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When I read csv data with a windows line terminator (\r\n), pandas creates extra rows in the dataframe ie it does not recognise the line terminator..i
test1.csv
Id,Description,Field1,Field2^M
1,"test data 1 ^M\n",2,3^M
2,"test data 2 ^M\n",3,4^M
In [80]: in_csv = 'test1.csv'
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closes #3480
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I am processing csv data with windows line terminators in some of the field data. The input data is properly quoted(with QUOTE_MINIMAL )
when writing the data using to_csv, pandas does not quote the fields with windows line terminators ie not using QUOTE_MINIMAL ?
test3.csv
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Series([1], Index(np.array([2**64-1],'u8'), 'u8')) + Series([1], Index(np.array([2**64-2],'u8'), 'u8'))
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- Fix assigning a new index to a duplicate index in a DataFrame would fail #3468
- Fix construction of a DataFrame with a duplicate index
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Signed-off-by: Kevin Stone kevinastone@gmail.com
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https://api.github.com/repos/pandas-dev/pandas/issues/3511 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3511/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3511/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3511/events | https://github.com/pandas-dev/pandas/pull/3511 | 13,877,944 | MDExOlB1bGxSZXF1ZXN0NTQ4NTk4MA== | 3,511 | ENH: add mode.mangle_dupe_cols option GH3468 | {
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"i... | 4 | 2013-05-02T05:34:41Z | 2014-06-24T13:24:56Z | 2013-05-05T09:14:08Z | NONE | null | #3468
as discussed, an added option to disable th mangling behavior
of dupe columns taken by pd.read_csv going back to olden days.
Not sure what the default should be, currently the default is compatible
with existing scripts.
Tested with both c and python parser engines.
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https://api.github.com/repos/pandas-dev/pandas/issues/3512 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3512/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3512/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3512/events | https://github.com/pandas-dev/pandas/issues/3512 | 13,886,226 | MDU6SXNzdWUxMzg4NjIyNg== | 3,512 | Change default value of dupe_mangle_cols in read-table/csv to False | {
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Update: there was no 0.11.1, it became 0.12.
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https://api.github.com/repos/pandas-dev/pandas/issues/3513 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3513/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3513/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3513/events | https://github.com/pandas-dev/pandas/issues/3513 | 13,894,370 | MDU6SXNzdWUxMzg5NDM3MA== | 3,513 | "wrong" covariance matrix returned in the presence of nans | {
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"f... | 8 | 2013-05-02T14:48:45Z | 2017-07-06T23:12:48Z | 2017-07-06T23:12:48Z | CONTRIBUTOR | null | See the mailing list "[pydata] Covariance matrix not positive semi-definite."
Currently, a covariance matrix is computed using pairwise available observations ie., if there is missing data at an index but not in the two pairs it still uses those pairs in the pairwise covariance matrix. The result of this computation i... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3514 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3514/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3514/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3514/events | https://github.com/pandas-dev/pandas/issues/3514 | 13,903,238 | MDU6SXNzdWUxMzkwMzIzOA== | 3,514 | Check whether google analytics results contain sampled data | {
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"f... | 1 | 2013-05-02T18:08:26Z | 2015-10-14T11:45:14Z | 2015-10-14T11:45:14Z | CONTRIBUTOR | null | Had a preview issue where the summary of returned results don't match the google analytics web portal results. This is probably why.
Need to look at the top level element for "containsSampledData".
https://developers.google.com/analytics/devguides/reporting/core/v3/reference#sampling
in pandas.io.ga.GDataReader.get_... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3515 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3515/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3515/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3515/events | https://github.com/pandas-dev/pandas/issues/3515 | 13,903,677 | MDU6SXNzdWUxMzkwMzY3Nw== | 3,515 | datetime64 is incorrectly converted by series.values | {
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Calling series.values on a datetime64 series will mess up the values, truncating them to 1970.
import pandas as pd
y = pd.DataFrame(pd.date_range('12/15/2012', '1/6/2013', freq='B'), columns=['dt'])
# Look at y, note the dates are all in 2012 and 2013. So far so good.
z = y.values
# Look at z -- uh... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3516 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3516/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3516/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3516/events | https://github.com/pandas-dev/pandas/pull/3516 | 13,904,938 | MDExOlB1bGxSZXF1ZXN0NTUwMDE2MA== | 3,516 | BUG/CLN: datetime64/timedelta64 | {
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"i... | 1 | 2013-05-02T18:46:25Z | 2014-06-16T01:29:27Z | 2013-05-08T21:51:09Z | CONTRIBUTOR | null | Various bugs related to datetime64s
- Properly convert np.datetime64 objects in a Series, #3416
This would convert to object dtype previously
```
In [1]: dates = [
...: np.datetime64(datetime.date(2013, 1, 1)),
...: np.datetime64(datetime.date(2013, 1, 2)),
...: np.datetime64(datetime.date(2013, ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3517 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3517/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3517/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3517/events | https://github.com/pandas-dev/pandas/pull/3517 | 13,908,335 | MDExOlB1bGxSZXF1ZXN0NTUwMjE1Mg== | 3,517 | TST: skip tests needing xlrd | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3518 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3518/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3518/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3518/events | https://github.com/pandas-dev/pandas/issues/3518 | 13,918,878 | MDU6SXNzdWUxMzkxODg3OA== | 3,518 | PDF for 0.11.0 is missing, points users to 0.10.1 version | {
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"i... | 3 | 2013-05-03T01:43:08Z | 2014-01-17T13:37:10Z | 2013-05-20T18:54:07Z | CONTRIBUTOR | null | The link to the pdf version here:
http://pandas.pydata.org/pandas-docs/stable/
point the user here:
http://pandas.pydata.org/pandas-docs/stable/pandas.pdf
Which is the 0.10.1 version of the docs, not the 0.11.0 version of the docs.
It should be updated so that the 0.11.0 pdf versions of the docs are available for P... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3519 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3519/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3519/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3519/events | https://github.com/pandas-dev/pandas/pull/3519 | 13,920,299 | MDExOlB1bGxSZXF1ZXN0NTUwODc5MA== | 3,519 | Parse raised exceptions and their error messages sans interpolation/format spec | {
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# Example
You c... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3520 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3520/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3520/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3520/events | https://github.com/pandas-dev/pandas/issues/3520 | 13,935,211 | MDU6SXNzdWUxMzkzNTIxMQ== | 3,520 | API/ENH: Add setter methods to vectorized string API | {
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"f... | 4 | 2013-05-03T14:00:13Z | 2013-08-05T01:45:54Z | 2013-08-05T01:45:54Z | MEMBER | null | This would be nice. The idea is simple: be able to modify the elements of vectorized string list or tuples, e.g., after splitting them, a la
``` python
s = Series(['a/b/c', 'd/e/f'])
sp = s.split('/')
sp.str[0] = 'x'
assert np.all(sp.str[0] == 'x')
# as well as
sp.str.set(0, 'y')
assert np.all(sp.str[0] == 'y')
```... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3521 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3521/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3521/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3521/events | https://github.com/pandas-dev/pandas/issues/3521 | 13,938,070 | MDU6SXNzdWUxMzkzODA3MA== | 3,521 | Inconsistent behavior of hierarchical indexes when indexes are of different data types | {
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import pandas as pd, pylab
#int index
col = pd.MultiIndex.from_tuples([('c1',0),('c1',1),('c2',0)],names=['f','s'])
dat = pylab.randn(2,len(col))
df1 = pd.DataFrame(dat, columns=col)
#Text index
col = pd.MultiIndex.from_tuples([('c3','x'),('c3','y')])
dat = pylab.randn(2,len(col))
df2 = pd.DataFrame(dat, c... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3522 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3522/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3522/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3522/events | https://github.com/pandas-dev/pandas/issues/3522 | 13,942,565 | MDU6SXNzdWUxMzk0MjU2NQ== | 3,522 | qt repr bug | {
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"html_url": "https://github.com/pandas-dev/pandas/milestone/22",
"i... | 1 | 2013-05-03T16:47:52Z | 2013-05-04T08:45:13Z | 2013-05-04T08:45:13Z | MEMBER | null | HTML is repr'd, not the symbols `<` and `>`:
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https://api.github.com/repos/pandas-dev/pandas/issues/3523 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3523/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3523/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3523/events | https://github.com/pandas-dev/pandas/issues/3523 | 13,947,560 | MDU6SXNzdWUxMzk0NzU2MA== | 3,523 | TypeError: object of type 'NoneType' has no len() with MySQL empty columns | {
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mysql.connector '1.0.9'
# Import statements and options
import mysql.connector
from mysql.connector import errorcode
import pandas.io.sql as sql
import pandas as pd
from pandas import DataFrame
## Error
Running this code against MySQL (view includes several completely blank columns):
``` python
#Quer... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3524 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3524/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3524/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3524/events | https://github.com/pandas-dev/pandas/issues/3524 | 13,949,123 | MDU6SXNzdWUxMzk0OTEyMw== | 3,524 | DatetimeIndex freq parameter is inconsistent when passing nanosecond frequencies | {
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``` python
import pandas as pd
import numpy as np
def create_ns_index(start, fs, nsamples, name='time'):
ns = int(1e9 / fs)
dtstart = np.datetime64(start)
dt = dtstart + np.arange(nsamples) * np.timedelta64(ns, 'ns')
freq = ns * pd.datetools.Nano()
return pd.DatetimeIndex(dt... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3525 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3525/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3525/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3525/events | https://github.com/pandas-dev/pandas/pull/3525 | 13,949,598 | MDExOlB1bGxSZXF1ZXN0NTUyMzg2Mw== | 3,525 | ENH: support for msgpack serialization/deserialization | {
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"html_url": "https://github.com/pandas-dev/pandas/milestone/22",
"i... | 42 | 2013-05-03T19:43:54Z | 2014-06-12T15:57:14Z | 2013-06-10T14:11:28Z | CONTRIBUTOR | null | warning: prototype!
msgpack serialization/deseriiization
- support all pandas objects: Timestamp,Period,all index types,Series,DataFrame,Panel,Sparse suite
- docs included (in io.rst)
- iterator support
- top-level api support
Here are 2 features which I think msgpack supports, but have to look further
- no support f... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3526 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3526/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3526/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3526/events | https://github.com/pandas-dev/pandas/issues/3526 | 13,949,853 | MDU6SXNzdWUxMzk0OTg1Mw== | 3,526 | fillna/fill_value fails when filling with a list | {
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In [48]: s = pd.Series([np.NaN, 'hello world'])
In [49]: s
Out[49]:
0 NaN
1 hello world
dtype: object
In [50]: s.fillna([])
Out[50]:
0 NaN
1 hello world
dtype: object
In [51]: s.fillna(['not empty'])
Out[51]:
0 not empty
1 hello world
dtype: object
```
Had a quick p... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3527 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3527/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3527/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3527/events | https://github.com/pandas-dev/pandas/pull/3527 | 13,957,385 | MDExOlB1bGxSZXF1ZXN0NTUyODMxNw== | 3,527 | fix qt repr bug | {
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"i... | 1 | 2013-05-03T23:59:41Z | 2014-07-03T08:22:45Z | 2013-05-04T00:22:02Z | MEMBER | null | addresses #3522.
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https://api.github.com/repos/pandas-dev/pandas/issues/3528 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3528/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3528/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3528/events | https://github.com/pandas-dev/pandas/issues/3528 | 13,971,826 | MDU6SXNzdWUxMzk3MTgyNg== | 3,528 | Resampling irregular timeseries | {
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"f... | 2 | 2013-05-04T23:27:26Z | 2013-11-19T17:06:29Z | 2013-11-19T17:06:29Z | NONE | null | I have some depth data from an echo-sounder in the following format
Date Time Depth
2013-00-28 00:00:38.652 2798.09
2013-00-28 00:00:48.632 2795.90
2013-00-28 00:00:58.388 2783.43
2013-00-28 00:01:08.160 2793.54
2013-00-28 00:01:17.517 2784.77
2013-00-28 00:01:27.289 2784.89
2013-00-28 00:01:37.860 2786.27
2013-00-28 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3529 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3529/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3529/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3529/events | https://github.com/pandas-dev/pandas/issues/3529 | 13,984,029 | MDU6SXNzdWUxMzk4NDAyOQ== | 3,529 | More flexible time series binning/resampling | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2013-05-05T22:29:45Z | 2017-03-16T12:27:47Z | 2017-03-16T12:27:47Z | MEMBER | null | e.g. http://stackoverflow.com/questions/16319106/whats-the-equivalent-of-cut-qcut-for-pandas-date-fields
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https://api.github.com/repos/pandas-dev/pandas/issues/3530 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3530/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3530/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3530/events | https://github.com/pandas-dev/pandas/pull/3530 | 13,985,743 | MDExOlB1bGxSZXF1ZXN0NTUzODI3NQ== | 3,530 | DOC: Fix header | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3531 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3531/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3531/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3531/events | https://github.com/pandas-dev/pandas/pull/3531 | 14,006,450 | MDExOlB1bGxSZXF1ZXN0NTU0NzY4Nw== | 3,531 | ENH: HDFStore enhancements | {
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"i... | 8 | 2013-05-06T15:35:58Z | 2014-06-17T13:08:16Z | 2013-05-08T20:26:10Z | CONTRIBUTOR | null | - will retain index attributes (freq,tz,name) on recreation #3499
- support datelike columns with a timezone as data_columns #2852
- will raise if trying to append with a different timezone than existing
will warn if the existing frequency of an index is different than an appended one
(thought raising was too strict)
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https://api.github.com/repos/pandas-dev/pandas/issues/3532 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3532/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3532/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3532/events | https://github.com/pandas-dev/pandas/issues/3532 | 14,008,059 | MDU6SXNzdWUxNDAwODA1OQ== | 3,532 | "can't compare datetime.date to str" in slicing/indexing (version 0.11.0) | {
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I don't know if this has been already fixed in developing 0.12, but just in case:
it seemed that indexing with datetime.date does not work properly.
E.g. the code
```
IDX=[datetime.strptime(yy,'%Y-%m-%d') for yy in ["2005-01-01","2006-01-01","2006-01-01","2007-01-01"]]
IDX
df = pd.DataFrame([1,2,3,4], ind... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3533 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3533/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3533/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3533/events | https://github.com/pandas-dev/pandas/pull/3533 | 14,015,884 | MDExOlB1bGxSZXF1ZXN0NTU1MjQwOQ== | 3,533 | BUG: Fixed bug in mixed frame assignment with aligned series (GH3492) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3534 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3534/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3534/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3534/events | https://github.com/pandas-dev/pandas/issues/3534 | 14,018,571 | MDU6SXNzdWUxNDAxODU3MQ== | 3,534 | doc: add doc/source/v0.11.1.txt to docs | {
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"i... | 2 | 2013-05-06T20:11:21Z | 2013-05-10T10:14:01Z | 2013-05-10T10:14:01Z | CONTRIBUTOR | null | When I go to
http://pandas.pydata.org/pandas-docs/dev/whatsnew.html
I would like to see the next release' changes included
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https://api.github.com/repos/pandas-dev/pandas/issues/3535 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3535/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3535/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3535/events | https://github.com/pandas-dev/pandas/issues/3535 | 14,018,624 | MDU6SXNzdWUxNDAxODYyNA== | 3,535 | doc: add read_html to io | {
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"i... | 1 | 2013-05-06T20:12:44Z | 2013-05-10T10:03:19Z | 2013-05-10T10:03:19Z | CONTRIBUTOR | null | add a link to
http://pandas.pydata.org/pandas-docs/dev/generated/pandas.io.html.read_html.html#pandas.io.html.read_html
on
http://pandas.pydata.org/pandas-docs/dev/io.html
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https://api.github.com/repos/pandas-dev/pandas/issues/3536 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3536/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3536/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3536/events | https://github.com/pandas-dev/pandas/pull/3536 | 14,019,927 | MDExOlB1bGxSZXF1ZXN0NTU1NDU4MQ== | 3,536 | Update io.rst | {
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"i... | 2 | 2013-05-06T20:40:46Z | 2013-05-10T10:03:07Z | 2013-05-10T10:02:36Z | CONTRIBUTOR | null | added io.read_html
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https://api.github.com/repos/pandas-dev/pandas/issues/3537 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3537/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3537/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3537/events | https://github.com/pandas-dev/pandas/pull/3537 | 14,024,879 | MDExOlB1bGxSZXF1ZXN0NTU1NzMzNw== | 3,537 | PERF: HDFStore table writing performance improvements | {
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"i... | 0 | 2013-05-06T22:38:17Z | 2014-07-16T08:07:27Z | 2013-05-08T21:44:20Z | CONTRIBUTOR | null | PERF: hdf performance by doing recarray translation in cython
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
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] | closed | false | null | [] | null | 1 | 2013-05-07T13:49:23Z | 2013-06-06T12:05:21Z | 2013-06-06T12:05:21Z | NONE | null | Hi, as heading states. Column of Nones could be dropped, but fillna(0) should change this. Using aggfunc=np.sum does not help. (all version 0.10.1)
In [160]: f = pd.DataFrame({'A' : ['one', 'two'] \* 3,
......: 'B' : ['x', 'y', 'z'] \* 2,
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"f... | 3 | 2013-05-07T16:48:48Z | 2021-04-11T02:00:17Z | 2021-04-11T02:00:17Z | CONTRIBUTOR | null | Along the lines of wes's answer to this question:
http://stackoverflow.com/questions/16384332/how-to-speed-up-pandas-row-filtering-by-string-matching
I think it is possible to have a dictionary recording certain parameters for a series (or a column in a frame), something like
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"i... | 3 | 2013-05-07T21:02:27Z | 2013-06-13T19:12:08Z | 2013-06-13T19:12:08Z | MEMBER | null | Was surprised at how difficult it was to convert a unix timestamp to a pandas Timestamp recently
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https://api.github.com/repos/pandas-dev/pandas/issues/3541 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3541/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3541/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3541/events | https://github.com/pandas-dev/pandas/issues/3541 | 14,073,106 | MDU6SXNzdWUxNDA3MzEwNg== | 3,541 | max_rows seems to have max value of 60 | {
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"i... | 18 | 2013-05-07T21:54:16Z | 2013-09-20T03:01:36Z | 2013-05-25T23:10:58Z | CONTRIBUTOR | null | Max_rows seems to have a maximum value of 60 (in 0.11 and dev, this works as expected in 0.10.1):
```
In [1]: %paste
from pandas import *
n = 100
foo = DataFrame(index=range(n))
foo['floats'] = np.random.randn(n)
set_option('display.max_rows', 101)
## -- End pasted text --
In [2]: foo.head(100)
Out[2]:
<class 'pand... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3542 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3542/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3542/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3542/events | https://github.com/pandas-dev/pandas/issues/3542 | 14,074,913 | MDU6SXNzdWUxNDA3NDkxMw== | 3,542 | Truncating long numbers when casting astype("string") or when printing | {
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```
b = pd.Series({"playerid": 544911367940993}, dtype='float64')
b['playerid'].astype('string')
```
This should output 544911367940993 or 544911367940993.0, but instead it prints '5.44911367941e+14'.
It seems this is because Pandas uses str(b['playerid']) instead of b['playerid'].**repr**()
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https://api.github.com/repos/pandas-dev/pandas/issues/3543 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3543/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3543/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3543/events | https://github.com/pandas-dev/pandas/pull/3543 | 14,079,171 | MDExOlB1bGxSZXF1ZXN0NTU4NDk4OA== | 3,543 | Document read_csv's usecols parameter. | {
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This patch fixes this:
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.parsers.read_csv.html
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https://api.github.com/repos/pandas-dev/pandas/issues/3544 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3544/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3544/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3544/events | https://github.com/pandas-dev/pandas/pull/3544 | 14,079,421 | MDExOlB1bGxSZXF1ZXN0NTU4NTExOA== | 3,544 | document read_csv's usecols argument. | {
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"i... | 3 | 2013-05-08T01:00:50Z | 2014-07-16T08:07:34Z | 2013-05-15T23:31:24Z | CONTRIBUTOR | null | read_csv's usecols is currently not documented here:
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.parsers.read_csv.html
This patch fixes that.
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https://api.github.com/repos/pandas-dev/pandas/issues/3545 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3545/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3545/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3545/events | https://github.com/pandas-dev/pandas/issues/3545 | 14,103,982 | MDU6SXNzdWUxNDEwMzk4Mg== | 3,545 | Floating point precision in DataFrame.read_csv | {
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How to reproduce:
```
Date, Close
2013-01-01, 25.56
```
It will read 25.26 as 25.559999999999999
Possibly duplicate of https://github.com/pydata/pandas/issues/2069
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"i... | 1 | 2013-05-08T16:01:40Z | 2013-06-14T15:36:07Z | 2013-05-08T18:00:28Z | CONTRIBUTOR | null | ```
>>> import pandas as pd
>>> pd.__version__
'0.12.0.dev'
>>> index = pd.date_range(start='2013-05-31 00:00', end='2013-05-31 23:00', freq='H')
>>> series = pd.Series(range(len(index)), index=index)
>>> print series.index.summary()
DatetimeIndex: 24 entries, 2013-05-31 00:00:00 to 2013-05-31 23:00:00
Freq: H
>>> prin... | {
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"i... | 5 | 2013-05-08T17:19:50Z | 2020-11-11T10:18:21Z | 2013-05-10T09:25:32Z | CONTRIBUTOR | null | Notice in the html below the second index column's 'a' field is given as `<th rowspan="2" valign="top">a</th>`, expanding over what should be an empty cell.
I actually found this using a pivot table, `margins=True`, as this creates a row keyed like `('All', '', '')`.
```
print df = pd.DataFrame({'c1': ['a', 'b'], 'c2... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3551 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3551/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3551/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3551/events | https://github.com/pandas-dev/pandas/issues/3551 | 14,150,210 | MDU6SXNzdWUxNDE1MDIxMA== | 3,551 | ENH: apply to generate Panels with returned Frames | {
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"description": "2014-02-3: previous version released",
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"labels_url": "htt... | 2 | 2013-05-09T15:11:36Z | 2014-02-15T21:15:44Z | 2014-02-15T21:15:44Z | CONTRIBUTOR | null | related is #1148 (the groupby)
sse also #3551
useful in a case where the returned arrity is > 1 (right now not implemented)
essentially
```
In [3]: df = DataFrame(randn(20,2),index=date_range('20130101',periods=20,freq='min'),
columns=list('AB'))
df.resample('15Min',how='ohlc')
NotImplementedError: ... | {
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"i... | 3 | 2013-05-09T16:04:20Z | 2014-06-19T15:13:37Z | 2013-05-09T19:18:31Z | NONE | null | possibly related to #3041 but still present in 0.11 and current HEAD (23f6058bd6249c21b5db95530a703fa4bc4c2510)
``` python
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: df1 = pd.DataFrame([[np.nan, 3.,True], [-4.6, np.nan, True], [np.nan, 7., False]])
In [4]: df2 = pd.DataFrame([[-42.6, np.nan, Tr... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3553 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3553/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3553/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3553/events | https://github.com/pandas-dev/pandas/issues/3553 | 14,154,213 | MDU6SXNzdWUxNDE1NDIxMw== | 3,553 | to_html fails for MultiIndexes with empty strings and display.multi_sparse == False | {
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"i... | 0 | 2013-05-09T16:39:42Z | 2013-05-10T09:07:38Z | 2013-05-10T09:07:15Z | MEMBER | null | Piggybacking off of #3547.
``` python
import pandas as pd
from nose.tools import assert_raises
pd.set_option('display.multi_sparse', False)
df = pd.DataFrame({'c1': ['a', 'b'], 'c2': ['a', ''], 'data': [1, 2]}).set_index(['c1', 'c2'])
assert_raises(TypeError, df.to_html) # True
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3555 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3555/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3555/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3555/events | https://github.com/pandas-dev/pandas/pull/3555 | 14,159,716 | MDExOlB1bGxSZXF1ZXN0NTYyNjI4Ng== | 3,555 | BUG: combine_first not returning the same dtype in cases where it can (GH3552) | {
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ENH: `convert_objects` now accepts a `copy` parameter (defaults to `True`)
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"i... | 6 | 2013-05-09T20:42:22Z | 2013-05-10T20:09:51Z | 2013-05-10T20:09:51Z | NONE | null | This code used to work some time around version 0.10, but now generates a ValueError:
In [14]: y
Out[14]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 3 (major_axis) x 2 (minor_axis)
Items axis: z1 to z2
Major_axis axis: 0 to 2
Minor_axis axis: a to b
In [15]: y.transpose('minor', 'major', 'items')
Out[... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3557 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3557/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3557/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3557/events | https://github.com/pandas-dev/pandas/pull/3557 | 14,169,368 | MDExOlB1bGxSZXF1ZXN0NTYzMTc4MA== | 3,557 | fix TypeError for display.multi_sparse == False | {
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"i... | 1 | 2013-05-09T22:25:23Z | 2014-07-16T08:08:02Z | 2013-05-10T09:05:54Z | MEMBER | null | fixes #3553.
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https://api.github.com/repos/pandas-dev/pandas/issues/3558 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3558/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3558/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3558/events | https://github.com/pandas-dev/pandas/pull/3558 | 14,172,024 | MDExOlB1bGxSZXF1ZXN0NTYzMzM0NQ== | 3,558 | fix panel tranpose arguments api bug | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3559 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3559/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3559/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3559/events | https://github.com/pandas-dev/pandas/pull/3559 | 14,173,885 | MDExOlB1bGxSZXF1ZXN0NTYzMzg5Nw== | 3,559 | ENH: raise useful error message on invalid concat arguments | {
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"i... | 0 | 2013-05-10T00:38:57Z | 2014-06-26T17:46:41Z | 2013-05-10T10:43:07Z | MEMBER | null | fixes #3481.
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"i... | 1 | 2013-05-10T01:22:14Z | 2014-06-23T18:20:54Z | 2013-05-10T09:29:37Z | MEMBER | null | addresses #3460.
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https://api.github.com/repos/pandas-dev/pandas/issues/3561 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3561/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3561/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3561/events | https://github.com/pandas-dev/pandas/issues/3561 | 14,180,924 | MDU6SXNzdWUxNDE4MDkyNA== | 3,561 | DataFrame.ix losing row ordering when index has duplicates | {
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"i... | 5 | 2013-05-10T07:18:40Z | 2013-05-14T21:44:57Z | 2013-05-14T21:44:57Z | CONTRIBUTOR | null | ``` python
import pandas as pd
ind = ['A', 'A', 'B', 'C']i
df = pd.DataFrame({'test':range(len(ind))}, index=ind)
rows = ['C', 'B']
res = df.ix[rows]
assert rows == list(res.index) # fails
```
The problem is that the resulting DataFrame keeps the ordering of the `df.index` and not the `rows` key. You'll notice that ... | {
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"i... | 3 | 2013-05-10T14:32:01Z | 2013-05-10T16:29:24Z | 2013-05-10T16:04:38Z | NONE | null | carried over from #3487
``` python
DataFrame.from_records([], columns=['a','b','c'])
Empty DataFrame
Columns: [a, b, c]
Index: []
```
vs.
``` python
DataFrame.from_records([], columns=['a','b','b'])
AssertionError: Number of manager items must equal union of block items
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3563 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3563/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3563/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3563/events | https://github.com/pandas-dev/pandas/pull/3563 | 14,193,856 | MDExOlB1bGxSZXF1ZXN0NTY0Mzk2MA== | 3,563 | BUG: (GH3561) non-unique indexers with a list-like now return in the same order as the passed values | {
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"i... | 1 | 2013-05-10T14:42:27Z | 2014-07-10T09:20:30Z | 2013-05-14T21:44:58Z | CONTRIBUTOR | null | close #3561
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https://api.github.com/repos/pandas-dev/pandas/issues/3564 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3564/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3564/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3564/events | https://github.com/pandas-dev/pandas/pull/3564 | 14,194,600 | MDExOlB1bGxSZXF1ZXN0NTY0NDM2Nw== | 3,564 | BUG: Duplicate indexes with and empty DataFrame.from_records will return a correct frame (GH3562) | {
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"i... | 0 | 2013-05-10T15:00:17Z | 2014-06-29T09:51:40Z | 2013-05-10T16:01:33Z | CONTRIBUTOR | null | closes #3562
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https://api.github.com/repos/pandas-dev/pandas/issues/3565 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3565/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3565/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3565/events | https://github.com/pandas-dev/pandas/pull/3565 | 14,195,950 | MDExOlB1bGxSZXF1ZXN0NTY0NTA4OA== | 3,565 | DOC: add bs4/lxml install note to README.rst | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3566 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3566/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3566/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3566/events | https://github.com/pandas-dev/pandas/issues/3566 | 14,201,003 | MDU6SXNzdWUxNDIwMTAwMw== | 3,566 | iterrows changes dtype of columns | {
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"i... | 10 | 2013-05-10T17:33:05Z | 2013-05-13T14:46:38Z | 2013-05-13T12:36:28Z | NONE | null | ```
In [105]: df = pd.DataFrame([[1, 1.0]], columns=['x', 'y'])
In [112]: row = next(df.iterrows())[1]
In [113]: row
Out[113]:
x 1
y 1
Name: 0, dtype: float64
In [114]: row['x'].dtype
Out[114]: dtype('float64')
In [115]: df['x'].dtype
Out[115]: dtype('int64')
```
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"followers_url": "https://api.github.com/users/wesm... | 1 | 2013-05-10T20:09:49Z | 2013-06-24T15:59:04Z | 2013-06-24T15:59:04Z | MEMBER | null | This is so minor I almost didn't want to open this issue, but here it is. The qt repr seems to output the repr of a frame larger than if you actually call `print(repr(frame))`. Screen shot:
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https://api.github.com/repos/pandas-dev/pandas/issues/3568 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3568/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3568/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3568/events | https://github.com/pandas-dev/pandas/issues/3568 | 14,212,806 | MDU6SXNzdWUxNDIxMjgwNg== | 3,568 | repr of large DatetimeIndex objects is too slow | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3569 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3569/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3569/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3569/events | https://github.com/pandas-dev/pandas/pull/3569 | 14,213,396 | MDExOlB1bGxSZXF1ZXN0NTY1NDc5NA== | 3,569 | DOC: document non-preservation of dtypes across rows with iterrows | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3570 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3570/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3570/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3570/events | https://github.com/pandas-dev/pandas/issues/3570 | 14,213,724 | MDU6SXNzdWUxNDIxMzcyNA== | 3,570 | Panel.fillna() loses index names | {
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"created_at": "2015-02-26T19:29:05Z",
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"followers_url": "https://api.github.com/users/jorisva... | 2 | 2013-05-10T23:23:05Z | 2018-07-06T20:20:59Z | 2017-07-11T09:54:17Z | NONE | null | ```
Panel.fillna()
```
should accept
```
(axis= [0, 1, 2] or ['items', 'major_axis', 'minor_axis'])
```
as an argument.
Right now it essentially defaults to
```
(axis='major_axis')
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3571 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3571/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3571/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3571/events | https://github.com/pandas-dev/pandas/issues/3571 | 14,214,356 | MDU6SXNzdWUxNDIxNDM1Ng== | 3,571 | Improve Reading and Writing of Multi-Index Columns | {
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"i... | 8 | 2013-05-10T23:53:17Z | 2013-05-19T14:41:57Z | 2013-05-19T14:41:57Z | NONE | null | link to #1651 (to_csv) and #3141 (read_csv)
Currently (0.11) when the read_csv and to_csv methods handle multi-index row labels fine, but don't do as well with multi-index column labels. For the column index the to_csv method writes them out as a tuple into the 1st row of the CSV file. This reads back in as a tuple.... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3572 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3572/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3572/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3572/events | https://github.com/pandas-dev/pandas/pull/3572 | 14,214,574 | MDExOlB1bGxSZXF1ZXN0NTY1NTQ3OQ== | 3,572 | ENH: plot only numeric data and raise an exception *before* plotting if there is no numeric data | {
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"i... | 29 | 2013-05-11T00:06:40Z | 2014-06-13T08:15:50Z | 2013-05-21T21:01:11Z | MEMBER | null | Raise a `TypeError` alerting the user to the fact that they are trying to plot nonnumeric data, or if there are any numeric data plot those. closes #1818.
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https://api.github.com/repos/pandas-dev/pandas/issues/3573 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3573/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3573/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3573/events | https://github.com/pandas-dev/pandas/issues/3573 | 14,214,776 | MDU6SXNzdWUxNDIxNDc3Ng== | 3,573 | Unintuitive default behavior with wide DataFrames in the IPython notebook | {
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"i... | 8 | 2013-05-11T00:16:57Z | 2013-09-20T03:01:36Z | 2013-05-25T23:25:04Z | MEMBER | null | In the IPython notebook, HTML output it the default and whether summary view is displayed should not be governed by hypothetical line width. I ran into this problem in a demo recently and it took me a minute to figure out what was wrong, definitely a bad change in 0.11.
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https://api.github.com/repos/pandas-dev/pandas/issues/3574 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3574/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3574/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3574/events | https://github.com/pandas-dev/pandas/issues/3574 | 14,215,152 | MDU6SXNzdWUxNDIxNTE1Mg== | 3,574 | DOC: docstring for min max should point out idxmax and idxmin | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3575 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3575/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3575/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3575/events | https://github.com/pandas-dev/pandas/pull/3575 | 14,217,257 | MDExOlB1bGxSZXF1ZXN0NTY1NjcxMw== | 3,575 | ENH: allow to_csv to write multi-index columns, read_csv to read with header=list arg | {
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"i... | 32 | 2013-05-11T04:08:31Z | 2014-06-12T12:04:57Z | 2013-05-19T14:22:04Z | CONTRIBUTOR | null | In theory should close:
#3571, #1651, #3141
Works, but a couple of issues/caveats:
- ~~index_col needs to be specified as an integer list (can be fixed)~~
- header is a list of rows to read that contain the multi-index,
a row that is skipped (e.g. [0,1,3,5], will just be skipped, like a comment)
- the writing forma... | {
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] | closed | false | null | [] | null | 4 | 2013-05-11T05:00:30Z | 2013-06-06T00:43:12Z | 2013-06-06T00:43:12Z | NONE | null | Using Pandas 0.11.0
A DataFrame with MultiIndex:
df = df.reorder_levels() drops the dataframe name attribute.
thus, df.name after the reorder_levels() has no attribute 'name'
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https://api.github.com/repos/pandas-dev/pandas/issues/3577 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3577/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3577/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3577/events | https://github.com/pandas-dev/pandas/issues/3577 | 14,220,503 | MDU6SXNzdWUxNDIyMDUwMw== | 3,577 | HDFStore complaining about 'timedelta64[ns]' | {
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```
import datetime as dt
import pandas as pd
s = pd.Series(pd.date_range('2012-1-1', periods=3, freq='D'))
td = pd.Series([ dt.timedelta(days=i) for i in range(3) ])
df = pd.DataFrame(dict(A = s, B = td))
print df
print df.dtypes
store = pd.HDFStor... | {
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I'm having an issue drawing a Pandas boxplot within a subplot. Based on the two ways I'm trying, creating the boxplot either removes all the subplots that I've already created, or plots the boxplot after the subplot grid. But I can't seem to draw it within the subplot grid.
import matplotlib.pyplot a... | {
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"i... | 4 | 2013-05-11T19:27:40Z | 2013-05-13T18:56:24Z | 2013-05-13T18:56:24Z | CONTRIBUTOR | null | I have found this while working with period-indexed data frames on Windows XP 32 bits:
```
import numpy as np
import pandas as pd
index = pd.period_range(start='1999-01', periods=5, freq='M')
s1 = pd.Series(np.random.rand(len(index)), index=index)
s2 = pd.Series(np.random.rand(len(index)), index=index)
series = [('s1'... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3580 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3580/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3580/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3580/events | https://github.com/pandas-dev/pandas/pull/3580 | 14,227,081 | MDExOlB1bGxSZXF1ZXN0NTY2MDI1OA== | 3,580 | DOC: add mention of idx* methods in max/min methods of Series/DataFrame | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3581 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3581/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3581/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3581/events | https://github.com/pandas-dev/pandas/issues/3581 | 14,227,106 | MDU6SXNzdWUxNDIyNzEwNg== | 3,581 | ENH: Add panel implementation of idxmin/max | {
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"followers_url": "https://api.github.com/users/jorisva... | 3 | 2013-05-11T21:17:37Z | 2018-07-06T20:21:00Z | 2016-09-29T22:31:03Z | MEMBER | null | Is this something that is worth pursuing? Personally, I don't use Panel really ever since DataFrame is basically N-dimensional, but this might be good for API consistency.
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https://api.github.com/repos/pandas-dev/pandas/issues/3582 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3582/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3582/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3582/events | https://github.com/pandas-dev/pandas/issues/3582 | 14,227,694 | MDU6SXNzdWUxNDIyNzY5NA== | 3,582 | Surprising behavior of DataFrame.replace | {
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"closed_at": "2013-12-30T19:53:14Z",
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``` python
>>> from string import ascii_lowercase as letters
>>> from pandas import DataFrame
>>> df = DataFrame(list(letters[:4]), columns=['a'])
>>> df
a
0 a
1 b
2 c
3 d
>>> df.replace({'a': 'b'})
a
0 a
1 a
2 c
3 d
>>> df.replace({'a': 'c'})
a
0 a
1 b
2 b
3 d
```
Do... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3583 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3583/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3583/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3583/events | https://github.com/pandas-dev/pandas/pull/3583 | 14,229,448 | MDExOlB1bGxSZXF1ZXN0NTY2MTA4MQ== | 3,583 | Moving pandasjson back into mainline pandas | {
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"i... | 6 | 2013-05-12T01:36:52Z | 2014-06-26T17:48:41Z | 2013-06-11T19:27:46Z | MEMBER | null | This extension works perfectly fine on Python 2.x and all platforms to my knowledge except win32 on MinGW (it's fine with VC2008+ AFAIK). It might have problems on some of @yarikoptic's esoteric Debian platforms, we should identify which ones those are and disable the extension in the setup.py until we have a chance to... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3584 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3584/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3584/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3584/events | https://github.com/pandas-dev/pandas/pull/3584 | 14,244,648 | MDExOlB1bGxSZXF1ZXN0NTY2NjUxOA== | 3,584 | ENH: add regex functionality to DataFrame.replace | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3585 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3585/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3585/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3585/events | https://github.com/pandas-dev/pandas/pull/3585 | 14,246,256 | MDExOlB1bGxSZXF1ZXN0NTY2NzI5Nw== | 3,585 | raise on fillna passed a list or tuple | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3586 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3586/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3586/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3586/events | https://github.com/pandas-dev/pandas/issues/3586 | 14,254,961 | MDU6SXNzdWUxNDI1NDk2MQ== | 3,586 | Trouble with NaNs: set_index().reset_index() corrupts data | {
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"i... | 8 | 2013-05-13T10:51:23Z | 2013-05-31T13:14:17Z | 2013-05-13T17:35:52Z | NONE | null | In the following code, my data are corrupted by the sequence set_index().reset_index(). The value of "QC" is actually changed to 1 from NaN where it should be NaN.
Btw, for symmetry I added the ".reset_index()", but the data corruption is introduced by set_index.
This problem exists in both version '0.10.1' and '0.11... | {
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"i... | 0 | 2013-05-13T13:29:29Z | 2014-06-29T12:28:29Z | 2013-05-13T17:35:20Z | CONTRIBUTOR | null | closes #3586
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https://api.github.com/repos/pandas-dev/pandas/issues/3588 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3588/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3588/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3588/events | https://github.com/pandas-dev/pandas/issues/3588 | 14,261,449 | MDU6SXNzdWUxNDI2MTQ0OQ== | 3,588 | Pivot NaN Bug | {
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"i... | 5 | 2013-05-13T13:57:24Z | 2014-06-17T11:23:40Z | 2013-05-19T16:55:29Z | NONE | null | The 'pivot' function does not properly handle NaNs. E.g.:
```
import numpy as np, pandas as pd
test = pd.DataFrame({"a":['R1', 'R2', 'R3', 'R4'], "b":["C1", "C3", np.nan , "C4"], "c":[10, 15, np.nan , 20]})
test.head(2).pivot('a', 'b', 'c')
b C1 C3
a
R1 10 NaN
R2 NaN 15
```
(No NaNs, works properly)
But:
```
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"f... | 1 | 2013-05-13T15:10:54Z | 2013-12-04T00:46:48Z | 2013-06-24T15:57:43Z | MEMBER | null | A script to automate some of the parts of updating docs when you add a feature would be great. @lodrago had a great idea regarding a gh issue sphinx extension.
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https://api.github.com/repos/pandas-dev/pandas/issues/3590 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3590/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3590/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3590/events | https://github.com/pandas-dev/pandas/issues/3590 | 14,266,259 | MDU6SXNzdWUxNDI2NjI1OQ== | 3,590 | Pandas mod 0 should not give 0 | {
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"i... | 4 | 2013-05-13T15:32:17Z | 2013-06-26T00:04:19Z | 2013-05-14T21:48:10Z | NONE | null | In python:
3 % 0
is undefined and returns "ZeroDivisionError: integer division or modulo by zero"
But in pandas 0.11, that gives 0. I believe that is incorrect. It ought to give NaN or Inf
p = pandas.DataFrame({ 'first' : [3,4,5,8], 'second' : [0,0,0,3] })
p['mod'] = p['first'] % p['second']
p
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https://api.github.com/repos/pandas-dev/pandas/issues/3591 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3591/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3591/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3591/events | https://github.com/pandas-dev/pandas/pull/3591 | 14,273,377 | MDExOlB1bGxSZXF1ZXN0NTY4MTAzMg== | 3,591 | BUG: fix take platorm issue with PeriodIndex (GH3579) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3592 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3592/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3592/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3592/events | https://github.com/pandas-dev/pandas/issues/3592 | 14,276,958 | MDU6SXNzdWUxNDI3Njk1OA== | 3,592 | resample with period='m' does not work on localized indexes for multiple methods in "how" | {
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"gists_url":... | [] | closed | false | null | [] | null | 1 | 2013-05-13T19:18:58Z | 2014-02-18T15:18:06Z | 2013-05-13T19:26:22Z | CONTRIBUTOR | null | For the following code:
```
rng = pd.date_range('1/1/2011', periods=200, freq='D')
rng = rng.tz_localize('UTC')
ts = pd.Series(cumsum(randn(len(rng))), index=rng)
```
the following resample:
`ts.resample('m', how='mean')`
works fine, but this one:
`ts.resample('m', how={'Mean': 'mean', 'Std': 'std'})`
fails with the ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3593 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3593/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3593/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3593/events | https://github.com/pandas-dev/pandas/issues/3593 | 14,278,920 | MDU6SXNzdWUxNDI3ODkyMA== | 3,593 | Unwanted conversion of `Timestamp` to `long` | {
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"i... | 5 | 2013-05-13T20:03:25Z | 2013-05-14T04:07:41Z | 2013-05-13T22:50:08Z | NONE | null | Method `combine_first` performs an unwanted conversion of `Timestamp` to `long`. Pandas version is "0.11.0". Here is an IPython session demonstrating the behavior, which is IMHO a bug:
```
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.11.0'
In [4]: from datetime import datetime
In [5]: df0 = pd.Data... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3594 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3594/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3594/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3594/events | https://github.com/pandas-dev/pandas/issues/3594 | 14,282,354 | MDU6SXNzdWUxNDI4MjM1NA== | 3,594 | read_fwf(), read_table() are mangling columns | {
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"i... | 2 | 2013-05-13T21:17:30Z | 2013-05-14T10:51:38Z | 2013-05-14T10:51:38Z | NONE | null | version 0.11.0 has introduced a confusing behavior when importing data via read_fwf (and, I'm pretty sure, read_table)
<PRE>
import pandas as pd
from cStringIO import StringIO
from datetime import datetime
tzlist = [1,10,20,30,60,80,100]
ntz = len(tzlist)
tcolspecs = [16]+[8]*ntz
tcolnames = ['SST'] + ["T%03d" % z fo... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3595 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3595/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3595/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3595/events | https://github.com/pandas-dev/pandas/pull/3595 | 14,284,463 | MDExOlB1bGxSZXF1ZXN0NTY4NzIyOA== | 3,595 | BUG: (GH3593) fixed a bug in the incorrect conversion of datetime64[ns] in combine_first | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3596 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3596/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3596/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3596/events | https://github.com/pandas-dev/pandas/issues/3596 | 14,286,266 | MDU6SXNzdWUxNDI4NjI2Ng== | 3,596 | DataFrame.groupby.count sometimes returns Series instead of DataFrame | {
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"i... | 3 | 2013-05-13T23:00:24Z | 2013-05-15T20:19:46Z | 2013-05-15T20:19:46Z | NONE | null | In version 0.11.0
``` python
A = DataFrame([[1,1],[2,1]],columns=['X','Y']).groupby('X').count()
```
returns a DataFrame, when there are 2+ grouping levels...
but,
``` python
B = DataFrame([[1,1],[1,1]],columns=['X','Y']).groupby('X').count()
```
returns a Series, when there is only 1 grouping level.
In version ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3597 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3597/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3597/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3597/events | https://github.com/pandas-dev/pandas/pull/3597 | 14,288,892 | MDExOlB1bGxSZXF1ZXN0NTY4OTU4MQ== | 3,597 | BUG: Fixed bug where a time-series was being selected in preference to an actual column name | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3598 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3598/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3598/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3598/events | https://github.com/pandas-dev/pandas/issues/3598 | 14,297,852 | MDU6SXNzdWUxNDI5Nzg1Mg== | 3,598 | Make secondary_y not being ignored in bar charts | {
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"i... | 1 | 2013-05-14T07:58:11Z | 2013-07-28T05:43:36Z | 2013-06-26T13:38:50Z | NONE | null | If I want to plot 2 series ('A' and 'B') with different y-scales using a **different y-axis** for each one of them, I can use `secondary_y`:
```
df = pd.DataFrame(np.random.uniform(size=10).reshape(5,2),columns=['A','B'])
df['A'] = df['A'] * 100
df.plot(secondary_y=['A'])
```
but if I want to create bar graphs, the e... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3599 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3599/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3599/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3599/events | https://github.com/pandas-dev/pandas/pull/3599 | 14,306,013 | MDExOlB1bGxSZXF1ZXN0NTY5ODAxNA== | 3,599 | BUG: Add squeeze keyword to groupby to allow reduction in returned type | {
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"html_url": "https://github.com/pandas-dev/pandas/milestone/22",
"i... | 4 | 2013-05-14T11:54:01Z | 2014-07-06T08:10:53Z | 2013-05-15T20:19:14Z | CONTRIBUTOR | null | This will allow a reduction in the returned type from DataFrame -> Series
if the groups are unique.
This is a fix for a regression from 0.10.1.
Allows functionaility in #2893, by specifying `squeeze=True`
in groupby call. #3596 functionaility is back as the default
This returns a Series because we are passing `sque... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3600 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3600/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3600/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3600/events | https://github.com/pandas-dev/pandas/pull/3600 | 14,312,284 | MDExOlB1bGxSZXF1ZXN0NTcwMTMyNQ== | 3,600 | BUG: Fix integer modulo and division to make integer and float dtypes work similarly for invalid values | {
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"closed_at": "2013-07-24T23:25:44Z",
"closed_issues": 410,
"created_at": "2013-04-30T04:12:30Z",
"creator": null,
"description": "Major release (formerly 0.11.1 bugfix but has outgrown its britches)",
"due_on": "2013-07-05T07:00:00Z",
"html_url": "https://github.com/pandas-dev/pandas/milestone/22",
"i... | 1 | 2013-05-14T14:28:08Z | 2014-07-16T08:09:04Z | 2013-05-14T21:48:10Z | CONTRIBUTOR | null | closes #3590
This is a numpy oddity that treats them differently.
```
In [131]: p = DataFrame({ 'first' : [3,4,5,8], 'second' : [0,0,0,3] })
In [132]: p % 0
Out[132]:
first second
0 NaN NaN
1 NaN NaN
2 NaN NaN
3 NaN NaN
In [133]: p % p
Out[133]:
first second
0 0 NaN
1 ... | {
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