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https://api.github.com/repos/pandas-dev/pandas/issues/3901 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3901/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3901/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3901/events | https://github.com/pandas-dev/pandas/issues/3901 | 15,549,926 | MDU6SXNzdWUxNTU0OTkyNg== | 3,901 | Pandas: AttributeError: 'numpy.ndarray' object has no attribute 'start' | {
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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from datetime import time
data = pd.read_csv('/temp/zondata/pvlog.csv', delimiter=';', parse_dates=True, index_col=1)
Gewenst = data['T_Sanyo_Open']
```
Next 2 examples(in Ipython console) gives no errors, and gives a result like the table at the bottom of this question:
``` python
Gewenst['2010']
Gewenst['2010-09']
```
Example below gives me previously described error when I want a specific bounded range of data:
``` python
Gewenst['2010-9':'2010-10']
```
I found a solution when I add this rule of code:
``` python
Gewenst = Gewenst.resample('1Min', fill_method='ffill')
```
But I don't want to resample the data.
table:
``` python
2010-08-31 12:36:53 30.37
2010-08-31 12:45:08 28.03
2010-08-31 12:55:09 25.16
2010-08-31 13:00:09 23.28
2010-08-31 13:05:09 22.37
2010-08-31 13:10:09 21.84
2010-08-31 13:15:08 22.19
2010-08-31 13:20:09 22.41
2010-08-31 13:25:09 23.16
2010-08-31 13:35:09 23.59
2010-08-31 13:40:09 26.75
2010-08-31 13:45:09 29.47
2010-08-31 13:50:10 33.13
2010-08-31 13:55:08 35.91
2010-08-31 14:00:08 37.78
...
2013-06-07 01:35:10 40.00
2013-06-07 01:40:10 40.00
2013-06-07 01:45:10 39.50
2013-06-07 01:50:12 39.75
2013-06-07 01:55:10 39.25
2013-06-07 02:00:10 39.50
2013-06-07 02:05:11 39.25
2013-06-07 02:10:11 39.25
2013-06-07 02:15:10 38.75
2013-06-07 02:20:11 38.75
2013-06-07 02:25:11 38.75
2013-06-07 02:30:10 39.25
2013-06-07 02:40:10 39.25
2013-06-07 02:45:10 39.00
2013-06-07 02:50:10 39.25
```
Does anyone have solution, or is this a bug in Pandas?
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} | 25 | "2013-06-14T10:17:04Z" | "2014-06-14T06:16:44Z" | "2013-07-02T14:39:10Z" | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/3863 by adding valid min and max fields to the Timestamp class.
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} | 4 | "2013-06-14T10:30:26Z" | "2014-07-16T08:14:08Z" | "2013-06-18T23:29:05Z" | CONTRIBUTOR | null | Numpy is a setup-time dependency due to some .h files, but is not declared as such. This causes tools like buildout ( http://buildout.org/ ) to fail.
Adding the `setup_requires` kwarg to the setuptools config shoud fix this: buildout will correctly assemble any declared setup-time dependencies, before executing the pandas build.
closes #3861
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} | 1 | "2013-06-14T13:12:33Z" | "2013-09-22T17:07:05Z" | "2013-09-22T17:07:05Z" | CONTRIBUTOR | null | from the ML
https://groups.google.com/forum/?fromgroups#!topic/pydata/Lc10-vz4NpU
not really sure if this is a bug, more of a convention
```
In [58]: data = "YEAR,DOY,TEMP\n2001,1,10.5\n2001,2,11.8\n2001,3,67.4"
In [59]: def date_converter(x):
return dt.datetime.strptime(x, '%Y %j')
In [60]: pandas.read_csv(StringIO(data), parse_dates=[[0,1]], index_col=0, sep=",", keep_date_col=True, date_parser=date_converter)
Out[60]:
YEAR DOY TEMP
YEAR_DOY
2001-01-01 2001 1 10.5
2001-01-02 2001 2 11.8
2001-01-03 2001 3 67.4
In [61]: data = "YEAR,DOY,TEMP\n2001.0,1.0,10.5\n2001.0,2.0,11.8\n2001.0,3,67.4"
In [62]: def date_converter2(*args):
return dt.datetime(int(float(args[0])),int(float(args[1])),1)
....:
In [63]: pandas.read_csv(StringIO(data), parse_dates=[[0,1]], index_col=0, sep=",", keep_date_col=True, date_parser=date_converter2)
Out[63]:
YEAR DOY TEMP
YEAR_DOY
2001-01-01 2001.0 1.0 10.5
2001-02-01 2001.0 2.0 11.8
2001-03-01 2001.0 3 67.4
```
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} | 13 | "2013-06-14T14:50:11Z" | "2014-04-23T23:15:47Z" | "2013-07-01T15:43:48Z" | CONTRIBUTOR | null | ...when the hour is not zero.
Example below:
``` python
In [7]: series = pd.TimeSeries(numpy.arange(5), pd.date_range('2014-01-01 09:00', periods=5, freq='D'))
...: series
...:
Out[7]:
2014-01-01 09:00:00 0
2014-01-02 09:00:00 1
2014-01-03 09:00:00 2
2014-01-04 09:00:00 3
2014-01-05 09:00:00 4
Freq: D, dtype: int32
In [8]: series[pd.datetime(2014, 1, 2):pd.datetime(2014, 1, 4)]
Out[8]:
2014-01-02 09:00:00 1
2014-01-03 09:00:00 2
Freq: D, dtype: int32
In [9]: series['02-Jan-2014':'04-Jan-2014']
Out[9]:
2014-01-02 09:00:00 1
2014-01-03 09:00:00 2
2014-01-04 09:00:00 3 <------------------------- ???
Freq: D, dtype: int32
In [10]:
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3906 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3906/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3906/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3906/events | https://github.com/pandas-dev/pandas/pull/3906 | 15,572,136 | MDExOlB1bGxSZXF1ZXN0NjMzMzUxOQ== | 3,906 | TST: convert knowntestfailures to skip tests | {
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} | 0 | "2013-06-14T18:41:53Z" | "2014-06-18T17:41:16Z" | "2013-06-14T19:07:45Z" | CONTRIBUTOR | null | in test_pickle, test_stats
closes #3894, #3896
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} | 27 | "2013-06-14T18:46:53Z" | "2016-10-12T23:03:53Z" | "2013-06-15T12:34:13Z" | CONTRIBUTOR | null | I believe `replace` should do a `convert_objects(copy=False)` after replacement to provide dtype soft-conversion
```
In [1]: df = DataFrame([['foo','bar','bah'],['bar','foo','bah']])
In [2]: df
Out[2]:
0 1 2
0 foo bar bah
1 bar foo bah
In [3]: m = { 'foo' : 1, 'bar' : 2, 'bah' : 3 }
In [5]: df.replace(m)
Out[5]:
0 1 2
0 1 3 2
1 2 1 3
In [6]: df.replace(m).dtypes
Out[6]:
0 object
1 object
2 object
dtype: object
In [8]: df.replace(m).convert_objects().dtypes
Out[8]:
0 int64
1 int64
2 int64
dtype: object
```
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} | 3 | "2013-06-14T21:41:38Z" | "2014-05-30T01:16:10Z" | "2014-05-30T01:16:10Z" | NONE | null | Hi,
I have a python script that runs on a daily basis and I noticed a problem this morning with the ga.read function. Nothing has changed with my GA account and I have not touched the python script. I've attached the error message below:
``` python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/Library/Python/2.7/site-packages/ipython-0.13.1-py2.7.egg/IPython/utils/py3compat.pyc in execfile(fname, *where)
176 else:
177 filename = fname
--> 178 __builtin__.execfile(filename, *where)
/Users/danielcollins/Documents/GA Python/EngCompCombined.py in <module>()
161 # account_id=account_id,
162 max_results=max_results,
--> 163 chunksize=5000)
164
165 df1_conc = pd.concat([x for x in df1])
/Library/Python/2.7/site-packages/pandas/io/ga.pyc in read_ga(metrics, dimensions, start_date, **kwargs)
100 reader = GAnalytics(**reader_kwds)
101 return reader.get_data(metrics=metrics, start_date=start_date,
--> 102 dimensions=dimensions, **kwargs)
103
104
/Library/Python/2.7/site-packages/pandas/io/ga.pyc in get_data(self, metrics, start_date, end_date, dimensions, segment, filters, start_index, max_results, index_col, parse_dates, keep_date_col, date_parser, na_values, converters, sort, dayfirst, account_name, account_id, property_name, property_id, profile_name, profile_id, chunksize)
255 account = self.get_account(account_name, account_id)
256 web_property = self.get_web_property(account.get('id'), property_name,
--> 257 property_id)
258 profile = self.get_profile(account.get('id'), web_property.get('id'),
259 profile_name, profile_id)
/Library/Python/2.7/site-packages/pandas/io/ga.pyc in get_web_property(self, account_id, name, id, **kwargs)
211 kwds['accountId'] = account_id
212 prop_for_acct = prop_store.list(**kwds).execute()
--> 213 return _get_match(prop_for_acct, name, id, **kwargs)
214
215 def get_profile(self, account_id=None, web_property_id=None, name=None,
/Library/Python/2.7/site-packages/pandas/io/ga.pyc in _get_match(obj_store, name, id, **kwargs)
387
388 if name is None and id is None and key is None:
--> 389 return obj_store.get('items')[0]
390
391 name_ok = lambda item: name is not None and item.get('name') == name
TypeError: 'NoneType' object is not subscriptable
---------------------------------------------------------------------------
```
I currently use Pandas v 0.11.0 (numpy 1.7.1) but I have tried uninstalling and reverting back to 0.10.1 just in case (didn't think this would help!) - Is it failing to pickup details associated with my account? In the above I have commented out my hardcoded account_id as i have never had to submit this before.
I have also tried the same query in the Google API Query Explorer and it still works fine.
This is my first post on github and I am a python beginner so please forgive any obvious mistakes.
Thanks
Dan
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} | 5 | "2013-06-15T01:29:17Z" | "2013-06-21T01:48:25Z" | "2013-06-21T01:48:25Z" | CONTRIBUTOR | null | Basically, do this in the network decorator:
``` python
@wraps
def network_wrapper(*args, **kwargs):
try:
return t(*args, **kwargs)
except URLError:
raise nose.skipTest
```
Working on it right now
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} | 9 | "2013-06-15T04:04:53Z" | "2013-06-15T15:00:25Z" | "2013-06-15T12:25:21Z" | MEMBER | null | Blocker for 0.11.1. This got broken sometime between 0.11.0 and master; I haven't had time to bisect yet. Essentially, write a mixed type DataFrame and a groupby function that extracts a row. This used to do type inference and convert object back to numeric in the columns of the resulting DataFrame. Didn't have a test so I don't blame anyone for breaking it by accident =)
```
In [151]: cafdata.dtypes
Out[151]:
id int64
food object
fgroup object
nutrient object
ngroup object
units object
value float64
dtype: object
In [152]:
def max_value(group):
return group.ix[group['value'].idxmax()]
max_value(cafdata)
Out[152]:
id 14366
food Tea, instant, unsweetened, powder
fgroup Beverages
nutrient Caffeine
ngroup Other
units mg
value 3680
Name: 336702, dtype: object
In [153]: cafdata.groupby('fgroup').apply(max_value).dtypes
Out[153]:
id object
food object
fgroup object
nutrient object
ngroup object
units object
value object
dtype: object
```
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} | 55 | "2013-06-15T12:12:57Z" | "2014-06-12T07:13:41Z" | "2013-06-21T01:48:25Z" | CONTRIBUTOR | null | Instead, `network` decorator in pandas.util.testing catches `IOError` instead.
You have to opt into failing on tests by setting
`pandas.util.testing._RAISE_NETWORK_ERROR_DEFAULT` to `True`.
Also adds a `with_network_connectivity_check` that can automatically check for a connection.
Fixes #3910.
This version of the fix ignores all IOErrors and assumes there are connectivity problems
with any URLError.
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https://api.github.com/repos/pandas-dev/pandas/issues/3915 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3915/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3915/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3915/events | https://github.com/pandas-dev/pandas/pull/3915 | 15,592,973 | MDExOlB1bGxSZXF1ZXN0NjM0MzE1OQ== | 3,915 | CLN: Fix CRLFs in repo + add .gitattributes | {
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} | 39 | "2013-06-15T15:28:08Z" | "2014-06-14T06:24:42Z" | "2013-06-17T23:46:16Z" | CONTRIBUTOR | null | View this pull request with [`?w=t`](https://github.com/pydata/pandas/pull/3915?w=t) added to the end of the url to see that there are not differences [aside from the .gitattributes files] - this should prevent more commits with CRLF entering the repo.
I think it'd be good to merge this (especially because we can avoid this happening in the future.), though (I think) it'll mess up the history for 10 minutes to pandas and `pandas/core/expressions.py`.
After this, will never need to deal with CRLF again! Yay!
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} | 4 | "2013-06-15T18:48:37Z" | "2014-04-05T19:32:40Z" | "2014-03-27T22:23:50Z" | CONTRIBUTOR | null | Nested sort doesn't seem to work with NaNs, see [this SO question](http://stackoverflow.com/questions/17126500/pandas-nested-sort-and-nan).
```
In [11]: df
Out[11]:
a b
0 1 9
1 2 NaN
2 NaN 5
3 1 2
4 6 5
5 8 4
6 4 5
In [12]: df.sort(columns=["a","b"])
Out[12]:
a b
3 1 2
0 1 9
1 2 NaN
2 NaN 5
6 4 5
4 6 5
5 8 4
```
(It works as expected using a single columns)
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} | 9 | "2013-06-15T19:31:23Z" | "2013-07-28T00:07:15Z" | "2013-06-16T16:12:16Z" | MEMBER | null | as per [the amazon docs](http://docs.aws.amazon.com/AmazonS3/latest/dev/VirtualHosting.html#VirtualHostingCustomURLs)
> "Any bucket with a DNS compatible name may be referenced as follows: http://[BucketName].s3.amazonaws.com/[Filename], for example, http://images.johnsmith.net.s3.amazonaws.com/mydog.jpg. Using CNAME you can map images.johnsmith.net to an Amazon S3 host name so the previous URL could become: http://images.johnsmith.net/mydog.jpg."
this function is looking at the scheme which will never test as `'s3'` since that is not (to my knowledge) a valid URL protocol
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} | 8 | "2013-06-15T23:21:09Z" | "2013-08-07T17:58:11Z" | "2013-08-07T17:58:11Z" | MEMBER | null | ```
In [9]: df = mkdf(10, 10, data_gen_f=lambda *args: randint(2), c_idx_type='u', r_idx_type='u')
In [10]: df.sort_index(df.columns[0])
```
barfs a `UnicodeEncodeError` inside of a `ValueError`. this maybe ongoing, but might be easy enough to use a macro to correct this in a reasonably fast way
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} | [] | closed | false | null | [] | null | 9 | "2013-06-16T03:41:25Z" | "2014-07-16T08:14:25Z" | "2013-06-17T12:21:25Z" | CONTRIBUTOR | null | This import was missing from `core/groupby.py`. This also suggests that there is an issue with the test cases, given that no test triggers this issue (suggesting that there is no current test that triggers the `elif` condition below).
``` python
def _try_cast(self, result, obj):
""" try to cast the result to our obj original type,
we may have roundtripped thru object in the mean-time """
try:
if obj.ndim > 1:
dtype = obj.values.dtype
else:
dtype = obj.dtype
if _is_numeric_dtype(dtype):
# need to respect a non-number here (e.g. Decimal)
if len(result) and issubclass(type(result[0]),(np.number,float,int)):
result = _possibly_downcast_to_dtype(result, dtype)
elif issubclass(dtype.type, np.datetime64):
if is_datetime64_dtype(obj.dtype):
result = result.astype(obj.dtype)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3921 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3921/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3921/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3921/events | https://github.com/pandas-dev/pandas/pull/3921 | 15,600,729 | MDExOlB1bGxSZXF1ZXN0NjM0NTcyNQ== | 3,921 | CLN: Remove unused Exceptions | {
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} | 25 | "2013-06-16T04:52:12Z" | "2014-07-06T05:46:03Z" | "2013-06-19T01:13:57Z" | CONTRIBUTOR | null | There are at least 3 `Exception`s that are _never_ raised anywhere, so they can be removed without issue. (There are other exceptions that probably should be removed or refactored, but this is just a basic commit to make sure that these don't get used going forward.)
CLN: Remove 'DateConversionError' that is never raised anywhere
CLN: Remove TimeSeriesError that was never used
CLN: Remove unused 'PanelError'
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https://api.github.com/repos/pandas-dev/pandas/issues/3922 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3922/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3922/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3922/events | https://github.com/pandas-dev/pandas/issues/3922 | 15,601,818 | MDU6SXNzdWUxNTYwMTgxOA== | 3,922 | pandas appears to crash doing pivot table from 10 minute examples | {
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} | 53 | "2013-06-16T07:24:05Z" | "2014-09-08T14:22:11Z" | "2013-06-21T20:16:55Z" | NONE | null | pandas 0.11.0 installed with Python 2.7.2 using "pip install pandas" on fully patched mac osx 10.8.5, running in a virtual environment. running any pivot table against any DataFrame create so for -- nothing happens. pressing ctrl-c I see the latest lines run are:
```
422 while com._long_prod(sizes) >= 2 ** 63:
423 i = len(sizes)
```
--> 424 while com._long_prod(sizes[:i]) >= 2 *\* 63:
425 i -= 1
The problem is reproducible using http://pandas.pydata.org/pandas-docs/stable/10min.html and running:
from pandas import DataFrame, Series
import pandas as pd
import numpy as np
then running lines [97], [98], and [99] in iPython and Python native.
I can't see any way to debug or go further.
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https://api.github.com/repos/pandas-dev/pandas/issues/3923 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3923/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3923/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3923/events | https://github.com/pandas-dev/pandas/issues/3923 | 15,604,521 | MDU6SXNzdWUxNTYwNDUyMQ== | 3,923 | Using cython in pandas tutorial | {
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] | closed | false | null | [] | null | 38 | "2013-06-16T12:25:46Z" | "2013-06-21T00:35:45Z" | "2013-06-21T00:35:45Z" | CONTRIBUTOR | null | ### Please see #3965 for current draft.
Re: this topic https://groups.google.com/forum/?fromgroups#!topic/pydata/aLxALYqosOU (cc @jreback).
I think this is one of the killer features of pandas so I think merits being in the docs. (I'd definitely be interested in reading it!) Maybe something structured like this with a good example:
- write in python first (unit-test, and check for speed, it may be good enough!)
- try and rewrite in python to be more efficient (now, it may be good enough!)
- profile to work out which part is slow (and needs cython love)
- writing and calling a cython function (to do that slow bit faster)
I'm not sure what would make a good toy example for this (and I think that choosing a good one is crucial).
Thoughts?
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https://api.github.com/repos/pandas-dev/pandas/issues/3924 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3924/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3924/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3924/events | https://github.com/pandas-dev/pandas/pull/3924 | 15,608,264 | MDExOlB1bGxSZXF1ZXN0NjM0NzkyOQ== | 3,924 | CLN: Replace bare exceptions with more descriptive ones | {
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} | [] | closed | false | null | [] | null | 5 | "2013-06-16T17:56:52Z" | "2014-06-20T16:24:15Z" | "2013-06-19T01:57:07Z" | CONTRIBUTOR | null | This takes care of the low-hanging fruit (really obvious Exception changes), there are still a bunch more to change, but I thought I would split it up into parts and deal with external exceptions before messing with anything that might potentially be raised internally. [I rebased this slightly to remove a few comments - but it did pass previously so it should be fine]
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https://api.github.com/repos/pandas-dev/pandas/issues/3925 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3925/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3925/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3925/events | https://github.com/pandas-dev/pandas/issues/3925 | 15,609,120 | MDU6SXNzdWUxNTYwOTEyMA== | 3,925 | Access DateTimeIndexed dataframe by timestamp | {
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} | 3 | "2013-06-16T19:06:30Z" | "2013-06-17T18:48:57Z" | "2013-06-17T18:48:57Z" | NONE | null | Hello,
I am new to pandas and thanks for this great library!
I have a data frame like this:
```
Gold_2012.head()
open high low close volume
date_time
2012-01-02 18:01:00 1571.0 1571.0 1569.1 1569.8 351
2012-01-02 18:02:00 1569.8 1570.0 1569.7 1569.8 54
2012-01-02 18:03:00 1570.0 1570.0 1569.1 1569.9 247
2012-01-02 18:04:00 1570.0 1570.0 1569.8 1569.9 55
2012-01-02 18:05:00 1569.8 1569.9 1568.5 1568.5 48
```
I am trying to access the first element of this dataframe. If I use loc function, everything works out:
```
Gold_2012.loc[Gold_2012.index[0]]
open 1571.0
high 1571.0
low 1569.1
close 1569.8
volume 351.0
Name: 2012-01-02 18:01:00-06:00, dtype: float64
```
But if I do something like this, an error is thrown. Is this expected?
```
Gold_2012[Gold_2012.index[0]]
```
---
KeyError Traceback (most recent call last)
<ipython-input-30-bb7117766fdd> in <module>()
----> 1 Gold_2012[Gold_2012.index[0]]
/Users/chen/Virtualenvs/python3Env/lib/python3.3/site-packages/pandas/core/frame.py in **getitem**(self, key)
1926 else:
1927 # get column
-> 1928 return self._get_item_cache(key)
1929
1930 def _getitem_slice(self, key):
/Users/chen/Virtualenvs/python3Env/lib/python3.3/site-packages/pandas/core/generic.py in _get_item_cache(self, item)
568 return cache[item]
569 except Exception:
--> 570 values = self._data.get(item)
571 res = self._box_item_values(item, values)
572 cache[item] = res
/Users/chen/Virtualenvs/python3Env/lib/python3.3/site-packages/pandas/core/internals.py in get(self, item)
1382
1383 def get(self, item):
-> 1384 _, block = self._find_block(item)
1385 return block.get(item)
1386
/Users/chen/Virtualenvs/python3Env/lib/python3.3/site-packages/pandas/core/internals.py in _find_block(self, item)
1524
1525 def _find_block(self, item):
-> 1526 self._check_have(item)
1527 for i, block in enumerate(self.blocks):
1528 if item in block:
/Users/chen/Virtualenvs/python3Env/lib/python3.3/site-packages/pandas/core/internals.py in _check_have(self, item)
1531 def _check_have(self, item):
1532 if item not in self.items:
-> 1533 raise KeyError('no item named %s' % com.pprint_thing(item))
1534
1535 def reindex_axis(self, new_axis, method=None, axis=0, copy=True):
KeyError: 'no item named 2012-01-02 18:01:00-06:00'
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https://api.github.com/repos/pandas-dev/pandas/issues/3926 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3926/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3926/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3926/events | https://github.com/pandas-dev/pandas/issues/3926 | 15,609,285 | MDU6SXNzdWUxNTYwOTI4NQ== | 3,926 | Avoid upcasting integers in some merge operations | {
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} | [] | closed | false | null | [] | null | 2 | "2013-06-16T19:19:08Z" | "2013-06-17T03:32:41Z" | "2013-06-17T03:32:41Z" | MEMBER | null | Reported on mailing list
```
I've noticed that merge does not necessarily retain the datatype of the
variables passed. For example,
----
stock = DataFrame({'date' : [2000, 2001, 2002],
'S' : [10.1, 10.5, 11.1]})
ex = DataFrame({'Grant' : [1, 1], 'date' : [2000, 2002]})
merged = stock.merge(ex, how='left', on='date')
----
In merged, the Grant variable has been promoted from int to float. Is this
deliberate behavior?
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3927 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3927/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3927/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3927/events | https://github.com/pandas-dev/pandas/pull/3927 | 15,610,136 | MDExOlB1bGxSZXF1ZXN0NjM0ODU3MQ== | 3,927 | TST: Fix error in assert_produces_warning. | {
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} | [] | closed | false | null | [] | null | 7 | "2013-06-16T20:25:20Z" | "2014-07-16T08:14:29Z" | "2013-06-17T00:18:26Z" | CONTRIBUTOR | null | When I copied this over and sent to @cpcloud,
I thought the indentation was wrong and changed it.
Turned out I was wrong -- sorry about that!
Now all the doctests pass...
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} | 3 | "2013-06-16T20:48:56Z" | "2021-04-11T02:09:20Z" | null | CONTRIBUTOR | null | Add to advanced groupby sections docs
This seem a little strange, here the integer column N is upcast when using an apply (since the row (Series) has floats in it), unfortunately in this example this means I get an exception:
```
from numpy.random import *
df = pd.DataFrame({'a': randn(100), 'b': randn(100), 'N': randint(10, 100, (100))})
df.dtypes
N int64
a float64
b float64
dtype: object
def f(x):
return x**2-x
def integrate_f(a, b, N):
s = 0
dx = (b-a)/N
for i in range(N):
s += f(a+i*dx)
return s * dx
df.apply(lambda x: integrate_f(x['a'], x['b'], x['N']), axis=1)---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-179-af52228271ac> in <module>()
----> 1 df.apply(lambda x: integrate_f(x['a'], x['b'], x['N']), axis=1)
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/frame.pyc in apply(self, func, axis, broadcast, raw, args, **kwds)
4116 return self._apply_raw(f, axis)
4117 else:
-> 4118 return self._apply_standard(f, axis)
4119 else:
4120 return self._apply_broadcast(f, axis)
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/frame.pyc in _apply_standard(self, func, axis, ignore_failures)
4191 # no k defined yet
4192 pass
-> 4193 raise e
4194
4195
TypeError: ('range() integer end argument expected, got numpy.float64.', u'occurred at index 0')
```
The obvious "fix" is to use a dummy column... ahem:
```
df = pd.DataFrame({'x': 'x', 'a': randn(100), 'b': randn(100), 'N': randint(10, 100, (100))})
```
related #3926
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https://api.github.com/repos/pandas-dev/pandas/issues/3929 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3929/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3929/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3929/events | https://github.com/pandas-dev/pandas/pull/3929 | 15,613,088 | MDExOlB1bGxSZXF1ZXN0NjM0OTU3OA== | 3,929 | PTF no more | {
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} | 1 | "2013-06-17T00:41:30Z" | "2014-07-16T08:14:30Z" | "2013-06-17T02:45:06Z" | NONE | null | The travis network build cache (activated via commit message, for whitelisted commiters) was added a few months back. It worked well for a while, but ultimately
it's seen little use in practive by commiters.
While it was gratifying to see this actually worked, all the additional
magic in ci/ doesn't pay for itself in practice, so I've decided to retire this
unless there are strong objections and get a simpler ci/ in return.
The build cache system got an upgrade as a side-effect of this
work and `scripts/use_build_cache.py` remains a useful bit of kit.
Not entirely wasted effort after all.
xref https://github.com/pydata/pandas/pull/3383
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https://api.github.com/repos/pandas-dev/pandas/issues/3930 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3930/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3930/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3930/events | https://github.com/pandas-dev/pandas/pull/3930 | 15,613,397 | MDExOlB1bGxSZXF1ZXN0NjM0OTY5Nw== | 3,930 | DOC: fix to_json docstring nesting issue | {
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} | 17 | "2013-06-17T01:37:13Z" | "2014-06-19T03:37:21Z" | "2013-06-19T00:59:39Z" | CONTRIBUTOR | null | this no longer has much to do with #3925, and is only fixing a bug
Minor revision to select on second frequency
```
In [11]: df = DataFrame(randn(5,5),columns=['open','high','low','close','volume'],index=date_range('2012-01-02 18:01:00',periods=5,tz='US/Central',freq='s'))
In [12]: df
Out[12]:
open high low close volume
2012-01-02 18:01:00-06:00 0.131243 0.301542 0.128027 0.804162 1.296658
2012-01-02 18:01:01-06:00 0.341487 1.548695 0.703234 0.904201 1.422337
2012-01-02 18:01:02-06:00 -1.050453 -1.884035 1.537788 -0.821058 0.558631
2012-01-02 18:01:03-06:00 0.846885 1.045378 -0.722903 -0.613625 -0.476531
2012-01-02 18:01:04-06:00 1.186823 -0.018299 -0.513886 -1.103269 -0.311907
In [14]: df['2012-01-02 18:01:02']
Out[14]:
open high low close volume
2012-01-02 18:01:02-06:00 -1.050453 -1.884035 1.537788 -0.821058 0.558631
```
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```
print(Gold_2012.head(), "\n")
print(Gold_2012)
open high low close volume
date_time
2012-01-02 18:01:00 1571.0 1571.0 1569.1 1569.8 351
2012-01-02 18:02:00 1569.8 1570.0 1569.7 1569.8 54
2012-01-02 18:03:00 1570.0 1570.0 1569.1 1569.9 247
2012-01-02 18:04:00 1570.0 1570.0 1569.8 1569.9 55
2012-01-02 18:05:00 1569.8 1569.9 1568.5 1568.5 48
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 351367 entries, 2012-01-02 18:01:00 to 2012-12-31 17:15:00
Data columns (total 5 columns):
open 351367 non-null values
high 351367 non-null values
low 351367 non-null values
close 351367 non-null values
volume 351367 non-null values
dtypes: float64(4), int64(1)
```
However, after
Gold_2012.asfreq(pd.offsets.Minute())
It becomes:
```
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 524115 entries, 2012-01-02 18:01:00 to 2012-12-31 17:15:00
Freq: T
Data columns (total 5 columns):
open 351367 non-null values
high 351367 non-null values
low 351367 non-null values
close 351367 non-null values
volume 351367 non-null values
dtypes: float64(5)
```
The volume column becomes dtype of float64.
system:
```
Panadas version: 0.11.0
Numpy version: 1.7.1
Matplotlib version: 1.2.1
Python version: 3.3.1 (default, May 12 2013, 21:14:39)
[GCC 4.2.1 Compatible Apple Clang 4.0 (tags/Apple/clang-418.0.60)]
```
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} | 5 | "2013-06-17T10:46:24Z" | "2016-07-24T04:10:17Z" | "2016-07-24T04:10:11Z" | NONE | null | Hi,
as far as I know, pandas does not support yet the analysis of time series homogeneity and the detection of breakpoints.
Would be really great if such a feature could be implemented. What I am thinking about is similar to the following R-project, called bfast (http://bfast.r-forge.r-project.org/).
Would be great if some people would be interested in this.
Alex
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https://api.github.com/repos/pandas-dev/pandas/issues/3934 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3934/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3934/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3934/events | https://github.com/pandas-dev/pandas/pull/3934 | 15,629,440 | MDExOlB1bGxSZXF1ZXN0NjM1NzI5Mw== | 3,934 | CLN: cleaned up _try_cast in core/groupby.py to eliminate cruft (GH3920) | {
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} | [] | closed | false | null | [] | null | 0 | "2013-06-17T12:20:16Z" | "2014-07-16T08:14:37Z" | "2013-06-17T12:47:20Z" | CONTRIBUTOR | null | raised on #3920
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https://api.github.com/repos/pandas-dev/pandas/issues/3935 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3935/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3935/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3935/events | https://github.com/pandas-dev/pandas/pull/3935 | 15,634,021 | MDExOlB1bGxSZXF1ZXN0NjM1OTYwNA== | 3,935 | FIX: StataReader | {
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} | 9 | "2013-06-17T14:05:37Z" | "2014-07-16T08:14:39Z" | "2013-06-21T00:11:15Z" | CONTRIBUTOR | null | Fix for a bug in StataReader resulting in errors when reading Stata file with string columns
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https://api.github.com/repos/pandas-dev/pandas/issues/3936 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3936/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3936/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3936/events | https://github.com/pandas-dev/pandas/pull/3936 | 15,651,005 | MDExOlB1bGxSZXF1ZXN0NjM2ODIyNg== | 3,936 | BUG: fix python3/2 numpy import_array macro build issue with clang | {
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} | [] | closed | false | null | [] | null | 8 | "2013-06-17T19:28:06Z" | "2014-06-21T19:34:31Z" | "2013-06-17T20:22:52Z" | MEMBER | null | closes #3872. no really, it does.
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https://api.github.com/repos/pandas-dev/pandas/issues/3937 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3937/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3937/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3937/events | https://github.com/pandas-dev/pandas/pull/3937 | 15,651,905 | MDExOlB1bGxSZXF1ZXN0NjM2ODY4Mw== | 3,937 | allow HDFStore to remain open when TableIterator is returned from read_hdf | {
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} | 12 | "2013-06-17T19:45:39Z" | "2014-07-16T08:14:42Z" | "2013-06-18T22:15:20Z" | NONE | null | Hi,
I'm using a TableIterator from pandas.read_hdf function (with the keyword argument iterator=True), I am unable to retrieve any data due to the error "ClosedNodeError: the node object is closed".
For instance:
```
pandas.DataFrame({'a':[1,2,3], 'b':[4,5,6]}).to_hdf("test.h5", "test", append=True)
it = pandas.read_hdf("test.h5","test",iterator=True)
iter(it).next()
Traceback (most recent call last):
File "<ipython-input-22-5634d86698ab>", line 1, in <module>
iter(it).next()
File "/usr/local/lib/python2.7/site-packages/pandas/io/pytables.py", line 912, in __iter__
v = self.func(current, stop)
...
File "/usr/local/lib/python2.7/site-packages/tables/node.py", line 355, in _g_check_open
raise ClosedNodeError("the node object is closed")
ClosedNodeError: the node object is closed
```
I looked through source code of panda.io.pytables and found that in the
get_store function, store.close() is always run when read_hdf returns, even if
it returns an TableIterator. My assumption is that store should remain open in
order for TableIterator to work. Can you please let me know if this fix is
acceptable, or is there an easier way to do this?
Thanks,
Sean
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https://api.github.com/repos/pandas-dev/pandas/issues/3938 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3938/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3938/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3938/events | https://github.com/pandas-dev/pandas/issues/3938 | 15,659,952 | MDU6SXNzdWUxNTY1OTk1Mg== | 3,938 | DOC: expand section on partial string indexing of timeseries | {
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} | 0 | "2013-06-17T22:34:24Z" | "2013-06-18T00:42:28Z" | "2013-06-18T00:42:28Z" | CONTRIBUTOR | null | see #3931
http://pandas.pydata.org/pandas-docs/dev/timeseries.html#datetimeindex
could use examples of increasing specialized partial string indexing
and especially issues of end inclusion (eg #3905)
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} | 2 | "2013-06-17T23:41:32Z" | "2014-06-14T06:04:28Z" | "2013-06-18T00:42:28Z" | CONTRIBUTOR | null | closes #3938
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} | 9 | "2013-06-18T01:26:02Z" | "2014-02-20T21:23:17Z" | "2014-02-20T21:23:17Z" | NONE | null | Installing on Windows with MinGW installed.
Pandas Version: Installing from source
NumPy Version : `1.7.1`
After running `setup.py install` I get this error:
```
cc1.exe: error: unrecognized command line option '-mno-cygwin'
error: command 'gcc' failed with exit status 1
```
I believe I've installed MinGW correctly. Is this a problem with Pandas or MinGW?
Terminal output:
```
C:\Python27\Lib\site-packages\pandas>setup.py install
running install
running bdist_egg
running egg_info
writing requirements to pandas.egg-info\requires.txt
writing pandas.egg-info\PKG-INFO
writing top-level names to pandas.egg-info\top_level.txt
writing dependency_links to pandas.egg-info\dependency_links.txt
reading manifest file 'pandas.egg-info\SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no files found matching 'TODO.rst'
warning: no files found matching 'setupegg.py'
no previously-included directories found matching 'doc\build'
warning: no previously-included files matching '*.so' found anywhere in distribution
warning: no previously-included files matching '*.pyd' found anywhere in distribution
warning: no previously-included files matching '*.pyc' found anywhere in distribution
warning: no previously-included files matching '.DS_Store' found anywhere in distribution
warning: no previously-included files matching '*.png' found anywhere in distribution
writing manifest file 'pandas.egg-info\SOURCES.txt'
installing library code to build\bdist.win-amd64\egg
running install_lib
running build_py
copying pandas\version.py -> build\lib.win-amd64-2.7\pandas
running build_ext
skipping 'pandas\index.c' Cython extension (up-to-date)
building 'pandas.index' extension
C:\MinGW\bin\gcc.exe -mno-cygwin -mdll -O -Wall -Ipandas/src/klib -Ipandas/src -
IC:\Python27\lib\site-packages\numpy\core\include -IC:\Python27\include -IC:\Pyt
hon27\PC -c pandas\index.c -o build\temp.win-amd64-2.7\Release\pandas\index.o
cc1.exe: error: unrecognized command line option '-mno-cygwin'
error: command 'gcc' failed with exit status 1
```
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} | 20 | "2013-06-18T08:30:02Z" | "2020-04-27T16:09:26Z" | "2020-04-27T16:09:26Z" | CONTRIBUTOR | null | Many python functions (sorting, max/min) accept a key argument, perhaps they could in pandas too.
.
_The terrible motivating example was this awful hack from [this question](http://stackoverflow.com/a/17157110/1240268).... for which maybe one could do_
```
df.sort_index(key=lambda t: literal_eval(t[1:-1]))
```
_This would still be an awful awful hack, but a slightly less awful one_.
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} | 5 | "2013-06-18T08:38:45Z" | "2014-07-14T21:42:11Z" | "2014-07-14T21:42:11Z" | CONTRIBUTOR | null | another example:
http://stackoverflow.com/questions/23814368/sorting-pandas-categorical-labels-after-groupby
At the moment bins / buckets are simply strings, so you can't sort them properly.
```
A=pd.qcut(X['x'], [0,0.25,0.5,0.75,1.0])
In [28]: A[0]
Out[28]: '(0.635, 2.4]'
In [29]: type(A[0])
Out[29]: str
```
The dtype of the Index is object (because these are strings), maybe we could just have these as a subclass of strings where:
```
def __lt__(self, other):
from ast import literal_eval
return literal_eval(self) < literal_eval(other)
```
Can put together pr if seems reasonable.
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} | 13 | "2013-06-18T11:22:42Z" | "2014-04-25T12:57:40Z" | "2014-02-04T09:24:55Z" | CONTRIBUTOR | null | after #3890, still unresolved / inconsistent
non-trivial to fix, so pushing to 0.12
```
In [14]: Timestamp('2012')
Out[14]: Timestamp('2012-06-18 00:00:00', tz=None)
In [15]: to_datetime('2012')
Out[15]: Timestamp('2012-01-01 00:00:00', tz=None)
```
```
In [17]: pd.date_range('2014', '2015', freq='M')
Out[17]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2014-04-30, ..., 2015-03-31]
Length: 12, Freq: M, Timezone: None
```
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} | 13 | "2013-06-18T12:35:55Z" | "2013-08-27T00:07:49Z" | "2013-08-27T00:07:49Z" | CONTRIBUTOR | null | Should I write a `Series.histogram` that calls `numpy.histogram` and returns a new Series of counts indexed by bins?
```
In [4]: s = Series(randn(100))
In [5]: counts, bins = np.histogram(s)
In [6]: Series(counts, index=bins[:-1])
Out[6]:
-2.968575 1
-2.355032 4
-1.741488 5
-1.127944 26
-0.514401 23
0.099143 23
0.712686 12
1.326230 5
1.939773 0
2.553317 1
dtype: int32
```
...would be accomplished by...
```
Series.histogram()
```
which would accept at the arguments that `np.histogram` accepts (e.g., choosing bins manually, specifying number or range of bins, etc.).
from http://stackoverflow.com/a/17150734/1221924
The proposed method doesn't save all that much typing, but it would help users discover this convenient way of storing a histogram. Proceed?
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} | 31 | "2013-06-18T14:12:54Z" | "2014-07-04T07:35:19Z" | "2013-06-19T01:46:00Z" | CONTRIBUTOR | null | This updates pandas JSON to use the latest ujson version. A fair few fixes and enhancements are included. Unfortunately however there were a lot of whitespace changes so it looks like a lot more than it actually is.
All tests pass on py27 and py33. Valgrind run of JSON tests with Python 2.7 is clean.
Also included are two fixes encountered during merging and pushed upstream to ultrajson:
https://github.com/esnme/ultrajson/pull/93
https://github.com/esnme/ultrajson/pull/94
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} | 13 | "2013-06-18T22:05:48Z" | "2014-07-01T10:07:30Z" | "2013-06-18T22:57:40Z" | CONTRIBUTOR | null | closes #3937
DOC: update v0.11.1.rst
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} | 10 | "2013-06-18T23:46:13Z" | "2014-07-23T22:09:51Z" | "2014-06-21T20:10:21Z" | CONTRIBUTOR | null | see http://stackoverflow.com/questions/17159207/change-timezone-of-date-time-column-in-pandas-and-add-as-hierarchical-index/17159276#17159276
```
dat = pd.DataFrame({'label':['a', 'a', 'a', 'b', 'b', 'b'], 'datetime':['2011-07-19 07:00:00', '2011-07-19 08:00:00', '2011-07-19 09:00:00', '2011-07-19 07:00:00', '2011-07-19 08:00:00', '2011-07-19 09:00:00'], 'value':range(6)})
dat.index = pd.to_datetime(dat.pop('datetime'), utc=True)
dat.index = dat.index.tz_localize('UTC').tz_convert('US/Pacific')
dat
label value
datetime
2011-07-19 00:00:00-07:00 a 0
2011-07-19 01:00:00-07:00 a 1
2011-07-19 02:00:00-07:00 a 2
2011-07-19 00:00:00-07:00 b 3
2011-07-19 01:00:00-07:00 b 4
2011-07-19 02:00:00-07:00 b 5
```
If we add another index (we lose the tz):
```
In [14]: dat.set_index('label', append=True).swaplevel(0, 1)
Out[14]:
value
label datetime
a 2011-07-19 07:00:00 0
2011-07-19 08:00:00 1
2011-07-19 09:00:00 2
b 2011-07-19 07:00:00 3
2011-07-19 08:00:00 4
2011-07-19 09:00:00 5
```
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```
/tmp/pandas/doc/source/visualization.rst:None: WARNING: image file not readable: _static/series_plot_basic2.png
/tmp/pandas/doc/source/visualization.rst:None: WARNING: image file not readable: _static/frame_plot_basic_noleg.png
/tmp/pandas/doc/source/visualization.rst:None: WARNING: image file not readable: _static/frame_plot_subplots.png
/tmp/pandas/doc/source/visualization.rst:None: WARNING: image file not readable: _static/series_plot_logy.png
/tmp/pandas/doc/source/visualization.rst:None: WARNING: image file not readable: _static/df_plot_xy.png
/tmp/pandas/doc/source/visualization.rst:None: WARNING: image file not readable: _static/series_plot_secondary_y.png
/tmp/pandas/doc/source/visualization.rst:None: WARNING: image file not readable: _static/frame_plot_secondary_y.png
/tmp/pandas/doc/source/visualization.rst:None: WARNING: image file not readable: _static/frame_plot_secondary_y_no_right.png
/tmp/pandas/doc/source/visualization.rst:None: WARNING: image file not readable: _static/ser_plot_suppress.png
/tmp/pandas/doc/source/visualization.rst:None: WARNING: image file not readable: _static/ser_plot_suppress_parm.png
/tmp/pandas/doc/source/visualization.rst:None: WARNING: image file not readable: _static/ser_plot_suppress_context.png
```
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} | 1 | "2013-06-19T01:04:17Z" | "2013-09-22T17:09:29Z" | "2013-09-22T17:09:29Z" | CONTRIBUTOR | null | see #3944
currently the default is 'ignore', which returns the input argument (rather than raise, say a `ValueError`); various routines will try a conversion (which will result in a conversion or no effect), then move on; rather than raising (and having to catch it)
so investigate....
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} | 21 | "2013-06-19T02:11:55Z" | "2014-03-11T02:19:02Z" | "2014-03-11T02:19:02Z" | CONTRIBUTOR | null | I'm moving this to an issue so it's easier to track and I can split into separate, smaller, pull requests. (previously was #3924).
For right now, the goal is to take care of the low-hanging fruit (the really obvious items that should be TypeError, ValueError, etc.) and then potentially take on more of the code base later.
Packages/Files where exceptions have been changed (will be checked off once test cases are edited for them)
- [x] `merge`
- [x] `stats/common`
- [x] `tools/tile`
- [x] `core/common`
- [x] `core/format`
- [x] `core/frame`
- [x] `pandas/sparse`
- [x] `pandas/tseries`
- [ ] `core/internals`
- [x] `core/index`
- [ ] `core/panel`
- [ ] `core/series`
- [ ] `core/panelnd`
- [ ] `rpy/common`
- [x] `io/excel`
More to come...
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} | 5 | "2013-06-19T03:46:35Z" | "2014-06-19T19:20:54Z" | "2013-06-19T21:09:34Z" | CONTRIBUTOR | null | Relates to #3954. In addition to changing exceptions types returned to be more explicit, this pull incorporates the following changes:
1. Adds a new `assertRaisesRegexp` to `util/testing.py` to port the `assertRaisesRegexp` helper from 2.7+ unittest
2. Cleans up stats/common.
3. Fix up initial assertions in `tseries.offset._cached_range` that were all off + fix the test cases which were all just raising TypeErrors because they were calling with the wrong arguments.
4. Changes the example timezone from Asia/Beijing to Asia/Hong+Kong b/c `Asia/Beijing` is not supported by pytz.
5. Any tz-aware : tz-naive comparison fails with TypeError, as will mismatched, localize, etc. calls.
6. Changes `SparseArray` indexing error messages to match tuple message for completeness.
7. Improves the window check in ols.
After you all say it's okay to merge, I'll update the docs to reflect changes.
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} | 26 | "2013-06-19T07:15:48Z" | "2013-06-19T17:30:27Z" | "2013-06-19T16:48:59Z" | NONE | null | Checked out a2a7a5f85dd25f05f7d3b813d15dc0f1ecc45fa2, `python3 setup.py build` fails with:
```
gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -Ipandas/src/ujson/python -Ipandas/src/ujson/lib -Ipandas/src/datetime -Ipandas/src/klib -Ipandas/src -I/home/me/anaconda/envs/py33/lib/python3.3/site-packages/numpy/core/include -I/home/me/anaconda/envs/py33/include/python3.3m -c pandas/src/ujson/lib/ultrajsonenc.c -o build/temp.linux-x86_64-3.3/pandas/src/ujson/lib/ultrajsonenc.o
gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -Ipandas/src/ujson/python -Ipandas/src/ujson/lib -Ipandas/src/datetime -Ipandas/src/klib -Ipandas/src -I/home/me/anaconda/envs/py33/lib/python3.3/site-packages/numpy/core/include -I/home/me/anaconda/envs/py33/include/python3.3m -c pandas/src/ujson/lib/ultrajsondec.c -o build/temp.linux-x86_64-3.3/pandas/src/ujson/lib/ultrajsondec.o
pandas/src/ujson/lib/ultrajsondec.c: In function ‘decode_numeric’:
pandas/src/ujson/lib/ultrajsondec.c:119: error: ‘LLONG_MAX’ undeclared (first use in this function)
pandas/src/ujson/lib/ultrajsondec.c:119: error: (Each undeclared identifier is reported only once
pandas/src/ujson/lib/ultrajsondec.c:119: error: for each function it appears in.)
pandas/src/ujson/lib/ultrajsondec.c:125: error: ‘LLONG_MIN’ undeclared (first use in this function)
error: command 'gcc' failed with exit status 1
```
NB: Building da14c6e857fd1fc7875cd552779a6063ec9e4ddc worked fine yesterday on Ubuntu 12.04, otherwise the same environment
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} | 1 | "2013-06-19T14:51:05Z" | "2014-07-11T21:47:26Z" | "2013-06-19T16:48:59Z" | MEMBER | null | The -D_GNU_SOURCE is needed by older versions of GCC. Many newer versions
enable this flag by default, which is why it wasn't being caught before.
closes #3957.
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} | 34 | "2013-06-19T15:43:06Z" | "2016-10-12T23:03:54Z" | "2014-05-14T21:21:48Z" | CONTRIBUTOR | null | I don't _think_ there is a way to get the nlargest elements in a DataFrame without sorting.
In ordinary python you'd use heapq's nlargest (and we can hack a bit to use it for a DataFrame):
```
In [10]: df
Out[10]:
IP Agent Count
0 74.86.158.106 Mozilla/5.0+(compatible; UptimeRobot/2.0; http... 369
1 203.81.107.103 Mozilla/5.0 (Windows NT 6.1; rv:21.0) Gecko/20... 388
2 173.199.120.155 Mozilla/5.0 (compatible; AhrefsBot/4.0; +http:... 417
3 124.43.84.242 Mozilla/5.0 (Windows NT 6.2) AppleWebKit/537.3... 448
4 112.135.196.223 Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.3... 454
5 124.43.155.138 Mozilla/5.0 (Windows NT 6.1; WOW64; rv:21.0) G... 461
6 124.43.104.198 Mozilla/5.0 (Windows NT 5.1; rv:21.0) Gecko/20... 467
In [11]: df.sort('Count', ascending=False).head(3)
Out[11]:
IP Agent Count
6 124.43.104.198 Mozilla/5.0 (Windows NT 5.1; rv:21.0) Gecko/20... 467
5 124.43.155.138 Mozilla/5.0 (Windows NT 6.1; WOW64; rv:21.0) G... 461
4 112.135.196.223 Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.3... 454
```
```
In [21]: from heapq import nlargest
In [22]: top_3 = nlargest(3, df.iterrows(), key=lambda x: x[1]['Count'])
In [23]: pd.DataFrame.from_items(top_3).T
Out[23]:
IP Agent Count
6 124.43.104.198 Mozilla/5.0 (Windows NT 5.1; rv:21.0) Gecko/20... 467
5 124.43.155.138 Mozilla/5.0 (Windows NT 6.1; WOW64; rv:21.0) G... 461
4 112.135.196.223 Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.3... 454
```
This is much slower than sorting, presumbly from the overhead, I thought I'd throw this as a feature idea anyway.
see http://stackoverflow.com/a/17194717/1240268
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} | 0 | "2013-06-19T17:36:36Z" | "2015-07-31T11:14:36Z" | "2015-07-31T11:14:36Z" | CONTRIBUTOR | null | Creating a pivot table from a DataFrame is easy.
``` python
In [17]: df
Out[17]:
date variable value
0 2000-01-03 00:00:00 A 0.528219
1 2000-01-04 00:00:00 A -0.135071
2 2000-01-05 00:00:00 A -0.343018
3 2000-01-03 00:00:00 B -0.097701
4 2000-01-04 00:00:00 B -1.383248
In [18]: df.pivot('date', 'variable', 'value')
Out[18]:
variable A B
date
2000-01-03 0.528219 -0.097701
2000-01-04 -0.135071 -1.383248
2000-01-05 -0.343018 NaN
```
However if the index has been set, and there is no column to be set as index, pivot fails. From api point of view, the `index` argument is optional, but in fact it is not.
``` python
In [19]: df.set_index('date', inplace=True)
In [20]: df
Out[20]:
variable value
date
2000-01-03 A 0.528219
2000-01-04 A -0.135071
2000-01-05 A -0.343018
2000-01-03 B -0.097701
2000-01-04 B -1.383248
In [21]: df.pivot(columns='variable', values='value')
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
...
KeyError: u'no item named None'
```
Of course one can reset the index before using pivot.
``` python
In [22]: df.reset_index().pivot('date', 'variable', 'value')
Out[22]:
variable A B
date
2000-01-03 0.528219 -0.097701
2000-01-04 -0.135071 -1.383248
2000-01-05 -0.343018 NaN
```
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} | 6 | "2013-06-19T21:12:19Z" | "2016-10-12T23:03:54Z" | "2013-09-14T01:59:59Z" | CONTRIBUTOR | null | Getting a `TypeError: Type str doesn't support the buffer API` when using `read_fwf` on a compressed file with python 3 only; works in python 2.7
Example:
with a file `fwf.bug.txt` like
```
1111111111
2222222222
3333333333
4444444444
```
``` python
import pandas as pd
print(pd.__version__)
widths = [5, 5]
!gzip -d fwf_bug.txt
df = pd.read_fwf('fwf_bug.txt', widths=widths, names=['one', 'two'])
print(df)
!gzip fwf_bug.txt
# python 3 throws an error here.
df2 = pd.read_fwf('fwf_bug.txt.gz', widths=widths,
names=['one', 'two'], compression='gzip')
print(df2)
```
Versions:
python3: `0.11.1.dev-4d06037` should be most recent
python2: `0.11.1.dev-3ebfef9`
I can paste the full traceback if you'd like.
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} | 3 | "2013-06-19T23:01:28Z" | "2014-12-07T15:42:17Z" | "2014-12-07T15:42:17Z" | CONTRIBUTOR | null | I am creating the attached plot like this:
``` python
fig, axes = subplots(3,3, sharex=True, sharey=True)
for ch in range(1,7):
axis = axes.flatten()[ch-1]
cols = some code to map ch to col_names
df[cols].plot(ax=axis)
# do the same for 3 more channels with different IDs
for ch in range(1,4):
axis = axes.flatten()[ch-1+6]
cols = some code to map ch to col_names, slightly different to above
df[cols].plot(ax=axis)
```
I have 2 problems with the result:
1. sharex does not seem to work for time axes
2. The last major x-ticklabel of the last row of axes is 10:00, while for the first 2 rows it is 9:00. I confirmed that all columns have data in the last row (index = 2011-04-02 10:00:00.718000) and not NaN which would have been excluded in the plot, potentially.
Possibly the 2 probs are related?
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} | 11 | "2013-06-19T23:28:35Z" | "2014-06-29T19:01:34Z" | "2013-06-21T00:15:16Z" | CONTRIBUTOR | null | WIP #3923
Please checkout this draft for a pandas cython tutorial, any feedback or ideas appreciated.
_Thanks to @cpcloud for patiently working out/explaining how to force ipython sphinx directive to play nicely with %%cython (it's incredibly sensitive!) and fixing the spacing bug. This seems to build now._ :)
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} | 4 | "2013-06-20T01:51:06Z" | "2015-01-26T01:06:18Z" | "2015-01-26T01:06:18Z" | CONTRIBUTOR | null | Bug? Did this used to work?
```
np.array(pandas.DataFrame.from_dict(dict(a=np.random.rand(50), b=np.random.randint(2, size=50).astype(bool))))
```
We would expect that this casts to a plain float ndarray.
See https://github.com/statsmodels/statsmodels/issues/880
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} | 13 | "2013-06-20T08:03:57Z" | "2013-06-21T12:20:03Z" | "2013-06-21T01:22:33Z" | CONTRIBUTOR | null | Hi there,
I tested:
``` python
data = """A,B,C
foo,1,2,3
bar,4,5,6
baz,7,8,9
"""
reader = pd.read_csv(StringIO(data), iterator=True)
```
I thought that I could do:
```
for row in reader:
print(row)
```
since `reader` is iterable. Unfortunately, it calls the generator `TextFileReader.__iter__`:
``` python
def __iter__(self):
try:
while True:
yield self.read(self.chunksize)
except StopIteration:
pass
```
where `self.chunksize` is `None`. Maybe set `self.chunsize` to 1 when it's not
defined and there is `iterator=True`. I'll propose a patch as soon as possible
--- today or tomorrow.
Best regards,
Damien G.
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} | 1 | "2013-06-20T13:55:35Z" | "2013-06-20T19:27:10Z" | "2013-06-20T19:26:50Z" | CONTRIBUTOR | null | I found this little gem here:
```
File ".....lib/python2.7/site-packages/pandas/core/frame.py" in extract_index
5366. raise ValueError('If use all scalar values, must pass index')
Exception Type: ValueError at /reporting/reports/7/preview
Exception Value: If use all scalar values, must pass index
```
Just wanted to point that out. It's not urgent or very grave.
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} | 5 | "2013-06-20T13:58:15Z" | "2014-03-27T17:44:26Z" | "2013-06-21T00:10:18Z" | CONTRIBUTOR | null | add `units='D'` to `Timestamp/to_datetime` epoch support
I have an int64 series representing days since epoch which I'd like to convert to datetime64.
This works in 11.0 but raises an exception in 0.11.1.dev-964516a. The exception message is "TypeError: cannot convert datetimelike to dtype [datetime64[D]]".
```
import pandas as pd
s = pd.Series([0,1,2])
print s.astype('M8[D]')
```
My workaround is:
```
import pandas as pd
s = pd.Series([0,1,2])
print pd.Series(s.values.astype('M8[D]'))
```
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} | 13 | "2013-06-20T15:56:19Z" | "2013-12-04T00:46:05Z" | "2013-06-28T18:41:53Z" | NONE | null | Basically, if you create a Data
``` python
import pandas as pd
df = pd.DataFrame({ "aa":range(5), "bb":[2.2]*5})
df["cc"] = 0.0 # remove this line, it works
ck = [True]*len(df)
df["bb"].iloc[0] = .13
df_tmp = df.iloc[ck] # or remove this line and it'll work
df["bb"].iloc[0] = .15 # doesn't happen
print df
```
which gives:
```
aa bb cc
0 0 0.13 0
1 1 2.20 0
2 2 2.20 0
3 3 2.20 0
4 4 2.20 0
```
Very strange.
Specs:
```
pd.version.version
Out[5]: '0.11.0'
np.version.version
Out[7]: '1.7.1'
```
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} | 4 | "2013-06-20T16:08:42Z" | "2013-09-22T17:13:54Z" | "2013-09-22T17:13:54Z" | CONTRIBUTOR | null | ``` python
import pandas as pd
import numpy as np
s = pd.Series(range(10))
s.where(s > 4, np.array([np.nan]))
Out[4]:
0 0
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
dtype: int64
```
The culprit is that if upcast is required, ser doesn't get changed in the `com` call. Since we're making a copy of self anyways, shouldn't we always pass in change?
``` python
change = ser if inplace else None
com._maybe_upcast_putmask(ser,~cond,other,change=change)
```
I didn't hit this organically and I'm assuming it's a fairly contrived example. Was just reading code and found this.
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} | 20 | "2013-06-20T21:52:45Z" | "2014-07-17T06:09:18Z" | "2014-03-13T14:37:40Z" | MEMBER | null | works similar to numpy except that the edgeitems is overriden by threshold rather than the other way around which is what np does.
closes #3391
- [x] add release notes
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} | 2 | "2013-06-20T23:40:53Z" | "2013-07-24T23:42:09Z" | "2013-07-24T23:42:01Z" | MEMBER | null | should change this back to stable when stable becomes 0.11.1.
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} | 12 | "2013-06-21T00:00:43Z" | "2013-06-28T17:58:04Z" | "2013-06-28T17:58:04Z" | CONTRIBUTOR | null | closes #3970
`df['bb'].iloc[0] = 0.13`
here `bb` is put into the `_item_cache` and the first element assigned 0.13; `bb` is still a view onto the frame block
`df_tmp = df.iloc[[True]*len(df)]`
`df_tmp` is now a copy of df, but built up as a take block-by-block from `df`. I believe numpy then decides to invalidate the views to the memory in the float block (why I have no idea); `bb` in `df` is still holding the view in the `_item_cache` to the old memory location.
`df['bb'].iloc[0] = 0.15`
grabs `bb` from the cache and assigns 0.15 to it, BUT it is not longer a view onto the float block in `df` so nothing appears to get updated
I tried 2 fixes:
1) when getting an item from the `_item_cache` check if its a view of the underlying data - this works, but makes lookups way slow
2) clear the cache when `taking` ON THE ORIGINAL frame, so future lookups will cache-miss and get the correct data from the block manager
Fundamentally we COULD do this in the first statement `df['bb'].iloc[0] = 0.13`, where we don't reset the cache, except we don't have a reference to df (well we do, but by the time the operation is carried out we have an operation on the series, so have lost the frame reference)
So this is fixed in this case, but not sure what other numpy ops implicity convert view-> copy (and we are holding onto the cached view).
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} | 0 | "2013-06-21T01:00:15Z" | "2014-07-10T21:01:57Z" | "2013-06-21T01:22:34Z" | CONTRIBUTOR | null | BUG: python parser failing with `chunksize=1`
closes #3967
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https://api.github.com/repos/pandas-dev/pandas/issues/3979 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3979/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3979/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3979/events | https://github.com/pandas-dev/pandas/issues/3979 | 15,831,422 | MDU6SXNzdWUxNTgzMTQyMg== | 3,979 | Selections on an empty dataframe drop all columns | {
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``` python
In [1]: pd.DataFrame(columns=['a', 'b'])
Out[1]:
Empty DataFrame
Columns: [a, b]
Index: []
```
Right now, dropping a column works:
``` python
In [2]: df.drop('a', 1)
Out[2]:
Empty DataFrame
Columns: [b]
Index: []
```
But if I do a selection first, I'll be in trouble - selection returns an empty DF:
``` python
In [3]: df[df.b]
Out[3]:
Empty DataFrame
Columns: []
Index: []
```
If I've done the selection, everything breaks:
``` python
In [4]: df[df.b].drop('a')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-befe906574d9> in <module>()
----> 1 df.drop('a')
/Users/spencer/src/pandas/pandas/core/generic.pyc in drop(self, labels, axis, level)
365 new_axis = axis.drop(labels, level=level)
366 else:
--> 367 new_axis = axis.drop(labels)
368 dropped = self.reindex(**{axis_name: new_axis})
369 try:
/Users/spencer/src/pandas/pandas/core/index.pyc in drop(self, labels)
1239 mask = indexer == -1
1240 if mask.any():
-> 1241 raise ValueError('labels %s not contained in axis' % labels[mask])
1242 return self.delete(indexer)
1243
ValueError: labels ['b'] not contained in axis
```
Yeah, this actually comes up for me, particularly in scripts that have a series of steps like fetch data, stick it in a dataframe, munge it, and then do stuff. An example:
``` python
def fetch_data_from_external_api():
raw_data = api.request(...)
return pd.DataFrame(raw_data, columns=['timestamp', 'site_was_alive', 'val'])
def split_by_site_up(df):
down = df[~df.site_was_alive].drop('site_was_alive', 1)
up = df[df.site_was_alive].drop('site_was_alive', 1)
return down, up
def get_data_and_split():
df = fetch_data_from_external_api()
down, up = split_by_site_up(df)
down.val.plot()
```
Normally, this'll be fine. But what if the external api responds with an empty dataset for some reason? Or what if `df[~df.site_was_alive]` is empty, because the site was always alive? It seems weird that this breaks because selections that return empty dataframes lose all the column metadata. I have to write some ugly "if not empty" type code :(
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} | 20 | "2013-06-21T01:25:19Z" | "2014-01-07T13:43:02Z" | "2014-01-07T13:43:02Z" | CONTRIBUTOR | null | I guess quite a lot of people come from an R background, and perhaps a good material would be a conversion table for pandas vs R functions/idioms etc. in http://pandas.pydata.org/pandas-docs/dev/comparison_with_r.html
Perhaps this site could offer some functions to consider including:
http://www.statmethods.net/management/variables.html
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} | 4 | "2013-06-21T02:23:14Z" | "2013-12-04T01:18:11Z" | "2013-07-05T17:08:59Z" | CONTRIBUTOR | null | Under test_google, the test_multi\* methods are not checking to see result is successful.
Test get quote for yahoo also needs to check that result is None.
We should also consider mocking the network object to raise URLErrors to make sure that network methods all handle them appropriately (ie they should raise the URLError unless they explicitly specify otherwise.
I can do fixes to test cases, just want to remember because you don't see it unless no wifi
@cpcloud I'm guessing the really long request issue is because some of the test_google tests are overly ambitious and want to grab more data when probably smaller amount would work.
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} | 35 | "2013-06-21T20:15:34Z" | "2013-06-28T14:22:33Z" | "2013-06-28T14:22:33Z" | MEMBER | null | we really need to fix this...
https://travis-ci.org/cpcloud/pandas/jobs/8321921
https://travis-ci.org/cpcloud/pandas/jobs/8322345
https://travis-ci.org/jreback/pandas/jobs/8448538
https://travis-ci.org/pydata/pandas/jobs/8449118 (HTTP error here)
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} | 2 | "2013-06-21T20:15:53Z" | "2016-09-30T12:35:16Z" | "2016-09-29T22:32:24Z" | CONTRIBUTOR | null | This is dealing with a wrap-around on int64
occurs only on some platforms (macosx) and only on certain installs
pandas has to be aware to deal in certain looping constructs (see #3922)
```
np.iinfo(np.int64).max
9223372036854775807
2**63
-9223372036854775808
```
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} | 3 | "2013-06-21T20:54:58Z" | "2014-05-02T12:51:32Z" | "2014-05-02T12:51:32Z" | CONTRIBUTOR | null | At the moment, you can use `sortlevel` to select one level to sort by and then "Data will be lexicographically sorted by the chosen level followed by the other levels (in order)"
Should this be the case?
Should we be able to specify multiple levels to sort by?
see http://stackoverflow.com/questions/17242970/multi-index-sorting-in-pandas
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>>> idx = pd.Index([pd.to_datetime('2013-06-21')], name='idx')
>>> idx.name
'idx'
>>> idx.shift(1, freq='B').name == None
True
```
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} | 1 | "2013-06-22T11:56:21Z" | "2013-07-10T18:39:23Z" | "2013-07-10T18:39:23Z" | MEMBER | null | From [stackoverflow](http://stackoverflow.com/questions/16456751/matplotlib-axis-invert-xaxis-crashes-when-using-datetime), a question from more than a month ago, but still an issue I think (tested with master).
Basicly, when using `ax.invert_xaxis()` with datetime x-values, the following exception is raised:
```
RuntimeError: MillisecondLocator estimated to generate 5270400 ticks from 2012-08-01 00:00:00+00:00 to 2012-10-01 00:00:00+00:00:
exceeds Locator.MAXTICKS* 2 (2000)
```
---
At first it may not seem an error from pandas, as you can trigger it by using pure matplotlib code, but the error comes from pandas\tseries\converter.py. And more, it is by _importing pandas that causes working matplotlib code to work no longer_:
``` python
import numpy as np
import matplotlib.pyplot as plt
import datetime
x = np.array([datetime.datetime(2012, 8, 1, 0, 0),
datetime.datetime(2012, 9, 1, 0, 0),
datetime.datetime(2012, 10, 1, 0, 0)], dtype=object)
y = np.array([-1,0,1])
## Working matplotlib!
fig, ax = plt.subplots()
ax.plot(x,y)
ax.invert_xaxis()
plt.show()
import pandas as pd
## Same matplotlib not working anymore after importing pandas
fig, ax = plt.subplots()
ax.plot(x,y)
ax.invert_xaxis()
plt.show()
```
---
**CAUSE of the bug**:
When using `ax.invert_xaxis()` the left and right xlim are interchanged. Because of this the `delta` in PandasAutoDateLocator will be negative (https://github.com/pydata/pandas/blob/master/pandas/tseries/converter.py#L241).
This in turn triggers MilliSecondLocator instead of the normal AutoDateLocator, and causes the error as the datetime range is too big for MilliSecondLocator.
What I **don't understand** is why this is not happening when using pandas `plot` to create the axes object (the workaround I gave at [stackoverflow](http://stackoverflow.com/questions/16456751/matplotlib-axis-invert-xaxis-crashes-when-using-datetime/16460828#16460828)):
``` python
df = pd.DataFrame(y, index=x, columns=['test'])
## This works as expected!
fig, ax = plt.subplots()
df.plot(ax=ax)
ax.invert_xaxis()
plt.show()
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3991 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3991/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3991/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3991/events | https://github.com/pandas-dev/pandas/pull/3991 | 15,883,810 | MDExOlB1bGxSZXF1ZXN0NjQ4NjcyOA== | 3,991 | BUG: invert_xaxis (negative tot_sec) triggers MilliSecondLocator (#3990) | {
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} | 11 | "2013-06-22T12:13:47Z" | "2014-06-25T21:40:21Z" | "2013-07-10T18:39:00Z" | MEMBER | null | Solves issue #3990.
Negative timedelta for xaxis range will no longer trigger MilliSecondLocator.
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https://api.github.com/repos/pandas-dev/pandas/issues/3992 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3992/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3992/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3992/events | https://github.com/pandas-dev/pandas/issues/3992 | 15,886,165 | MDU6SXNzdWUxNTg4NjE2NQ== | 3,992 | BUG: why in scipy.integrate.trapz(y,x) using x,y from pd.read_csv does not give the right answer | {
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} | [] | closed | false | null | [] | null | 2 | "2013-06-22T16:14:34Z" | "2013-06-23T11:12:25Z" | "2013-06-23T11:12:25Z" | NONE | null | Please, refer to this question in StackOverflow for more details:
http://stackoverflow.com/q/17252024/832621
The OP reads a csv using:
```
data_df = pd.read_csv("example.csv", sep="\t", index_col=0, names=["test"])
```
And when doing integration using the "Index" object at data_df.index it does not work, but works with data_df.index.values,,,,
Is it a bug?
Thank you!
Saullo
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https://api.github.com/repos/pandas-dev/pandas/issues/3993 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3993/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3993/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3993/events | https://github.com/pandas-dev/pandas/issues/3993 | 15,887,148 | MDU6SXNzdWUxNTg4NzE0OA== | 3,993 | doc build WARNINGs with HEAD | {
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} | [] | closed | false | null | [] | null | 1 | "2013-06-22T17:12:48Z" | "2013-06-22T18:58:02Z" | "2013-06-22T18:58:02Z" | CONTRIBUTOR | null | Here is the log shown:
```
python make.py
...
checking consistency... /tmp/pandas/doc/source/generated/pandas.DataFrame.load.rst:: WARNING: document isn't included in any toctree
/tmp/pandas/doc/source/generated/pandas.DataFrame.save.rst:: WARNING: document isn't included in any toctree
/tmp/pandas/doc/source/generated/pandas.Panel.load.rst:: WARNING: document isn't included in any toctree
/tmp/pandas/doc/source/generated/pandas.Panel.save.rst:: WARNING: document isn't included in any toctree
/tmp/pandas/doc/source/generated/pandas.Series.load.rst:: WARNING: document isn't included in any toctree
/tmp/pandas/doc/source/generated/pandas.Series.save.rst:: WARNING: document isn't included in any toctree
/tmp/pandas/doc/source/generated/pandas.core.common.load.rst:: WARNING: document isn't included in any toctree
/tmp/pandas/doc/source/generated/pandas.core.common.save.rst:: WARNING: document isn't included in any toctree
done
preparing documents... done
...
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3996 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3996/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3996/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3996/events | https://github.com/pandas-dev/pandas/issues/3996 | 15,887,717 | MDU6SXNzdWUxNTg4NzcxNw== | 3,996 | Problem with Series.clip with Timestamp threshold | {
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} | [] | closed | false | null | [] | null | 1 | "2013-06-22T17:55:42Z" | "2013-06-25T20:04:24Z" | "2013-06-25T20:04:24Z" | CONTRIBUTOR | null | ```
>>> import pandas as pd
>>> s = pd.Series(pd.date_range('2013-05-01', '2013-05-03'))
>>> s
0 2013-05-01 00:00:00
1 2013-05-02 00:00:00
2 2013-05-03 00:00:00
dtype: datetime64[ns]
>>> s.clip_lower(s[1])
0 2013-05-02 00:00:00
1 1367452800000000000
2 1367539200000000000
dtype: object
>>> s.clip_lower(s[1].value)
0 2013-05-02 00:00:00
1 2013-05-02 00:00:00
2 2013-05-03 00:00:00
dtype: datetime64[ns]
>>> print pd.__version__
0.11.1.dev-f1a3226
```
Any reason not to implement clip_lower and clip_upper with Series.where rather than np.where? It solves this problem.
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https://api.github.com/repos/pandas-dev/pandas/issues/3997 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3997/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3997/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3997/events | https://github.com/pandas-dev/pandas/issues/3997 | 15,887,904 | MDU6SXNzdWUxNTg4NzkwNA== | 3,997 | Dropna=False is ignored for MultiIndex stack. | {
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} | [] | closed | false | null | [] | null | 0 | "2013-06-22T18:11:34Z" | "2013-06-23T09:20:03Z" | "2013-06-23T09:20:03Z" | CONTRIBUTOR | null | ```
>>> df = pd.DataFrame({'A': ['a1', 'a2'], 'B': ['b1', 'b2'], 'C': [1, 1]})
>>> df
A B C
0 a1 b1 1
1 a2 b2 1
# Unstack introduces some NaNs
>>> df.set_index(['A', 'B']).unstack()
C
B b1 b2
A
a1 1 NaN
a2 NaN 1
# Stack with dropna=False should keep the NaNs, but doesn't.
>>> df.set_index(['A', 'B']).unstack().stack(dropna=False)
C
A B
a1 b1 1
a2 b2 1
# This is what I expected to get
>>> df.set_index(['A', 'B']).unstack().stack(dropna=False)
C
A B
a1 b1 1
b2 NaN
a2 b1 NaN
b2 1
```
The problem seems to be with reshape.py, line 495 with forces dropna=True.
```
if isinstance(frame.columns, MultiIndex):
return _stack_multi_columns(frame, level=level, dropna=True)
```
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} | 19 | "2013-06-22T20:25:59Z" | "2014-07-16T08:15:26Z" | "2013-06-25T20:04:24Z" | CONTRIBUTOR | null | Closes #3996
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