [4]: temp[2][:-1] = temp[3][:-1] = temp[4][0] = temp[5][0] = False
In [5]: [[x.equals(y) for y in temp] for x in temp]
Out[5]:
[[True, True, False, False, False, False],
[True, True, False, False, False, False],
[False, False, True, False, False, False],
[False, False, False, True, False, False],
[False, False, False, False, True, True],
[False, False, False, False, True, True]]
```
Here the 4x4 square in the upper left should be `True`. (`temp[4:6]` are different because inserting `False` into a `float64` array or vice-versa coerces `False` into `0.0`; this is inconvenient when attempting to use the array for boolean indexing.)
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} | 6 | 2014-10-01T21:43:35Z | 2018-11-01T06:18:57Z | 2018-11-01T06:18:57Z | MEMBER | null | Add these to the API docs:
The contents of `pd.tseries.offsets.__all__`
```
['Day',
'BusinessDay',
'BDay',
'CustomBusinessDay',
'CDay',
'CBMonthEnd',
'CBMonthBegin',
'MonthBegin',
'BMonthBegin',
'MonthEnd',
'BMonthEnd',
'SemiMonthEnd',
'SemiMonthBegin',
'BusinessHour',
'CustomBusinessHour',
'YearBegin',
'BYearBegin',
'YearEnd',
'BYearEnd',
'QuarterBegin',
'BQuarterBegin',
'QuarterEnd',
'BQuarterEnd',
'LastWeekOfMonth',
'FY5253Quarter',
'FY5253',
'Week',
'WeekOfMonth',
'Easter',
'Hour',
'Minute',
'Second',
'Milli',
'Micro',
'Nano',
'DateOffset']
```
and `pd.tseries.api.infer_freq`
---
From mailingslist (https://groups.google.com/forum/?fromgroups#!topic/pydata/66lTrtgev7A), eg `frequencies.is_super/subperiod` are missing in the docs (both in user guide as in api docs)
A lot of the functions/objects in `pandas.tseries.offsets` and `pandas.tseries.frequencies` are not included in the API docs, or mentioned in the timeseries.rst docs.
Furthermore, the difference between both submodules is not fully clear to me (eg the offsets objects like `BDay` are available in both)
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} | 1 | 2014-10-02T02:31:14Z | 2014-10-02T06:41:56Z | 2014-10-02T06:41:41Z | CONTRIBUTOR | null | Trivial update to select_dtypes docstring to hint on how to include Categorical types, as it's not part of the numpy type-hierarchy mentioned here.
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} | 3 | 2014-10-02T08:35:02Z | 2014-10-05T19:29:01Z | 2014-10-05T19:29:00Z | MEMBER | null | WIP, related to #7960
- [x] renaming of
- PandasSQLAlchemy -> SQLDatabase
- PandasSQLTable -> SQLTable
- PandasSQLLegacy -> SQLiteDatabase
- PandasSQLLegacyTable -> SQLiteTable
- SQLDatabase.read_sql -> read_query
- [ ] adding docstrings to public functions of SQLDatabase
- [x] read_query, read_table, to_sql
- [ ] has_table, drop_table, ..
- [ ] add docs on use of SQLDatabase
- [ ] add api docs for SQLDatabase
- [x] rename some mentions of 'legacy' to 'fallback'
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| null | 1 | 2014-10-02T09:15:47Z | 2014-10-02T11:35:24Z | 2014-10-02T11:35:11Z | NONE | null | I can create multi-indexes with tuples as level values and union levels:
```
>> import pandas as pd
>> pd.version.version
'0.14.1'
>> i1 = pd.MultiIndex.from_tuples([((1,2), 3), ((4, 5), 6)], names=['a', 'b'])
>> i2 = pd.MultiIndex.from_tuples([((2,2), 3), ((5, 5), 6)], names=['a', 'b'])
>> i1.levels[0]
Index([(1, 2), (4, 5)], dtype='object')
>> i2.levels[0]
Index([(2, 2), (5, 5)], dtype='object')
>> i1.levels[0].union(i2.levels[0])
Index([(1, 2), (2, 2), (4, 5), (5, 5)], dtype='object')
```
But can't union multi-indexes:
```
>> i1.union(i2)
/home/lyakishev/apps/ui2_2/local/lib/python2.7/site-packages/pandas/core/index.pyc in union(self, other)
3753 uniq_tuples = lib.fast_unique_multiple([self.values, other.values])
3754 return MultiIndex.from_arrays(lzip(*uniq_tuples), sortorder=0,
-> 3755 names=result_names)
3756
3757 def intersection(self, other):
/home/lyakishev/apps/ui2_2/local/lib/python2.7/site-packages/pandas/core/index.pyc in from_arrays(cls, arrays, sortorder, names)
2795 return Index(arrays[0], name=name)
2796
-> 2797 cats = [Categorical.from_array(arr) for arr in arrays]
2798 levels = [c.levels for c in cats]
2799 labels = [c.labels for c in cats]
/home/lyakishev/apps/ui2_2/local/lib/python2.7/site-packages/pandas/core/categorical.pyc in from_array(cls, data)
101 the unique values of `data`.
102 """
--> 103 return Categorical(data)
104
105 _levels = None
/home/lyakishev/apps/ui2_2/local/lib/python2.7/site-packages/pandas/core/categorical.pyc in __init__(self, labels, levels, name)
82 name = getattr(labels, 'name', None)
83 try:
---> 84 labels, levels = factorize(labels, sort=True)
85 except TypeError:
86 labels, levels = factorize(labels, sort=False)
/home/lyakishev/apps/ui2_2/local/lib/python2.7/site-packages/pandas/core/algorithms.pyc in factorize(values, sort, order, na_sentinel)
128 table = hash_klass(len(vals))
129 uniques = vec_klass()
--> 130 labels = table.get_labels(vals, uniques, 0, na_sentinel)
131
132 labels = com._ensure_platform_int(labels)
/home/lyakishev/apps/ui2_2/local/lib/python2.7/site-packages/pandas/hashtable.so in pandas.hashtable.Int64HashTable.get_labels (pandas/hashtable.c:7797)()
ValueError: Buffer has wrong number of dimensions (expected 1, got 2)
```
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| null | 1 | 2014-10-02T13:33:10Z | 2014-10-02T13:42:16Z | 2014-10-02T13:42:16Z | NONE | null | pandas 0.14.0 in plotting.py
the varable n in line 389: n = len(frame)
is wrongly asigned again in line 404: n = len(frame.columns) - 1
that brakes in line 409: for i in range(n):
This shuld fix the bug
line 389: rows = len(frame)
line 409: for i in range(rows):
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} | 2 | 2014-10-02T17:37:00Z | 2014-10-05T17:03:01Z | 2014-10-05T17:03:01Z | CONTRIBUTOR | null | I came across this today when being a little bit careless when using a boolean array to index into a Series. The result was not what I expected which turned out to be because I was using a boolean index in a DataFrame to index a Series. Either we should make this work or throw an error that you cannot index a Series with a DataFrame.
```
import pandas as pd
series = pd.Series(10, index=range(10))
df = pd.DataFrame(range(10), index=range(10))
series[df > 5]
Out[9]:
0 10
dtype: int64
# What is really meant is this:
series[df[0] > 5]
Out[11]:
6 10
7 10
8 10
9 10
dtype: int64
```
This is on 0.14.1
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} | 5 | 2014-10-02T19:24:48Z | 2015-02-16T12:35:24Z | 2015-02-16T12:35:24Z | NONE | null | xref #9286
0 / 0 does not have a value, and should return NaN, not infinity.
``` python
>>> x = pd.DataFrame([[0, 0], [1, 0], [-1, 0]])
>>> x[0] / x[1]
0 inf <------------- here
1 inf
2 -inf
dtype: float64
```
numpy does this correctly:
``` python
>>> y = np.array([[0, 0], [1, 0], [-1, 0]])
>>> y[:, 0] / y[:, 1]
array([ nan, inf, -inf])
```
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| null | 2 | 2014-10-02T21:37:01Z | 2014-10-02T21:42:30Z | 2014-10-02T21:42:30Z | NONE | null | When selecting the max() of a Series containing timedelta64, the return value is a Series with a single element, instead of the element itself:
In [1]: pd.**version**
Out[1]: '0.14.1'
In [2]: np.**version**
Out[2]: '1.9.0'
In [3]: from numpy.random import randn
In [4]: s = pd.Series([np.timedelta64(int(x), 'us') for x in randn(10)*1e6])
In [5]: s.dtype
Out[5]: dtype('<m8[ns]')
In [6]: s.max()
Out[6]:
0 00:00:01.275209
dtype: timedelta64[ns]
In [7]: type(s.max())
Out[7]: pandas.core.series.Series
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} | 4 | 2014-10-02T21:52:54Z | 2018-04-03T07:05:06Z | 2018-04-03T07:04:59Z | NONE | null | Extensive documentation is probably not needed, but at a minimum, the docstring should say
- what the red line is (median)
- what the box shows (quartiles)
- what the whiskers show (?)
- by what rules the outliers are shown or not
A link to wikipedia and/or matplotlib docs would be nice too.
I could try to contribute this, if it is deemed useful.
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} | 12 | 2014-10-03T08:08:26Z | 2015-03-07T00:02:58Z | 2015-03-07T00:02:58Z | CONTRIBUTOR | null | closes #781
The 'decimal' option exists for read_csv method but not yet in 'to_csv' methods.
The lack of this option is particulary painful when we _have to_ work with Excel with European regional settings.
This modification add this option to both Series.to_csv and Dataframe.to_csv.
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} | 0 | 2014-10-03T12:05:53Z | 2014-10-03T12:33:46Z | 2014-10-03T12:33:46Z | CONTRIBUTOR | null | xref #7619
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} | 6 | 2014-10-03T12:28:36Z | 2018-10-25T12:45:14Z | 2018-10-25T12:45:13Z | CONTRIBUTOR | null | https://docs.pytest.org/en/latest/usage.html#profiling-test-execution-duration
Can add `--durations=10` or something to list the 10 slowest tests.
We could add this to our setup.cfg so it's used by default. | {
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} | 2 | 2014-10-03T15:26:32Z | 2014-10-05T15:33:47Z | 2014-10-03T18:00:03Z | CONTRIBUTOR | null | Part of #6581. Remove DataFrame.delevel which was previously deprecated in version 0.7.
@jreback
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} | 2 | 2014-10-03T15:49:28Z | 2014-12-07T22:59:39Z | 2014-12-07T22:27:38Z | NONE | null | I'm trying to use to_html() to print a DataFrame that has a MultiIndex column, and I want to hide the index. When I do, the column labels are shifted right by one column. I use:
``` python
import pandas as pd
from IPython.display import HTML
df = pd.DataFrame({'a': range(10), 'b': range(10,20), 'c': range(10,20), 'd': range(10,20)})
df.columns = pd.MultiIndex.from_product([['a', 'b'], ['c', 'd']])
HTML(df.to_html(index=False))
```
and the result is:
Seems to me to_html() isn't properly accounting for the index=False when there is a column Multindex. With single-level column index ( df.columns=list('abcd') ) it prints fine. With index=True, to_html() also works fine.
My versions are:
## INSTALLED VERSIONS
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Darwin
OS-release: 13.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.1
nose: 1.3.3
Cython: 0.20.1
numpy: 1.9.0
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.1.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.7
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.5
lxml: 3.3.5
bs4: 4.3.1
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
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} | 1 | 2014-10-03T18:07:31Z | 2014-10-04T16:38:15Z | 2014-10-04T16:38:15Z | CONTRIBUTOR | null | So it seems by using a full Int64 array for the codes, plus the categorires we are actually using MORE memory to store a Categorical. Because the pointers are the same sized as an object array (plus have the categories).
So need to change the codes store to use a smaller dtype of int. Maybe switch this to a plain ndarray, and use `dtype=uint8`. Would provide a lot of benefit
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} | 0 | 2014-10-03T18:42:22Z | 2014-10-03T19:12:08Z | 2014-10-03T19:12:07Z | CONTRIBUTOR | null | closes #8431
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} | 6 | 2014-10-03T20:55:41Z | 2014-10-04T16:38:15Z | 2014-10-04T16:38:15Z | CONTRIBUTOR | null | closes #8453
So easy enough to optimize the dtype of the codes array depending on the number of categories. Most
of the time will simply be `int8`. So pretty good savings.
This doesn't completely solve the storage issues as currently `factorize` returns a `int64`, but can deal with that later (as might also need some more cython definitions).
And of course in the extreme case (where len(categories) == len(codes) you are better off with an object dtype).
```
In [1]: s = Series(['foo','bar']*1000)
In [2]: s.nbytes
Out[2]: 16000
In [3]: s.astype('category').nbytes
Out[3]: 2016
In [5]: s.astype('category').values.codes
Out[5]: array([1, 0, 1, ..., 0, 1, 0], dtype=int8)
```
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} | 0 | 2014-10-03T21:10:24Z | 2014-10-30T11:19:17Z | 2014-10-30T11:19:17Z | CONTRIBUTOR | null | xref #8453
factorize returns int64s, prob need some cython updating to allow lower ints returns
FrozenList by default also uses a ndarray like (that is int64)
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} | 3 | 2014-10-03T22:15:31Z | 2014-10-04T17:41:00Z | 2014-10-04T17:19:50Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/8258, but more generally, on pandas master, assignment with indexer will align _both rows and columns_ if all columns have the same type:
```
>>> cols, idx = ['jim', 'joe', 'jolie'], ['first', 'last']
>>> vals = np.arange(1, 7).reshape(2, 3, order='F')
>>> df = DataFrame(vals, columns=cols, index=idx)
>>> left, rhs = df.copy(), - df.iloc[::-1, -2::-1]
>>> print(left, rhs, sep='\n')
jim joe jolie
first 1 3 5
last 2 4 6
joe jim
last -4 -2
first -3 -1
>>>
>>> left.loc[:, ['jim', 'joe']] = rhs
>>> left
jim joe jolie
first -1 -3 5
last -2 -4 6
```
However, if I change the type of a column even _outside_ the section which gets assigned to, _only columns_ are aligned:
```
>>> df = DataFrame(vals, columns=cols, index=idx)
>>> df['jolie'] = df['jolie'].astype('float64')
>>> left, rhs = df.copy(), - df.iloc[::-1, -2::-1]
>>> print(left, rhs, sep='\n')
jim joe jolie
first 1 3 5
last 2 4 6
joe jim
last -4 -2
first -3 -1
>>>
>>> left.loc[:, ['jim', 'joe']] = rhs
>>> left
jim joe jolie
first -2 -4 5
last -1 -3 6
```
Code-wise this is a bug, because [this line](https://github.com/pydata/pandas/blob/fccd7feabfe0bf15c30d712f3a4a4ff71e76ad4a/pandas/core/indexing.py#L446) is taking the _entire frame_'s index and re-indexes to that, regardless of the actual indexer which selects a section of the frame to assign to. That said, there are two places in tests where they rely on this behaviour, [here](https://github.com/pydata/pandas/blob/fccd7feabfe0bf15c30d712f3a4a4ff71e76ad4a/pandas/tests/test_frame.py#L1405) and [here](https://github.com/pydata/pandas/blob/fccd7feabfe0bf15c30d712f3a4a4ff71e76ad4a/pandas/tests/test_indexing.py#L1033).
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} | 3 | 2014-10-04T12:38:12Z | 2018-07-06T21:27:10Z | 2018-07-06T21:27:03Z | CONTRIBUTOR | null | xref #8293
The code has evolved quite a bit and could probably benefit from refactoring.
In particular warpping the busdaycalendar logic in a wrapper class could make the code easier.
Notes about the current implementation:
- `CustomBusinessDay` uses `np.busdaycalendar` to do the computations.
- To speed up creation of new `CustomBusinessDay` offsets the calendar is cached in `kwds`.
- To check equality of two `CustomBusinessDay` instances, the attributes `holidays` and `weekdays` are used.
- remove EXPERIMENTAL (in docs, but check off in the experimental issue #5049)
- might be some docs that say requires numpy >= 1.7 (but now all of pandas on that, so can cleanup)
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| null | 0 | 2014-10-04T13:34:22Z | 2015-08-18T12:45:00Z | 2014-10-04T15:09:16Z | CONTRIBUTOR | null | Branched off of https://github.com/pydata/pandas/pull/8037/files
just a couple of minor tweaks to that and some PEP8 stuff.
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CLN: drop copy_if_needed kwarg of Index.reindex, it's True everywhere
TST: enable back empty-list loc/ix tests that failed before
DOC: Bump Index.reindex docstring
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} | 8 | 2014-10-04T17:18:39Z | 2014-10-05T19:03:13Z | 2014-10-05T14:05:18Z | CONTRIBUTOR | null | This PR fixes #7774 and also includes:
TST: check index/columns types when doing empty loc/ix tests
CLN: don't _ensure_index in NDFrame._reindex_axes, it is done in Index.reindex
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} | 7 | 2014-10-04T17:52:59Z | 2014-10-05T01:09:59Z | 2014-10-05T01:09:59Z | CONTRIBUTOR | null | #7971 recently changed use to use conda on the 5 main builds
installs now take 300+ seconds, vs 100+ before.
I think a big cause is the installation of some basic libraries that conda installs by default
e.g. Python 2.7.8. But I don't think this is necessary and maybe can skip some of these
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} | 1 | 2014-10-04T21:39:51Z | 2016-03-12T15:21:11Z | 2016-03-12T15:21:06Z | MEMBER | null | continued from #7447
- [ ] `timedelta64` tests
- [ ] more edge case testing
- [ ] `side` argument testing
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} | 4 | 2014-10-05T03:12:05Z | 2018-07-06T21:25:03Z | 2018-07-06T21:24:57Z | NONE | null | xref #6771
> Period("2000-01")
> Period('2000-01', 'M')
>
> PeriodIndex(["2000-01"])
> ValueError: freq not specified and cannot be inferred from first element
Same problem with 2000-Jan and Jan-2000. Works fine if I pass freq="M".
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} | 5 | 2014-10-05T05:07:32Z | 2014-10-06T22:56:15Z | 2014-10-06T13:54:51Z | NONE | null | I ran into this unexplained behaviour with `groupby` when using `group_keys=True` (the default),
it's not clear why using `x` vs. `x[:]` causes the `group_keys` argument to be ignored.
``` python
In [86]: df = DataFrame({'key': [1, 1, 1, 2, 2, 2, 3, 3, 3],
...: 'value': range(9)})
...: df
Out[86]:
key value
0 1 0
1 1 1
2 1 2
3 2 3
4 2 4
5 2 5
6 3 6
7 3 7
8 3 8
In [87]: df.groupby('key', group_keys=True).apply(lambda x: x[:].key)
Out[87]:
key
1 0 1
1 1
2 1
2 3 2
4 2
5 2
3 6 3
7 3
8 3
Name: key, dtype: int64
In [88]: df.groupby('key', group_keys=True).apply(lambda x: x.key)
Out[88]:
0 1
1 1
2 1
3 2
4 2
5 2
6 3
7 3
8 3
Name: key, dtype: int64
```
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| null | 1 | 2014-10-05T05:15:55Z | 2014-10-05T13:52:39Z | 2014-10-05T13:52:39Z | NONE | null | Fix for #8466.
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} | 9 | 2014-10-05T05:23:20Z | 2014-10-07T14:40:00Z | 2014-10-05T21:55:33Z | NONE | null | Nested dictionaries are commonly emitted by web APIs that speak json. Getting this sort of data into pandas isn't very easy right now, without manual data structure munging, as the dicts reaing objects rather then converted into a flat naming hirerchy.
Here's a common example of data:
``` python
In [95]: data=[dict(user=dict(uid=123,full_name='Alice'),followers=1),
...: dict(user=dict(uid=456,full_name='Bob'),followers=2)]
...: data
Out[95]:
[{'followers': 1, 'user': {'full_name': 'Alice', 'uid': 123}},
{'followers': 2, 'user': {'full_name': 'Bob', 'uid': 456}}]
```
Pandas keeps the dicts as objects:
``` python
In [96]: df=pd.DataFrame(data)
...: df
Out[96]:
followers user
0 1 {u'uid': 123, u'full_name': u'Alice'}
1 2 {u'uid': 456, u'full_name': u'Bob'}
```
But I'd love to see something along the lines of:
``` python
In [91]: df=pd.DataFrame(data,flatten_dicts=True)
...: df
Out[94]:
followers user.uid user.full_name
0 1 123 Alice
1 2 456 Bob
```
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} | 0 | 2014-10-05T10:08:12Z | 2014-10-05T18:01:12Z | 2014-10-05T18:01:12Z | MEMBER | null | Related to #8341, does not closes it, but deals with the fact that it now fails silently
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} | 0 | 2014-10-05T14:57:48Z | 2014-10-05T23:52:47Z | 2014-10-05T23:52:47Z | CONTRIBUTOR | null | This currently raises `TypeError` as its disabled in `core/nanops.py`, see here: https://github.com/pydata/pandas/commit/d29d4c6623a29db36a4eb8d062bdcfe603e2b490
The issue is that `.var()` can generate a value that is too big for the timedeltas to hold propery, and std CALLS var.
solns:
- reduce the value of the timedeltas going into var to make them work (and inflate on exit), e.g. divide by 1e9 or something. lose some small amount of precision.
- allow std to return a valid value by passing a flag thru to var (and don't wrap the results). a bit hacky
```
In [7]: max_int = np.iinfo(np.int64).max
In [8]: max_int
Out[8]: 9223372036854775807
In [9]: pd.Timedelta(max_int)
Out[9]: Timedelta('106751 days 23:47:16.854775')
In [10]: big_float = 3e19
In [12]: pd.Timedelta(big_float)
---------------------------------------------------------------------------
OverflowError Traceback (most recent call last)
OverflowError: Python int too large to convert to C long
In [13]: pd.Timedelta(np.sqrt(big_float))
Out[13]: Timedelta('0 days 00:00:05.477225')
```
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} | 3 | 2014-10-05T15:18:16Z | 2014-10-05T17:03:44Z | 2014-10-05T17:03:01Z | CONTRIBUTOR | null | Fixes #8444. I don't like importing DataFrame, but this was most obvious way to do it.
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} | 7 | 2014-10-05T19:59:28Z | 2014-10-05T21:15:45Z | 2014-10-05T21:15:45Z | MEMBER | null | From docs: http://pandas-docs.github.io/pandas-docs-travis/indexing.html#fast-scalar-value-getting-and-setting
```
dates = date_range('1/1/2000', periods=8)
df = DataFrame(randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])
df.at[dates[-1]+1, 0] = 7
```
does now raise an error
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} | 0 | 2014-10-05T20:04:32Z | 2014-10-05T21:24:57Z | 2014-10-05T21:24:57Z | MEMBER | null | Forgot to add this to #8470
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} | 0 | 2014-10-05T20:42:22Z | 2014-10-05T21:15:45Z | 2014-10-05T21:15:45Z | CONTRIBUTOR | null | closes #8473
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} | 0 | 2014-10-05T22:14:34Z | 2014-10-05T23:52:47Z | 2014-10-05T23:52:47Z | CONTRIBUTOR | null | close #8471
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} | 5 | 2014-10-05T22:36:00Z | 2014-10-30T11:19:43Z | 2014-10-27T14:42:02Z | MEMBER | null | Sorry, long issue :-)
In issue #7750 there was some heated discussion about backwards compatibility and the fact we did or did not care enough about it. I don't want to start a new discussion on that topic again, rest assured (or not now :-)), but notwithstanding the at some point rather personal attacks in that discussion, I think we still should look at what we can learn from it.
And what I learned is that we can do a _better_ job at documenting _backward incompatible changes_.
- We have no clear overview of these changes (and I am really speaking about 'changes for which users will have to change their code to keep it running/working correctly')
- In the whatsnew docs, there is the "API changes" section, which you could assume is about such kind of changes. In practice however, we made it a mix of enhancements (which are of course also api changes in se, as they 'change (add to) the api', but that fact is not really relevant) and breaking api changes.
- eg the current api changes section: http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#api-changes. Second point (addition of `level` keyword) is an enhancement, not a breaking api change, as with a lot of the other bullet points. For this reason, the 'real' breaking api changes disappear a bit between all the others.
- deprecations are in a seperate section, but are also a kind of api changes
---
**What I propose**:
1. a better stuctured whatsnew, with a more strict structure
2. limit the 'API changes' section to backwards incompatible changes, the rest are new features/enhancements (keep a clear distinction)
3. for each backwards incompatible change, very clearly explain: what was the previous behaviour? what is the new behaviour? How can I keep the old behaviour? What should I do to update my code to keep it working?
Some examples of other libraries: Scipy speaks about "_Backwards incompatible changes_" (http://docs.scipy.org/doc/scipy-0.14.0/reference/release.0.14.0.html), scikit-learn about "_API changes summary_" (http://scikit-learn.org/stable/whats_new.html), sqlalchemy about "_Behavioral changes_" (http://docs.sqlalchemy.org/en/rel_0_9/changelog/migration_09.html#behavioral-changes-orm-09) and matlab about "_Compatibility considerations_" (http://www.mathworks.nl/help/matlab/release-notes.html).
A possible structure (in the past, in pandas there was also a more rigid structure with new features/api changes):
```
1. New features
1.1 ... (subsections highlighting bigger new features)
1.2 Categoricals
1.3 Timedelta(Index)
1.4 .dt accessor
1.5 ....
2. Backwards incompatible API changes
2.1 Breaking changes
2.2 Deprecations
2.3 Removal previously deprecated (prior version deprecations)
3. Other enhancements
4. Performance improvements
5. Bug fixes
```
For comparison, the current table of contents for 0.15 whatsnew
```
API changes
Memory Usage
.dt accessor
Timezone API changes
Rolling/Expanding Moments API changes
Internal Refactoring
Categoricals in Series/DataFrame
TimedeltaIndex/Scalar
Prior Version Deprecations/Changes
Deprecations
Enhancements
Performance
Bug Fixes
```
Some points of dicussion:
- 'api changes' section before new features instead of after?
- 'other enhancements' as subsection in 'new features'
- how to name the 'backwards incompatible API changes' (other options like behavioral changes, compatibility considerations, breaking API changes, ...)
- and of course the general idea I describe in this issue
Thoughts?
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} | 1 | 2014-10-06T02:28:33Z | 2017-04-05T02:06:01Z | 2014-10-06T22:28:57Z | CONTRIBUTOR | null | Two things included:
1. Try to use ggplot style. If not, fall back to the previous 'default'.
2. Fixes some warnings in the doc build (part of https://github.com/pydata/pandas/issues/8234). There are some remaining warnings that I didn't have a chance to track down. Will see if I can later.
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} | 1 | 2014-10-06T02:31:53Z | 2017-04-05T02:06:08Z | 2015-05-09T16:00:59Z | CONTRIBUTOR | null | Chipping away at https://github.com/pydata/pandas/issues/8450
Unfortunately I wasn't able to find too much. Most of those tests are slow because they're checking a lot of things.
This will only shave about 30s off the test time. It's a start...
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} | 1 | 2014-10-06T03:31:45Z | 2014-10-07T00:36:25Z | 2014-10-06T13:03:02Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/7655
```
>>> df
jim joe jolie
first last first last first last
left center right left center right left center right left center right left center right left center right
2000-01-03 0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51
2000-01-04 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52
2000-01-05 2 5 8 11 14 17 20 23 26 29 32 35 38 41 44 47 50 53
>>> i, j = df.index.values.copy(), it[-1][:]
>>> np.random.shuffle(i)
>>> df['jim'] = df['jolie'].loc[i, ::-1]
>>> df.loc[:, ['jim', 'jolie']]
jim jolie
first last first last
left center right left center right left center right left center right
2000-01-03 36 39 42 45 48 51 36 39 42 45 48 51
2000-01-04 37 40 43 46 49 52 37 40 43 46 49 52
2000-01-05 38 41 44 47 50 53 38 41 44 47 50 53
>>> np.random.shuffle(j)
>>> df[('joe', 'first')] = df[('jolie', 'last')].loc[i, j]
>>> np.random.shuffle(j)
>>> df[('joe', 'last')] = df[('jolie', 'first')].loc[i, j]
>>> df.loc[:, ['joe', 'jolie']]
joe jolie
first last first last
left center right left center right left center right left center right
2000-01-03 45 48 51 36 39 42 36 39 42 45 48 51
2000-01-04 46 49 52 37 40 43 37 40 43 46 49 52
2000-01-05 47 50 53 38 41 44 38 41 44 47 50 53
```
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} | 2 | 2014-10-06T12:37:37Z | 2014-10-06T13:21:02Z | 2014-10-06T13:21:02Z | CONTRIBUTOR | null | This is really superfluous as `.describe` is much more comprehensive.
It is only a very brief mention in the docs, only a single test (for a single dtype), and not implemented generically.
https://github.com/pydata/pandas/blob/master/pandas/tools/describe.py
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} | 11 | 2014-10-06T12:41:01Z | 2014-10-30T11:19:43Z | 2014-10-28T00:25:36Z | NONE | null | A minor issue: The function in pandas.io.data uses 2-letter codes to identify countries in data requests, rather than the more common 3-letter codes. The latter is closer to the social norm at the iMF, World Bank, etc. More important, they're easier to track down. If you download the World Bank data (all of it), the data includes country names and 3-letter codes, but not 2-letter codes. So to me the 3-letter codes would be more user-friendly.
Cheers.
http://pandas.pydata.org/pandas-docs/stable/remote_data.html#world-bank
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} | 0 | 2014-10-06T12:48:06Z | 2014-10-06T13:21:03Z | 2014-10-06T13:21:02Z | CONTRIBUTOR | null | closes #8481
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} | 0 | 2014-10-06T13:19:58Z | 2014-10-06T13:54:52Z | 2014-10-06T13:54:52Z | CONTRIBUTOR | null | closes #8467
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} | 2 | 2014-10-06T14:28:12Z | 2014-10-06T16:50:42Z | 2014-10-06T15:01:11Z | CONTRIBUTOR | null | xref #8227
mixxing updates in indexing.rst
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} | 6 | 2014-10-06T14:34:21Z | 2014-10-06T20:10:08Z | 2014-10-06T19:56:16Z | CONTRIBUTOR | null | closes #7840
Unlike the implementation offered in #7840, this version preserves the option to pass prefixes to the `orient` parameter.
I also rewrote the docstring to use the more readable list format from `pd.DataFrame.to_json`.
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} | 4 | 2014-10-06T16:00:32Z | 2014-10-06T17:13:37Z | 2014-10-06T17:09:18Z | CONTRIBUTOR | null | closes #7967
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} | 17 | 2014-10-06T18:04:37Z | 2016-02-01T23:39:27Z | 2016-02-01T23:39:27Z | NONE | null | Hello, I can't get the BigQuery auth to work on remote servers:
```
WARNING:root:This function, oauth2client.tools.run(), and the use of the gflags library are deprecated and will be removed in a future version of the library.
Your browser has been opened to visit:
big-url-here
If your browser is on a different machine then exit and re-run
this application with the command-line parameter
--noauth_local_webserver
```
Even if I try to open that URL on my workstation, it tries to redirect me to a local server. The documentation says that:
> Should the browser not be available, or fail to launch, a code will be provided to complete the process manually.
however, I've yet to see this alternative.
Any help?
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} | 13 | 2014-10-06T19:18:01Z | 2014-10-06T21:58:40Z | 2014-10-06T21:58:40Z | MEMBER | null | I get an error while building the docs with `searchsorted` (but there is not error on the travis docs, http://pandas-docs.github.io/pandas-docs-travis/basics.html#sorting-by-index-and-value):
```
In [22]: ser = Series([3, 1, 2])
In [23]: a = ser.values
In [24]: a.searchsorted([0,3], sorter=np.argsort(a))
Out[24]: array([0, 2])
In [25]: ser.searchsorted([0,3], sorter=np.argsort(ser))
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-25-d96921d13837> in <module>()
----> 1 ser.searchsorted([0,3], sorter=np.argsort(ser))
/home/joris/scipy/pandas/pandas/core/series.pyc in searchsorted(self, v, side, sorter)
1431 """
1432 return self.values.searchsorted(Series(v).values, side=side,
-> 1433 sorter=sorter)
1434
1435 #------------------------------------------------------------------------------
ValueError: could not parse sorter argument
In [26]: pd.__version__
Out[26]: '0.14.1-641-gd65c9d7'
In [27]: np.__version__
Out[27]: '1.9.0'
```
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} | 2 | 2014-10-06T20:02:02Z | 2014-10-11T19:27:25Z | 2014-10-11T19:27:25Z | MEMBER | null | Compare stable http://pandas.pydata.org/pandas-docs/stable/visualization.html#basic-plotting-plot vs dev docs http://pandas-docs.github.io/pandas-docs-travis/visualization.html#basic-plotting-plot
For the simple example of Series plotting, a legens is added with entry None when there should be no legend:
```
ts = Series(randn(1000), index=date_range('1/1/2000', periods=1000))
ts = ts.cumsum()
ts.plot()
```
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} | 2 | 2014-10-06T20:21:42Z | 2014-10-06T22:01:43Z | 2014-10-06T21:58:41Z | MEMBER | null | Closes #8490
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} | 0 | 2014-10-06T20:32:54Z | 2014-10-06T20:33:17Z | 2014-10-06T20:33:17Z | MEMBER | null | There was some repetition in the docstring note of `plot` since the refactor of the docstring (hexbin part) + scatter is only needed for dataframe docstring (not series)
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} | 1 | 2014-10-06T20:44:01Z | 2014-10-30T11:19:43Z | 2014-10-28T03:56:19Z | NONE | null | Setting the label parameter in the call to df.plot alters the DataFrame's index name. For example,
```
df = pd.DataFrame(data=np.random.rand(5,3), index=np.arange(5), columns=['A', 'B', 'C'])
df.index.name='RowNumber'
df.plot(y='B', label='Test')
print df.index.name
Out[1]: Test
```
See a better example at [StackOverflow](http://stackoverflow.com/questions/26068598/specifying-y-array-and-plot-label-in-pandas-dataframe-plot-changes-the-dataframe).
[An answer in above link](http://stackoverflow.com/questions/26068598/specifying-y-array-and-plot-label-in-pandas-dataframe-plot-changes-the-dataframe/26070511#26070511) points problem to [here](https://github.com/pydata/pandas/blob/master/pandas/tools/plotting.py#L2278).
pandas version 0.14.1.
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| null | 4 | 2014-10-06T23:37:03Z | 2015-10-21T04:06:57Z | 2015-10-20T23:12:38Z | NONE | null | `DataFrame.from_csv` doesn't account for Windows' new lines. In the current implementation it returns a single row `DataFrame`, which is not accurate. The online suggested solution is to do something like: `mydata = csv.reader(open(mypath,'rU'))` but implementing within that method might be better.
cc: @ElDeveloper
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} | 4 | 2014-10-07T10:43:45Z | 2019-07-05T20:20:54Z | 2018-02-09T12:29:03Z | CONTRIBUTOR | null | I'm using the pandas.DataFrame.to_html() method to generate a table to embed into a a templated document which references the table via an id. It would be useful if pandas.DataFrame.to_html() supported a `table_id` parameter:
`df.to_html(table_id='myTable')`
to produce:
`<table border="1" class="dataframe" id='mytable'>`
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| null | 3 | 2014-10-07T15:28:14Z | 2014-10-07T18:17:23Z | 2014-10-07T15:32:04Z | CONTRIBUTOR | null | Missed a semi-colon, here's the fix.
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} | 3 | 2014-10-07T15:40:33Z | 2014-10-07T17:43:45Z | 2014-10-07T17:43:45Z | CONTRIBUTOR | null | Sorry for basically duplicate pr, noticed this one after.
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| null | 1 | 2014-10-07T16:28:32Z | 2014-10-07T16:29:49Z | 2014-10-07T16:29:38Z | NONE | null | Pandas 0.14.1
How to reproduce.
In the example below, the first item df['a'] = 1 is executed twice.
$ import pandas as pd
$ f = pd.DataFrame({'a':[1,2,3,1,1,2], 'b':[1,2,3,4,5,6]})
$ def fx(df):
print df
return df['b'].sum()
$ f.groupby('a').apply(fx)
a b
0 1 1
3 1 4
4 1 5
a b
0 1 1
3 1 4
4 1 5
a b
1 2 2
5 2 6
a b
2 3 3
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"type": "User",
"url": "https://api.github.com/users/jreback"
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"description": "after 0.14.1 of course!",
"due_on": "2014-10-19T07:00:00Z",
"html_url": "https://github.com/pandas-dev/pandas/milestone/28",
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"title": "0.15.0",
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} | 0 | 2014-10-07T16:31:32Z | 2014-10-07T21:52:42Z | 2014-10-07T21:52:42Z | CONTRIBUTOR | null | building using appveyor (windows CI)
closes #5561
https://ci.appveyor.com/project/jreback/pandas/build/1.0.455/job/2avadnw54tpv3lob
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} | https://api.github.com/repos/pandas-dev/pandas/issues/8500/timeline | null | 0 | {
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